R version 4.4.0 RC (2024-04-16 r86451 ucrt) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library( "censReg" ) Loading required package: maxLik Loading required package: miscTools Please cite the 'maxLik' package as: Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1. If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site: https://r-forge.r-project.org/projects/maxlik/ Please cite the 'censReg' package as: Henningsen, Arne (2017). censReg: Censored Regression (Tobit) Models. R package version 0.5. http://CRAN.R-Project.org/package=censReg. If you have questions, suggestions, or comments regarding the 'censReg' package, please use a forum or 'tracker' at the R-Forge site of the 'sampleSelection' project: https://r-forge.r-project.org/projects/sampleselection/ > library( "lmtest" ) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > library( "sandwich" ) > > options( digits = 5 ) > > printAll <- function( x, logSigmaFalse = FALSE, sDigits = 2, + meCalcVCov = TRUE, meReturnJacobian = FALSE, + sumMeCalcVCov = TRUE, sumMeReturnJacobian = FALSE ) { + for( n in names( x ) ) { + if( ! n %in% c( "code", "message", "iterations" ) ) { + cat( "$", n, "\n", sep = "" ) + if( n %in% c( "hessian" ) ) { + print( round( x[[ n ]], 1 ) ) + } else if( n %in% c( "estimate", "gradientObs" ) ) { + print( round( x[[ n ]], 2 ) ) + } else if( n %in% c( "gradient" ) ) { + print( round( x[[ n ]], 3 ) ) + } else { + print( x[[ n ]] ) + } + cat( "\n" ) + } + } + cat( "class\n" ) + print( class( x ) ) + + cat( "print( x, digits = 2 )\n" ) + print( x, digits = 2 ) + + if( logSigmaFalse ) { + cat( "print( x, logSigma = FALSE, digits = 2 )\n" ) + print( x, logSigma = FALSE, digits = 2 ) + } + + cat( "print( round( margEff( x ), digits = 2 ) )\n" ) + print( round( margEff( x, calcVCov = meCalcVCov, + returnJacobian = meReturnJacobian ), 2 ) ) + + cat( "printME( margEff( x ) )\n" ) + printME( margEff( x, calcVCov = meCalcVCov, + returnJacobian = meReturnJacobian ) ) + + cat( "print( summary( margEff( x ) ), digits = sDigits )\n" ) + print( summary( margEff( x, calcVCov = sumMeCalcVCov, + returnJacobian = sumMeReturnJacobian ) ), digits = sDigits ) + + x$code <- 0 + x$message <- "removed message" + x$iterations <- 0 + + cat( "print( maxLik:::summary.maxLik( x ), sDigits )\n" ) + print( maxLik:::summary.maxLik( x ), digits = sDigits ) + + cat( "print( summary( x ), digits = sDigits )\n" ) + print( summary( x ), digits = sDigits ) + + if( logSigmaFalse ) { + cat( "print( summary( x ), logSigma = FALSE, digits = sDigits )\n" ) + print( summary( x ), logSigma = FALSE, digits = sDigits ) + } + } > > printME <- function( x ) { + print( round( x, 3 ) ) + y <- attributes( x ) + for( n in names( y ) ) { + if( ! n %in% c( "names" ) ) { + cat( "attr(,\"", n, "\")\n", sep = "" ) + if( n %in% c( "vcov" ) ) { + print( round( y[[ n ]], 3 ) ) + } else if( n %in% c( "jacobian" ) ) { + print( round( y[[ n ]], 3 ) ) + } else { + print( y[[ n ]] ) + } + } + } + } > > data( "Affairs", package = "AER" ) > affairsFormula <- affairs ~ age + yearsmarried + religiousness + + occupation + rating > > ## usual tobit estimation > estResult <- censReg( affairsFormula, data = Affairs ) > printAll( estResult, logSigmaFalse = TRUE, sDigits = 1 ) $maximum [1] -705.58 $estimate (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 $gradient (Intercept) age yearsmarried religiousness occupation 0 0 0 0 0 rating logSigma 0 0 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.0 -165.5 -44.9 -14.8 -21.3 -18.5 age -165.5 -5890.2 -1675.4 -500.7 -717.1 -602.6 yearsmarried -44.9 -1675.4 -550.7 -140.7 -193.4 -159.0 religiousness -14.8 -500.7 -140.7 -50.8 -62.7 -54.8 occupation -21.3 -717.1 -193.4 -62.7 -106.7 -78.9 rating -18.5 -602.6 -159.0 -54.8 -78.9 -74.9 logSigma -37.1 -1206.3 -301.5 -116.1 -155.3 -148.5 logSigma (Intercept) -37.1 age -1206.3 yearsmarried -301.5 religiousness -116.1 occupation -155.3 rating -148.5 logSigma -530.5 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "Newton-Raphson maximisation" $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.06 -2.09 -0.56 -0.17 -0.40 -0.23 -0.27 [2,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [3,] -0.10 -3.17 -1.49 -0.10 -0.10 -0.40 0.02 [4,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [5,] -0.07 -1.44 -0.05 -0.13 -0.39 -0.20 -0.24 [6,] -0.03 -0.85 -0.04 -0.05 -0.13 -0.13 -0.26 [7,] -0.05 -1.18 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.06 -3.18 -0.84 -0.11 -0.22 -0.22 -0.28 [9,] -0.09 -3.02 -1.42 -0.38 -0.09 -0.19 -0.02 [10,] -0.02 -0.41 -0.03 -0.08 -0.08 -0.09 -0.22 [11,] -0.14 -5.15 -2.09 -0.28 -0.97 -0.28 0.58 [12,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [13,] -0.04 -1.82 -0.58 -0.19 -0.23 -0.16 -0.29 [14,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [15,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [16,] -0.08 -3.03 -1.23 -0.08 -0.41 -0.41 -0.13 [17,] -0.11 -3.91 -1.58 -0.21 -0.42 -0.32 0.10 [18,] -0.04 -0.81 -0.03 -0.11 -0.18 -0.15 -0.29 [19,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.05 -1.38 -0.51 -0.10 -0.05 -0.26 -0.29 [21,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [22,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [23,] -0.05 -1.42 -0.53 -0.21 -0.26 -0.21 -0.28 [24,] -0.04 -1.13 -0.35 -0.11 -0.04 -0.18 -0.29 [25,] -0.04 -1.60 -0.17 -0.09 -0.26 -0.17 -0.29 [26,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [27,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [28,] -0.04 -1.86 -0.66 -0.22 -0.27 -0.18 -0.29 [29,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [30,] -0.04 -1.13 -0.17 -0.13 -0.21 -0.17 -0.29 [31,] -0.07 -3.07 -1.10 -0.29 -0.44 -0.22 -0.20 [32,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [33,] -0.02 -0.66 -0.01 -0.10 -0.15 -0.10 -0.25 [34,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [35,] -0.06 -1.95 -0.24 -0.06 -0.37 -0.24 -0.26 [36,] -0.04 -0.94 -0.06 -0.17 -0.21 -0.13 -0.29 [37,] -0.06 -2.35 -0.84 -0.17 -0.06 -0.22 -0.27 [38,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [39,] -0.04 -0.92 -0.06 -0.04 -0.12 -0.21 -0.29 [40,] -0.02 -0.44 -0.01 -0.06 -0.02 -0.10 -0.23 [41,] -0.03 -0.85 -0.26 -0.13 -0.16 -0.13 -0.26 [42,] -0.05 -2.46 -0.71 -0.24 -0.28 -0.14 -0.29 [43,] -0.02 -0.34 -0.01 -0.08 -0.02 -0.06 -0.20 [44,] -0.11 -3.10 -0.46 -0.23 -0.69 -0.11 0.21 [45,] -0.04 -1.28 -0.28 -0.20 -0.20 -0.12 -0.29 [46,] -0.03 -0.75 -0.14 -0.10 -0.17 -0.17 -0.29 [47,] -0.03 -0.83 -0.22 -0.12 -0.19 -0.15 -0.28 [48,] -0.08 -3.34 -1.19 -0.16 -0.40 -0.32 -0.15 [49,] -0.01 -0.40 -0.02 -0.06 -0.04 -0.07 -0.19 [50,] -0.08 -3.46 -1.23 -0.16 -0.49 -0.33 -0.13 [51,] -0.01 -0.25 -0.01 -0.06 -0.03 -0.06 -0.16 [52,] -0.06 -1.95 -0.43 -0.12 -0.37 -0.24 -0.26 [53,] -0.02 -0.46 -0.07 -0.08 -0.10 -0.08 -0.21 [54,] -0.05 -1.48 -0.55 -0.22 -0.33 -0.22 -0.28 [55,] -0.06 -1.21 -0.22 -0.06 -0.28 -0.28 -0.28 [56,] -0.11 -4.23 -1.71 -0.46 -0.34 -0.11 0.21 [57,] -0.02 -0.46 -0.03 -0.11 -0.08 -0.08 -0.24 [58,] -0.04 -1.34 -0.54 -0.15 -0.04 -0.18 -0.29 [59,] -0.02 -0.65 -0.02 -0.10 -0.12 -0.10 -0.25 [60,] -0.05 -1.56 -0.49 -0.20 -0.29 -0.20 -0.29 [61,] -0.07 -3.44 -1.10 -0.37 -0.51 -0.15 -0.20 [62,] -0.05 -2.00 -0.54 -0.16 -0.32 -0.22 -0.28 [63,] -0.03 -0.73 -0.03 -0.07 -0.17 -0.17 -0.28 [64,] -0.04 -0.99 -0.15 -0.07 -0.15 -0.18 -0.29 [65,] -0.04 -1.23 -0.27 -0.15 -0.23 -0.15 -0.29 [66,] -0.06 -2.36 -0.84 -0.11 -0.17 -0.28 -0.27 [67,] -0.04 -1.59 -0.43 -0.17 -0.26 -0.17 -0.29 [68,] -0.05 -2.13 -0.68 -0.14 -0.27 -0.23 -0.29 [69,] -0.01 -0.31 -0.02 -0.07 -0.07 -0.07 -0.19 [70,] -0.05 -1.24 -0.07 -0.09 -0.28 -0.18 -0.29 [71,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [72,] -0.02 -0.76 -0.24 -0.12 -0.09 -0.12 -0.25 [73,] -0.03 -0.69 0.00 -0.06 -0.16 -0.16 -0.28 [74,] -0.06 -2.86 -0.91 -0.24 -0.24 -0.18 -0.26 [75,] -0.09 -2.88 -1.35 -0.09 -0.45 -0.45 -0.07 [76,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.27 [77,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [78,] -0.03 -0.84 -0.12 -0.09 -0.19 -0.16 -0.28 [79,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [80,] -0.10 -5.84 -1.54 -0.20 -0.72 -0.20 0.06 [81,] -0.03 -0.57 -0.05 -0.10 -0.20 -0.16 -0.28 [82,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [83,] -0.04 -0.94 -0.03 -0.09 -0.13 -0.17 -0.29 [84,] -0.03 -1.14 -0.11 -0.11 -0.08 -0.08 -0.27 [85,] -0.02 -0.34 -0.02 -0.06 -0.02 -0.08 -0.20 [86,] -0.06 -1.31 -0.02 -0.06 -0.36 -0.24 -0.26 [87,] -0.05 -1.59 -0.75 -0.20 -0.25 -0.25 -0.29 [88,] -0.06 -1.73 -0.10 -0.19 -0.32 -0.13 -0.25 [89,] -0.02 -0.47 -0.03 -0.06 -0.02 -0.11 -0.24 [90,] -0.06 -2.31 -0.94 -0.19 -0.06 -0.25 -0.25 [91,] -0.06 -1.97 -0.92 -0.25 -0.18 -0.25 -0.26 [92,] -0.08 -3.07 -0.83 -0.17 -0.42 -0.25 -0.13 [93,] -0.04 -1.52 -0.41 -0.16 -0.21 -0.16 -0.29 [94,] -0.04 -2.26 -0.60 -0.20 -0.20 -0.12 -0.29 [95,] -0.05 -1.26 -0.02 -0.05 -0.14 -0.19 -0.29 [96,] -0.02 -0.99 -0.35 -0.12 -0.02 -0.12 -0.25 [97,] -0.11 -5.99 -1.58 -0.32 -0.63 -0.11 0.09 [98,] -0.08 -2.98 -0.81 -0.08 -0.48 -0.32 -0.14 [99,] -0.06 -2.05 -0.83 -0.17 -0.28 -0.28 -0.28 [100,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [101,] -0.02 -0.63 -0.23 -0.12 -0.02 -0.12 -0.25 [102,] -0.07 -2.47 -0.67 -0.13 -0.40 -0.27 -0.23 [103,] -0.02 -0.36 0.00 -0.06 -0.06 -0.08 -0.20 [104,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [105,] -0.06 -2.30 -0.93 -0.25 -0.37 -0.25 -0.25 [106,] -0.04 -0.86 -0.16 -0.16 -0.24 -0.16 -0.29 [107,] -0.04 -1.13 -0.29 -0.17 -0.21 -0.17 -0.29 [108,] -0.04 -2.07 -0.54 -0.15 -0.18 -0.15 -0.29 [109,] -0.10 -3.34 -1.56 -0.31 -0.63 -0.31 0.08 [110,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [111,] -0.02 -0.63 -0.14 -0.08 -0.02 -0.10 -0.23 [112,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [113,] -0.01 -0.31 -0.01 -0.05 -0.05 -0.05 -0.14 [114,] -0.03 -1.28 -0.30 -0.15 -0.21 -0.12 -0.28 [115,] -0.05 -1.43 -0.37 -0.16 -0.26 -0.21 -0.28 [116,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [117,] -0.09 -3.41 -1.38 -0.37 -0.28 -0.18 -0.04 [118,] -0.04 -1.23 -0.38 -0.19 -0.23 -0.15 -0.29 [119,] -0.01 -0.31 -0.01 -0.06 -0.01 -0.07 -0.19 [120,] -0.04 -0.98 -0.25 -0.14 -0.07 -0.14 -0.29 [121,] -0.04 -1.15 -0.30 -0.08 -0.08 -0.21 -0.29 [122,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [123,] -0.07 -2.96 -1.06 -0.28 -0.35 -0.21 -0.21 [124,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [125,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [126,] -0.04 -1.33 -0.54 -0.18 -0.22 -0.18 -0.29 [127,] -0.03 -0.66 0.00 -0.06 -0.12 -0.15 -0.28 [128,] -0.02 -0.45 -0.03 -0.07 -0.08 -0.08 -0.21 [129,] -0.03 -0.90 -0.04 -0.06 -0.17 -0.14 -0.27 [130,] -0.03 -0.88 -0.05 -0.07 -0.20 -0.16 -0.28 [131,] -0.06 -1.61 -0.60 -0.24 -0.06 -0.18 -0.26 [132,] -0.04 -1.70 -0.61 -0.16 -0.24 -0.20 -0.29 [133,] -0.03 -0.88 -0.05 -0.07 -0.20 -0.16 -0.28 [134,] -0.07 -1.97 -0.29 -0.15 -0.44 -0.22 -0.20 [135,] -0.08 -2.44 -0.76 -0.23 -0.38 -0.23 -0.18 [136,] -0.08 -2.57 -1.21 -0.24 -0.40 -0.32 -0.15 [137,] -0.04 -0.77 -0.03 -0.07 -0.21 -0.18 -0.29 [138,] -0.08 -2.81 -1.14 -0.15 -0.08 -0.30 -0.18 [139,] -0.02 -0.59 -0.09 -0.09 -0.11 -0.11 -0.24 [140,] -0.07 -1.77 -0.26 -0.07 -0.33 -0.26 -0.24 [141,] -0.07 -1.84 -0.68 -0.14 -0.07 -0.27 -0.23 [142,] -0.06 -1.80 -0.84 -0.28 -0.34 -0.23 -0.27 [143,] -0.05 -1.23 -0.32 -0.23 -0.23 -0.14 -0.29 [144,] -0.06 -3.37 -0.97 -0.13 -0.32 -0.26 -0.24 [145,] -0.06 -1.61 -0.24 -0.18 -0.36 -0.18 -0.26 [146,] -0.05 -1.93 -0.21 -0.05 -0.26 -0.21 -0.28 [147,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [148,] -0.04 -1.94 -0.56 -0.19 -0.04 -0.11 -0.29 [149,] -0.04 -2.17 -0.57 -0.15 -0.23 -0.15 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-0.07 -0.22 [354,] -0.02 -0.46 -0.07 -0.07 -0.02 -0.09 -0.21 [355,] -0.02 -0.46 -0.07 -0.07 -0.02 -0.09 -0.21 [356,] -0.05 -2.56 -0.82 -0.11 -0.27 -0.27 -0.28 [357,] -0.10 -3.24 -1.52 -0.30 -0.51 -0.30 0.05 [358,] -0.03 -1.38 -0.23 -0.07 -0.16 -0.16 -0.28 [359,] -0.03 -0.65 -0.02 -0.12 -0.18 -0.12 -0.27 [360,] -0.02 -0.58 0.00 -0.06 -0.13 -0.11 -0.24 [361,] -0.04 -1.44 -0.45 -0.13 -0.27 -0.22 -0.29 [362,] -0.01 -0.26 0.00 -0.06 -0.05 -0.06 -0.17 [363,] -0.03 -1.41 -0.45 -0.15 -0.03 -0.12 -0.28 [364,] -0.04 -1.13 -0.35 -0.11 -0.04 -0.18 -0.29 [365,] -0.02 -1.42 -0.37 -0.10 -0.12 -0.12 -0.26 [366,] -0.03 -0.84 -0.12 -0.09 -0.19 -0.16 -0.28 [367,] -0.02 -0.63 -0.14 -0.08 -0.02 -0.10 -0.23 [368,] -0.02 -0.84 -0.23 -0.09 -0.02 -0.11 -0.25 [369,] -0.07 -2.38 -0.74 -0.07 -0.07 -0.30 -0.19 [370,] -0.04 -0.87 -0.16 -0.12 -0.04 -0.16 -0.29 [371,] -0.07 -1.88 -0.49 -0.28 -0.21 -0.14 -0.22 [372,] -0.05 -3.11 -0.82 -0.27 -0.27 -0.11 -0.28 [373,] -0.04 -1.38 -0.30 -0.09 -0.21 -0.21 -0.29 [374,] -0.03 -0.82 -0.05 -0.12 -0.03 -0.09 -0.28 [375,] -0.02 -0.44 -0.03 -0.08 -0.10 -0.10 -0.23 [376,] -0.03 -0.66 -0.04 -0.12 -0.15 -0.12 -0.28 [377,] -0.03 -0.86 -0.19 -0.08 -0.03 -0.13 -0.26 [378,] -0.06 -2.77 -0.88 -0.18 -0.29 -0.24 -0.27 [379,] -0.02 -0.44 -0.01 -0.06 -0.02 -0.10 -0.23 [380,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [381,] -0.05 -1.32 -0.20 -0.05 -0.24 -0.24 -0.29 [382,] -0.03 -1.51 -0.44 -0.12 -0.15 -0.15 -0.27 [383,] -0.03 -1.12 -0.35 -0.14 -0.21 -0.17 -0.29 [384,] -0.05 -2.32 -0.74 -0.20 -0.30 -0.20 -0.29 [385,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [386,] -0.02 -0.41 -0.03 -0.08 -0.08 -0.09 -0.22 [387,] -0.07 -2.28 -0.71 -0.14 -0.36 -0.29 -0.21 [388,] -0.05 -1.03 -0.04 -0.09 -0.23 -0.19 -0.29 [389,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [390,] -0.07 -2.87 -1.02 -0.20 -0.41 -0.27 -0.22 [391,] -0.02 -0.55 -0.14 -0.10 -0.08 -0.10 -0.23 [392,] -0.05 -2.14 -0.76 -0.20 -0.20 -0.20 -0.29 [393,] -0.08 -4.62 -1.22 -0.24 -0.41 -0.16 -0.14 [394,] -0.11 -4.58 -1.64 -0.33 -0.65 -0.22 0.14 [395,] -0.08 -2.72 -0.59 -0.17 -0.08 -0.17 -0.11 [396,] -0.02 -0.39 -0.07 -0.09 -0.07 -0.09 -0.21 [397,] -0.05 -1.05 -0.07 -0.05 -0.29 -0.24 -0.29 [398,] -0.04 -0.90 -0.03 -0.04 -0.16 -0.21 -0.29 [399,] -0.04 -1.33 -0.62 -0.17 -0.04 -0.21 -0.29 [400,] -0.07 -1.46 -0.10 -0.13 -0.33 -0.20 -0.23 [401,] -0.01 -0.35 -0.05 -0.06 -0.03 -0.06 -0.18 [402,] -0.02 -0.46 -0.07 -0.07 -0.02 -0.09 -0.21 [403,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [404,] -0.06 -1.84 -0.09 -0.12 -0.40 -0.17 -0.27 [405,] -0.08 -4.60 -1.21 -0.32 -0.24 -0.08 -0.14 [406,] -0.02 -0.79 -0.15 -0.09 -0.11 -0.11 -0.24 [407,] -0.06 -3.37 -0.97 -0.13 -0.32 -0.26 -0.24 [408,] -0.04 -1.66 -0.53 -0.14 -0.21 -0.18 -0.29 [409,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [410,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.27 [411,] -0.07 -1.59 -0.29 -0.14 -0.22 -0.22 -0.20 [412,] -0.04 -1.46 -0.28 -0.08 -0.24 -0.20 -0.29 [413,] -0.05 -1.48 -0.38 -0.22 -0.22 -0.16 -0.28 [414,] -0.04 -1.58 -0.38 -0.15 -0.23 -0.15 -0.29 [415,] -0.02 -0.47 -0.03 -0.06 -0.02 -0.11 -0.24 [416,] -0.07 -1.47 -0.27 -0.13 -0.07 -0.20 -0.23 [417,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [418,] -0.08 -2.78 -1.13 -0.30 -0.30 -0.23 -0.18 [419,] -0.04 -1.03 -0.27 -0.11 -0.19 -0.19 -0.29 [420,] -0.05 -0.81 -0.47 -0.19 -0.19 -0.23 -0.29 [421,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [422,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [423,] -0.16 -5.81 -2.36 -0.31 -0.79 -0.16 0.91 [424,] -0.02 -0.38 -0.03 -0.09 -0.02 -0.07 -0.21 [425,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [426,] -0.02 -0.59 -0.09 -0.09 -0.11 -0.11 -0.24 [427,] -0.04 -0.82 0.00 -0.04 -0.11 -0.19 -0.29 [428,] -0.03 -0.93 -0.24 -0.14 -0.03 -0.14 -0.29 [429,] -0.07 -2.28 -1.07 -0.36 -0.36 -0.21 -0.21 [430,] -0.05 -1.49 -0.47 -0.19 -0.23 -0.19 -0.29 [431,] -0.09 -2.86 -1.34 -0.18 -0.27 -0.36 -0.07 [432,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [433,] -0.03 -0.83 -0.12 -0.12 -0.12 -0.12 -0.28 [434,] -0.02 -0.88 -0.25 -0.08 -0.02 -0.08 -0.21 [435,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [436,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [437,] -0.07 -2.88 -1.03 -0.14 -0.07 -0.27 -0.22 [438,] -0.05 -2.23 -0.80 -0.21 -0.27 -0.21 -0.28 [439,] -0.05 -1.42 -0.37 -0.21 -0.16 -0.16 -0.28 [440,] -0.11 -2.93 -0.76 -0.22 -0.65 -0.22 0.13 [441,] -0.08 -3.32 -1.18 -0.24 -0.24 -0.24 -0.16 [442,] -0.03 -0.71 -0.11 -0.08 -0.08 -0.13 -0.26 [443,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [444,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [445,] -0.03 -0.70 -0.10 -0.10 -0.03 -0.10 -0.26 [446,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [447,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [448,] -0.05 -2.22 -0.71 -0.19 -0.24 -0.19 -0.29 [449,] -0.11 -3.97 -1.07 -0.21 -0.64 -0.21 0.12 [450,] -0.07 -2.69 -1.09 -0.22 -0.36 -0.29 -0.20 [451,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [452,] 0.17 4.66 0.26 0.52 0.69 0.69 1.02 [453,] 0.20 5.40 0.80 0.60 0.20 1.00 1.72 [454,] 0.12 4.46 1.81 0.60 0.72 0.24 -0.01 [455,] 0.18 5.66 1.77 0.53 0.88 0.35 1.12 [456,] 0.19 4.28 0.02 0.78 0.97 0.97 1.58 [457,] 0.15 3.37 0.23 0.31 0.15 0.77 0.59 [458,] 0.17 6.44 2.61 0.70 0.87 0.35 1.06 [459,] 0.20 4.36 0.30 0.40 0.59 0.79 1.67 [460,] 0.04 1.47 0.60 0.08 0.24 0.16 -0.89 [461,] 0.04 1.22 0.57 0.15 0.11 0.08 -0.90 [462,] 0.02 0.63 0.26 0.07 0.07 0.03 -0.98 [463,] 0.18 7.36 2.63 0.53 0.18 0.70 1.09 [464,] 0.18 7.69 2.75 0.92 0.73 0.18 1.28 [465,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [466,] 0.16 4.97 2.33 0.47 0.16 0.31 0.64 [467,] 0.13 3.42 0.51 0.13 0.76 0.63 0.09 [468,] 0.12 4.28 1.16 0.23 0.81 0.35 -0.09 [469,] 0.24 6.47 0.96 0.72 1.20 1.20 2.91 [470,] 0.13 5.30 1.89 0.50 0.63 0.63 0.08 [471,] 0.17 7.94 2.53 0.84 0.68 0.84 0.94 [472,] 0.21 5.57 0.82 0.62 1.03 0.82 1.89 [473,] 0.16 4.38 1.14 0.81 0.16 0.65 0.79 [474,] 0.30 8.10 0.45 0.90 1.50 1.20 5.12 [475,] 0.21 5.62 1.46 0.83 1.25 0.42 1.94 [476,] 0.12 5.13 1.83 0.49 0.61 0.49 0.01 [477,] 0.14 3.75 1.39 0.56 0.97 0.42 0.31 [478,] 0.05 1.25 0.07 0.09 0.23 0.09 -0.85 [479,] 0.15 4.83 0.60 0.60 0.91 0.60 0.55 [480,] 0.08 2.13 0.55 0.24 0.08 0.24 -0.58 [481,] 0.16 4.98 1.56 0.62 0.16 0.62 0.65 [482,] 0.05 1.24 0.18 0.09 0.32 0.09 -0.86 [483,] 0.21 3.63 0.16 1.04 0.83 1.04 1.92 [484,] 0.16 5.11 1.60 0.64 0.16 0.80 0.74 [485,] 0.17 5.29 1.16 0.33 0.99 0.66 0.86 [486,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [487,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [488,] 0.25 8.09 2.53 0.51 1.26 1.26 3.34 [489,] 0.15 7.95 2.29 0.31 0.92 0.61 0.59 [490,] 0.09 3.87 1.38 0.09 0.09 0.28 -0.42 [491,] 0.03 1.61 0.47 0.06 0.19 0.09 -0.93 [492,] 0.10 3.63 1.47 0.29 0.59 0.49 -0.34 [493,] 0.28 6.07 1.10 0.83 0.83 1.10 4.18 [494,] 0.13 3.61 0.94 0.13 0.80 0.27 0.21 [495,] 0.15 4.09 0.61 0.45 0.76 0.76 0.56 [496,] 0.30 13.93 4.45 1.19 1.78 1.48 4.97 [497,] 0.17 7.26 2.59 0.69 0.17 0.17 1.03 [498,] 0.22 5.82 0.86 0.65 0.65 0.86 2.16 [499,] 0.26 8.26 1.81 1.03 1.03 1.29 3.53 [500,] 0.17 5.44 0.07 0.51 0.51 0.68 0.96 [501,] 0.16 7.52 2.40 0.80 0.80 0.64 0.74 [502,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [503,] 0.16 3.59 0.65 0.33 0.98 0.65 0.81 [504,] 0.13 3.55 0.53 0.26 0.53 0.66 0.18 [505,] 0.22 11.31 3.26 1.09 0.22 0.65 2.22 [506,] 0.09 2.54 0.38 0.28 0.28 0.28 -0.40 [507,] 0.11 3.05 1.13 0.45 0.11 0.45 -0.13 [508,] 0.10 3.11 0.68 0.29 0.68 0.39 -0.36 [509,] 0.07 2.37 0.52 0.15 0.30 0.07 -0.63 [510,] 0.05 1.04 0.07 0.05 0.14 0.09 -0.85 [511,] 0.19 4.14 0.75 0.56 1.13 0.75 1.41 [512,] 0.18 7.39 2.64 0.70 1.06 0.70 1.11 [513,] 0.07 4.13 1.09 0.07 0.36 0.29 -0.64 [514,] 0.15 4.87 0.61 0.46 0.76 0.30 0.57 [515,] 0.07 1.97 0.29 0.07 0.29 0.29 -0.64 [516,] 0.24 7.64 1.67 0.96 0.24 0.96 2.88 [517,] 0.08 4.41 1.16 0.08 0.31 0.31 -0.59 [518,] 0.11 4.77 1.70 0.45 0.57 0.23 -0.12 [519,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [520,] -0.01 -0.35 -0.13 -0.03 -0.05 -0.01 -1.00 [521,] 0.10 5.15 1.48 0.30 0.40 0.40 -0.33 [522,] 0.07 2.01 0.52 0.22 0.37 0.22 -0.62 [523,] 0.18 5.80 1.27 0.36 0.72 0.36 1.23 [524,] 0.18 3.90 0.71 0.71 0.35 0.89 1.14 [525,] 0.12 3.19 0.83 0.35 0.71 0.47 -0.05 [526,] 0.20 7.41 3.01 0.20 1.00 1.00 1.73 [527,] 0.12 3.69 1.73 0.35 0.12 0.35 -0.10 [528,] 0.19 5.14 1.33 0.38 0.95 0.95 1.47 [529,] 0.07 2.34 0.51 0.22 0.37 0.22 -0.64 [530,] 0.14 4.51 0.21 0.28 0.28 0.56 0.35 [531,] 0.21 8.67 3.10 0.83 0.21 0.41 1.90 [532,] 0.17 5.45 1.70 0.51 0.85 0.68 0.98 [533,] 0.14 5.35 0.58 0.14 0.87 0.43 0.42 [534,] 0.06 1.61 0.24 0.12 0.30 0.18 -0.76 [535,] 0.20 8.43 3.01 0.60 0.80 0.60 1.74 [536,] 0.15 3.98 1.47 0.74 0.88 0.74 0.48 [537,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [538,] 0.18 4.90 1.27 0.18 0.54 0.54 1.24 [539,] 0.10 2.69 0.70 0.40 0.10 0.20 -0.32 [540,] 0.09 2.93 0.92 0.18 0.37 0.37 -0.43 [541,] 0.24 4.23 0.18 0.48 0.24 0.73 2.98 [542,] 0.20 6.50 3.05 0.61 1.02 0.81 1.81 [543,] 0.09 2.00 0.64 0.36 0.36 0.27 -0.44 [544,] 0.16 5.13 1.12 0.64 0.96 0.80 0.75 [545,] 0.11 2.95 0.44 0.22 0.66 0.22 -0.19 [546,] 0.14 3.10 0.21 0.71 0.71 0.42 0.35 [547,] 0.10 3.28 1.54 0.31 0.51 0.10 -0.29 [548,] 0.16 6.58 2.35 0.31 0.16 0.31 0.67 [549,] 0.16 6.55 2.34 0.47 0.78 0.62 0.66 [550,] 0.16 5.02 1.57 0.31 0.63 0.31 0.67 [551,] 0.12 3.89 1.82 0.36 0.12 0.12 0.01 [552,] 0.28 16.16 4.25 1.42 1.13 1.42 4.47 [553,] 0.26 12.35 3.94 1.05 1.58 1.05 3.70 [554,] 0.02 0.81 0.29 0.04 0.12 0.06 -0.97 [555,] 0.18 6.59 2.67 0.53 1.07 0.53 1.15 [556,] 0.20 7.35 2.98 0.99 0.99 0.40 1.69 [557,] 0.13 3.45 1.28 0.26 0.77 0.51 0.11 [558,] 0.04 1.65 0.67 0.09 0.22 0.18 -0.86 [559,] 0.09 2.77 1.30 0.09 0.43 0.17 -0.49 [560,] 0.13 4.23 1.32 0.40 0.79 0.40 0.19 [561,] -0.01 -0.25 -0.10 -0.03 -0.03 -0.01 -1.00 [562,] 0.24 6.35 0.35 0.47 1.18 1.18 2.77 [563,] 0.02 0.87 0.28 0.04 0.09 0.04 -0.98 [564,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [565,] 0.29 7.79 1.15 0.58 1.44 1.44 4.66 [566,] 0.16 4.35 1.61 0.65 0.16 0.81 0.77 [567,] 0.09 1.99 0.36 0.27 0.09 0.27 -0.45 [568,] 0.38 19.73 2.66 1.52 1.90 1.90 8.80 [569,] 0.13 3.41 0.50 0.13 0.38 0.63 0.08 [570,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [571,] -0.01 -0.22 -0.03 -0.01 -0.02 -0.01 -1.00 [572,] 0.30 5.24 0.22 0.60 0.90 1.50 5.10 [573,] 0.18 5.74 2.69 0.90 0.90 0.72 1.19 [574,] 0.19 4.10 0.75 0.19 0.56 0.93 1.37 [575,] 0.17 5.31 0.66 0.66 0.99 0.66 0.87 [576,] 0.06 1.27 0.09 0.17 0.29 0.12 -0.77 [577,] 0.07 3.04 1.09 0.14 0.36 0.29 -0.64 [578,] 0.14 4.36 0.95 0.54 0.54 0.54 0.26 [579,] 0.14 5.34 2.17 0.43 0.87 0.29 0.42 [580,] 0.03 1.24 0.44 0.09 0.18 0.09 -0.94 [581,] 0.07 1.84 0.27 0.07 0.34 0.27 -0.68 [582,] 0.05 1.89 0.76 0.20 0.36 0.15 -0.82 [583,] 0.14 5.11 2.07 0.41 0.83 0.55 0.30 [584,] 0.11 2.33 0.16 0.21 0.32 0.32 -0.24 [585,] 0.09 2.83 0.35 0.27 0.53 0.18 -0.47 [586,] 0.13 4.31 2.02 0.67 0.81 0.67 0.23 [587,] 0.24 12.48 3.60 0.24 1.20 1.20 2.92 [588,] 0.22 10.43 3.33 0.22 1.33 1.11 2.35 [589,] 0.10 3.21 1.51 0.40 0.40 0.40 -0.31 [590,] 0.07 2.31 1.08 0.22 0.22 0.14 -0.65 [591,] 0.16 4.28 1.11 0.63 0.16 0.32 0.71 [592,] 0.16 6.62 2.36 0.47 0.95 0.32 0.69 [593,] 0.07 3.09 1.10 0.15 0.22 0.15 -0.63 [594,] 0.23 6.22 1.61 0.46 1.15 0.92 2.61 [595,] 0.11 3.44 1.08 0.43 0.43 0.32 -0.21 [596,] 0.11 5.02 1.60 0.32 0.43 0.21 -0.22 [597,] 0.12 2.72 0.19 0.12 0.25 0.62 0.04 [598,] 0.15 4.66 1.46 0.29 0.73 0.58 0.44 [599,] 0.10 3.23 1.01 0.20 0.61 0.50 -0.31 [600,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 [601,] 0.09 3.02 1.41 0.28 0.09 0.47 -0.40 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 150 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsFormula, data = Affairs) $terms affairs ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairs 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairs age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 451 150 0 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation 5.608161 -0.050347 0.161852 -0.476324 0.106006 rating logSigma -0.712242 2.244542 $left [1] 0 $right [1] Inf class [1] "censReg" "maxLik" "maxim" "list" print( x, digits = 2 ) Call: censReg(formula = affairsFormula, data = Affairs) Coefficients: (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 print( x, logSigma = FALSE, digits = 2 ) Call: censReg(formula = affairsFormula, data = Affairs) Coefficients: (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating sigma -2.28 8.25 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating -0.04 0.13 -0.39 0.08 -0.53 printME( margEff( x ) ) age yearsmarried religiousness occupation rating -0.042 0.130 -0.394 0.076 -0.534 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0.000 0.000 0.000 0.000 yearsmarried 0 0.001 0.000 0.000 0.000 religiousness 0 0.000 0.009 0.000 0.000 occupation 0 0.000 0.000 0.004 0.000 rating 0 0.000 0.000 0.000 0.009 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.04 0.02 -2 0.02 * yearsmarried 0.13 0.03 4 4e-05 *** religiousness -0.39 0.09 -4 3e-05 *** occupation 0.08 0.06 1 0.20 rating -0.53 0.09 -6 3e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-Likelihood: -705.58 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 8.17 2.74 3 0.003 ** age -0.18 0.08 -2 0.023 * yearsmarried 0.55 0.13 4 4e-05 *** religiousness -1.69 0.40 -4 3e-05 *** occupation 0.33 0.25 1 0.200 rating -2.28 0.41 -6 2e-08 *** logSigma 2.11 0.07 31 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsFormula, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 8.17 2.74 3 0.003 ** age -0.18 0.08 -2 0.023 * yearsmarried 0.55 0.13 4 4e-05 *** religiousness -1.69 0.40 -4 3e-05 *** occupation 0.33 0.25 1 0.200 rating -2.28 0.41 -6 2e-08 *** logSigma 2.11 0.07 31 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -705.58 on 7 Df print( summary( x ), logSigma = FALSE, digits = sDigits ) Call: censReg(formula = affairsFormula, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 8.17 2.74 3 0.003 ** age -0.18 0.08 -2 0.023 * yearsmarried 0.55 0.13 4 4e-05 *** religiousness -1.69 0.40 -4 3e-05 *** occupation 0.33 0.25 1 0.200 rating -2.28 0.41 -6 2e-08 *** sigma 8.25 0.55 15 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -705.58 on 7 Df > round( coef( estResult ), 2 ) (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 > round( coef( estResult, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating sigma -2.28 8.25 > round( vcov( estResult ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.52 -0.12 0.09 -0.40 -0.18 -0.62 age -0.12 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.09 -0.01 0.02 -0.01 0.00 0.00 religiousness -0.40 0.00 -0.01 0.16 0.01 0.00 occupation -0.18 0.00 0.00 0.01 0.06 -0.01 rating -0.62 0.00 0.00 0.00 -0.01 0.17 logSigma 0.01 0.00 0.00 0.00 0.00 -0.01 logSigma (Intercept) 0.01 age 0.00 yearsmarried 0.00 religiousness 0.00 occupation 0.00 rating -0.01 logSigma 0.00 > round( vcov( estResult, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.52 -0.12 0.09 -0.40 -0.18 -0.62 age -0.12 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.09 -0.01 0.02 -0.01 0.00 0.00 religiousness -0.40 0.00 -0.01 0.16 0.01 0.00 occupation -0.18 0.00 0.00 0.01 0.06 -0.01 rating -0.62 0.00 0.00 0.00 -0.01 0.17 sigma 0.06 0.00 0.01 -0.04 0.01 -0.06 sigma (Intercept) 0.06 age 0.00 yearsmarried 0.01 religiousness -0.04 occupation 0.01 rating -0.06 sigma 0.31 > round( coef( summary( estResult ) ), 2 ) Estimate Std. error t value Pr(> t) (Intercept) 8.17 2.74 2.98 0.00 age -0.18 0.08 -2.27 0.02 yearsmarried 0.55 0.13 4.12 0.00 religiousness -1.69 0.40 -4.18 0.00 occupation 0.33 0.25 1.28 0.20 rating -2.28 0.41 -5.60 0.00 logSigma 2.11 0.07 31.44 0.00 > round( coef( summary( estResult ), logSigma = FALSE ), 2 ) Estimate Std. error t value Pr(> t) (Intercept) 8.17 2.74 2.98 0.00 age -0.18 0.08 -2.27 0.02 yearsmarried 0.55 0.13 4.12 0.00 religiousness -1.69 0.40 -4.18 0.00 occupation 0.33 0.25 1.28 0.20 rating -2.28 0.41 -5.60 0.00 sigma 8.25 0.55 14.90 0.00 > all.equal( margEff( estResult ), + margEff( estResult, xValues = estResult$xMean ) ) [1] TRUE > round( margEff( estResult, xValues = c( 1, 40, 4, 2, 4, 4 ) ), 2 ) age yearsmarried religiousness occupation rating -0.03 0.09 -0.28 0.05 -0.38 > printME( margEff( estResult, xValues = c( 1, 40, 4, 2, 4, 4 ) ) ) age yearsmarried religiousness occupation rating -0.030 0.092 -0.280 0.054 -0.380 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0.000 0.000 0.000 0.000 yearsmarried 0 0.000 -0.001 0.000 0.000 religiousness 0 -0.001 0.010 0.000 0.007 occupation 0 0.000 0.000 0.002 0.000 rating 0 0.000 0.007 0.000 0.011 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" > print( summary( margEff( estResult, xValues = c( 1, 40, 4, 2, 4, 4 ) ) ), + digits = 2 ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.0298 0.0083 -3.6 3e-04 *** yearsmarried 0.0922 0.0139 6.6 8e-11 *** religiousness -0.2804 0.1022 -2.7 0.006 ** occupation 0.0542 0.0415 1.3 0.192 rating -0.3800 0.1066 -3.6 4e-04 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > logLik( estResult ) 'log Lik.' -705.58 (df=7) > nobs( estResult ) [1] 601 > extractAIC( estResult ) [1] 7.0 1425.2 > formula( estResult ) affairs ~ age + yearsmarried + religiousness + occupation + rating > model.frame( estResult ) affairs age yearsmarried religiousness occupation rating 4 0 37.0 10.000 3 7 4 5 0 27.0 4.000 4 6 4 11 0 32.0 15.000 1 1 4 16 0 57.0 15.000 5 6 5 23 0 22.0 0.750 2 6 3 29 0 32.0 1.500 2 5 5 44 0 22.0 0.750 2 1 3 45 0 57.0 15.000 2 4 4 47 0 32.0 15.000 4 1 2 49 0 22.0 1.500 4 4 5 50 0 37.0 15.000 2 7 2 55 0 27.0 4.000 4 6 4 64 0 47.0 15.000 5 6 4 80 0 22.0 1.500 2 5 4 86 0 27.0 4.000 4 5 4 93 0 37.0 15.000 1 5 5 108 0 37.0 15.000 2 4 3 114 0 22.0 0.750 3 5 4 115 0 22.0 1.500 2 5 5 116 0 27.0 10.000 2 1 5 123 0 22.0 1.500 2 5 5 127 0 22.0 1.500 2 5 5 129 0 27.0 10.000 4 5 4 134 0 32.0 10.000 3 1 5 137 0 37.0 4.000 2 6 4 139 0 22.0 1.500 2 5 5 147 0 27.0 7.000 4 1 5 151 0 42.0 15.000 5 6 4 153 0 27.0 4.000 3 5 5 155 0 27.0 4.000 3 5 4 162 0 42.0 15.000 4 6 3 163 0 22.0 1.500 3 5 5 165 0 27.0 0.417 4 6 4 168 0 42.0 15.000 5 5 4 170 0 32.0 4.000 1 6 4 172 0 22.0 1.500 4 5 3 184 0 42.0 15.000 3 1 4 187 0 22.0 4.000 4 5 5 192 0 22.0 1.500 1 3 5 194 0 22.0 0.750 3 1 5 210 0 32.0 10.000 5 6 5 217 0 52.0 15.000 5 6 3 220 0 22.0 0.417 5 1 4 224 0 27.0 4.000 2 6 1 227 0 32.0 7.000 5 5 3 228 0 22.0 4.000 3 5 5 239 0 27.0 7.000 4 6 5 241 0 42.0 15.000 2 5 4 245 0 27.0 1.500 4 3 5 249 0 42.0 15.000 2 6 4 262 0 22.0 0.750 5 3 5 265 0 32.0 7.000 2 6 4 267 0 27.0 4.000 5 6 5 269 0 27.0 10.000 4 6 4 271 0 22.0 4.000 1 5 5 277 0 37.0 15.000 4 3 1 290 0 22.0 1.500 5 4 4 292 0 37.0 15.000 4 1 5 293 0 27.0 0.750 4 5 4 295 0 32.0 10.000 4 6 4 299 0 47.0 15.000 5 7 2 320 0 37.0 10.000 3 6 4 321 0 22.0 0.750 2 5 5 324 0 27.0 4.000 2 4 5 334 0 32.0 7.000 4 6 4 351 0 42.0 15.000 2 3 5 355 0 37.0 10.000 4 6 4 361 0 47.0 15.000 3 6 5 362 0 22.0 1.500 5 5 5 366 0 27.0 1.500 2 6 4 370 0 27.0 4.000 3 5 5 374 0 32.0 10.000 5 4 5 378 0 22.0 0.125 2 5 5 381 0 47.0 15.000 4 4 3 382 0 32.0 15.000 1 5 5 383 0 27.0 7.000 4 5 5 384 0 22.0 1.500 3 5 5 400 0 27.0 4.000 3 6 5 403 0 22.0 1.500 3 5 5 409 0 57.0 15.000 2 7 2 412 0 17.5 1.500 3 6 5 413 0 57.0 15.000 4 6 5 416 0 22.0 0.750 2 3 4 418 0 42.0 4.000 4 3 3 422 0 22.0 1.500 4 1 5 435 0 22.0 0.417 1 6 4 439 0 32.0 15.000 4 5 5 445 0 27.0 1.500 3 5 2 447 0 22.0 1.500 3 1 5 448 0 37.0 15.000 3 1 4 449 0 32.0 15.000 4 3 4 478 0 37.0 10.000 2 5 3 482 0 37.0 10.000 4 5 4 486 0 57.0 15.000 5 5 3 489 0 27.0 0.417 1 3 4 490 0 42.0 15.000 5 1 5 491 0 57.0 15.000 3 6 1 492 0 37.0 10.000 1 6 4 503 0 37.0 15.000 3 5 5 508 0 37.0 15.000 4 6 5 509 0 27.0 10.000 5 1 5 512 0 37.0 10.000 2 6 4 515 0 22.0 0.125 4 4 5 517 0 57.0 15.000 5 6 5 532 0 37.0 15.000 4 6 4 533 0 22.0 4.000 4 6 4 535 0 27.0 7.000 4 5 4 537 0 57.0 15.000 4 5 4 538 0 32.0 15.000 3 6 3 543 0 22.0 1.500 2 5 4 547 0 32.0 7.000 4 1 5 550 0 37.0 15.000 4 6 5 558 0 32.0 1.500 5 5 5 571 0 42.0 10.000 5 7 4 578 0 27.0 7.000 3 5 4 583 0 37.0 15.000 4 6 5 586 0 37.0 15.000 4 3 2 594 0 32.0 10.000 5 6 4 597 0 22.0 0.750 4 1 5 602 0 27.0 7.000 4 2 4 603 0 27.0 7.000 2 2 5 604 0 42.0 15.000 5 5 4 612 0 42.0 15.000 4 5 3 613 0 27.0 7.000 2 1 2 621 0 22.0 1.500 3 5 5 627 0 37.0 15.000 5 6 5 630 0 22.0 0.125 2 4 5 631 0 27.0 1.500 4 5 5 632 0 32.0 1.500 2 6 5 639 0 27.0 1.500 2 6 5 645 0 27.0 10.000 4 1 3 647 0 42.0 15.000 4 6 5 648 0 27.0 1.500 2 6 5 651 0 27.0 4.000 2 6 3 655 0 32.0 10.000 3 5 3 667 0 32.0 15.000 3 5 4 670 0 22.0 0.750 2 6 5 671 0 37.0 15.000 2 1 4 673 0 27.0 4.000 4 5 5 701 0 27.0 4.000 1 5 4 705 0 27.0 10.000 2 1 4 706 0 32.0 15.000 5 6 4 709 0 27.0 7.000 5 5 3 717 0 52.0 15.000 2 5 4 719 0 27.0 4.000 3 6 3 723 0 37.0 4.000 1 5 4 724 0 27.0 4.000 4 5 4 726 0 52.0 15.000 5 1 3 734 0 57.0 15.000 4 6 4 735 0 27.0 7.000 1 5 4 736 0 37.0 7.000 4 6 3 737 0 22.0 0.750 2 4 3 739 0 32.0 4.000 2 5 3 743 0 37.0 15.000 4 6 3 745 0 22.0 0.750 2 4 3 747 0 42.0 15.000 4 6 3 751 0 52.0 15.000 5 1 1 752 0 37.0 15.000 4 1 2 754 0 27.0 7.000 4 5 3 760 0 32.0 4.000 2 5 5 763 0 27.0 4.000 2 6 5 774 0 27.0 4.000 2 5 5 776 0 37.0 15.000 5 6 5 779 0 47.0 15.000 5 5 4 784 0 32.0 10.000 3 1 4 788 0 27.0 1.500 4 1 2 794 0 57.0 15.000 2 5 2 795 0 22.0 1.500 4 5 4 798 0 42.0 15.000 3 3 4 800 0 57.0 15.000 4 2 2 803 0 57.0 15.000 4 6 5 807 0 22.0 0.125 4 4 5 812 0 32.0 10.000 4 1 5 820 0 42.0 15.000 3 5 4 823 0 27.0 1.500 2 6 5 830 0 32.0 0.125 2 5 2 843 0 27.0 4.000 3 5 4 848 0 27.0 10.000 2 1 4 851 0 32.0 7.000 4 1 3 854 0 37.0 15.000 4 5 4 856 0 42.0 15.000 5 6 2 857 0 32.0 1.500 4 6 5 859 0 32.0 4.000 3 5 3 863 0 37.0 7.000 4 5 5 865 0 22.0 0.417 3 3 5 867 0 27.0 7.000 4 1 5 870 0 27.0 0.750 3 5 5 873 0 27.0 4.000 2 5 5 875 0 32.0 10.000 4 4 5 876 0 32.0 15.000 1 5 5 877 0 22.0 0.750 3 4 5 880 0 27.0 7.000 4 1 4 903 0 27.0 0.417 4 5 4 904 0 37.0 15.000 4 5 4 905 0 37.0 15.000 2 1 3 908 0 22.0 4.000 1 5 4 909 0 37.0 15.000 4 5 3 910 0 22.0 1.500 2 4 5 912 0 52.0 15.000 4 6 2 914 0 22.0 1.500 4 5 5 915 0 32.0 4.000 5 3 5 916 0 32.0 4.000 2 3 5 920 0 22.0 1.500 3 6 5 921 0 27.0 0.750 2 3 3 925 0 22.0 7.000 2 5 2 926 0 27.0 0.750 2 5 3 929 0 37.0 15.000 4 1 2 931 0 22.0 1.500 1 1 5 945 0 37.0 10.000 2 4 4 947 0 37.0 15.000 4 5 3 949 0 42.0 15.000 3 3 3 950 0 22.0 4.000 2 5 5 961 0 52.0 7.000 2 6 2 965 0 27.0 0.750 2 5 5 966 0 27.0 4.000 2 4 5 967 0 42.0 1.500 5 6 5 987 0 22.0 1.500 4 6 5 990 0 22.0 4.000 4 5 3 992 0 22.0 4.000 1 5 4 995 0 37.0 15.000 5 4 5 1009 0 37.0 10.000 3 6 3 1021 0 42.0 15.000 4 6 5 1026 0 47.0 15.000 4 5 5 1027 0 22.0 1.500 4 5 4 1030 0 32.0 10.000 3 1 4 1031 0 22.0 7.000 1 3 5 1034 0 32.0 10.000 4 5 4 1037 0 27.0 1.500 2 2 4 1038 0 37.0 15.000 4 5 5 1039 0 42.0 4.000 3 4 5 1045 0 37.0 15.000 5 5 4 1046 0 32.0 7.000 4 5 5 1054 0 42.0 15.000 4 6 5 1059 0 27.0 4.000 4 6 4 1063 0 22.0 0.750 4 6 5 1068 0 27.0 4.000 4 5 3 1070 0 22.0 0.750 5 1 5 1072 0 52.0 15.000 5 5 5 1073 0 32.0 10.000 3 5 5 1077 0 37.0 15.000 4 4 4 1081 0 32.0 7.000 2 5 4 1083 0 42.0 15.000 3 1 4 1084 0 32.0 15.000 1 5 5 1086 0 27.0 4.000 3 5 5 1087 0 32.0 15.000 4 3 4 1089 0 22.0 0.750 3 2 4 1096 0 22.0 1.500 3 5 3 1102 0 42.0 15.000 4 3 5 1103 0 52.0 15.000 3 5 4 1107 0 37.0 15.000 5 6 4 1109 0 47.0 15.000 4 2 3 1115 0 57.0 15.000 2 6 4 1119 0 32.0 7.000 4 5 5 1124 0 27.0 7.000 4 1 4 1126 0 22.0 1.500 1 6 5 1128 0 22.0 4.000 3 1 4 1129 0 22.0 1.500 2 1 5 1130 0 42.0 15.000 2 6 4 1133 0 57.0 15.000 4 2 4 1140 0 27.0 7.000 2 1 5 1143 0 22.0 4.000 3 1 5 1146 0 37.0 15.000 4 5 3 1153 0 32.0 7.000 1 6 4 1156 0 22.0 1.500 2 5 5 1157 0 22.0 1.500 3 1 3 1158 0 52.0 15.000 2 5 5 1160 0 37.0 15.000 2 1 1 1161 0 32.0 10.000 2 5 5 1166 0 42.0 15.000 4 4 5 1177 0 27.0 4.000 3 4 5 1178 0 37.0 15.000 4 6 5 1180 0 27.0 1.500 3 5 5 1187 0 22.0 0.125 2 6 3 1191 0 32.0 10.000 2 6 3 1195 0 27.0 4.000 4 5 4 1207 0 27.0 7.000 2 5 1 1208 0 32.0 4.000 5 6 3 1209 0 37.0 15.000 2 5 5 1211 0 47.0 15.000 4 6 4 1215 0 27.0 1.500 1 5 5 1221 0 37.0 15.000 4 6 4 1226 0 32.0 15.000 4 1 4 1229 0 32.0 7.000 4 5 4 1231 0 42.0 15.000 3 1 3 1234 0 27.0 7.000 3 1 4 1235 0 27.0 1.500 3 4 2 1242 0 22.0 1.500 3 3 5 1245 0 27.0 4.000 3 4 2 1260 0 27.0 7.000 3 1 2 1266 0 37.0 15.000 2 5 4 1271 0 37.0 7.000 3 4 4 1273 0 22.0 1.500 2 5 5 1276 0 37.0 15.000 5 5 4 1280 0 22.0 1.500 4 5 3 1282 0 32.0 10.000 4 1 5 1285 0 27.0 4.000 2 5 3 1295 0 22.0 0.417 4 5 5 1298 0 27.0 4.000 2 5 5 1299 0 37.0 15.000 4 5 3 1304 0 37.0 10.000 5 7 4 1305 0 27.0 7.000 2 4 2 1311 0 32.0 4.000 2 5 5 1314 0 32.0 4.000 2 6 4 1319 0 22.0 1.500 3 4 5 1322 0 22.0 4.000 4 3 4 1324 0 17.5 0.750 2 5 4 1327 0 32.0 10.000 4 4 5 1328 0 32.0 0.750 5 3 3 1330 0 37.0 15.000 4 5 3 1332 0 32.0 4.000 3 4 5 1333 0 27.0 1.500 2 3 2 1336 0 22.0 7.000 4 1 5 1341 0 47.0 15.000 5 6 5 1344 0 27.0 4.000 1 4 4 1352 0 37.0 15.000 5 1 3 1358 0 42.0 4.000 4 5 5 1359 0 32.0 4.000 2 1 5 1361 0 52.0 15.000 2 7 4 1364 0 22.0 1.500 2 1 4 1368 0 52.0 15.000 4 2 4 1384 0 22.0 0.417 3 1 5 1390 0 22.0 1.500 2 5 5 1393 0 27.0 4.000 4 6 4 1394 0 32.0 15.000 4 1 5 1402 0 27.0 1.500 2 3 5 1407 0 32.0 4.000 1 6 5 1408 0 37.0 15.000 3 6 4 1412 0 32.0 10.000 2 6 5 1413 0 32.0 10.000 5 5 5 1416 0 37.0 1.500 4 5 3 1417 0 32.0 1.500 2 4 4 1418 0 32.0 10.000 4 1 4 1419 0 47.0 15.000 4 5 4 1420 0 27.0 10.000 5 1 5 1423 0 27.0 4.000 3 4 5 1424 0 37.0 15.000 4 4 2 1432 0 27.0 0.750 4 5 5 1433 0 37.0 15.000 4 1 5 1437 0 32.0 15.000 3 1 5 1438 0 27.0 10.000 2 1 5 1439 0 27.0 7.000 2 6 5 1446 0 37.0 15.000 2 1 3 1450 0 27.0 1.500 2 4 4 1451 0 22.0 0.750 2 1 5 1452 0 22.0 4.000 4 2 4 1453 0 42.0 0.125 4 6 4 1456 0 27.0 1.500 4 6 5 1464 0 27.0 7.000 3 6 3 1469 0 52.0 15.000 4 1 3 1473 0 27.0 1.500 5 5 2 1481 0 27.0 1.500 2 5 5 1482 0 27.0 1.500 3 5 5 1496 0 22.0 0.125 5 4 4 1497 0 27.0 4.000 4 1 5 1504 0 27.0 4.000 4 1 5 1513 0 47.0 15.000 2 5 5 1515 0 32.0 15.000 3 5 3 1534 0 42.0 7.000 2 5 5 1535 0 22.0 0.750 4 6 4 1536 0 27.0 0.125 3 6 5 1540 0 32.0 10.000 3 6 5 1551 0 22.0 0.417 5 4 5 1555 0 47.0 15.000 5 1 4 1557 0 32.0 10.000 3 1 5 1566 0 57.0 15.000 4 5 5 1567 0 27.0 4.000 3 6 5 1576 0 32.0 7.000 4 1 5 1584 0 37.0 10.000 4 1 5 1585 0 32.0 10.000 1 1 4 1590 0 22.0 4.000 3 1 4 1594 0 27.0 7.000 4 3 2 1595 0 57.0 15.000 5 5 2 1603 0 32.0 7.000 2 5 5 1608 0 27.0 1.500 4 1 3 1609 0 22.0 1.500 4 5 5 1615 0 22.0 1.500 4 5 4 1616 0 32.0 7.000 3 1 5 1617 0 47.0 15.000 3 5 4 1620 0 22.0 0.750 3 1 5 1621 0 22.0 1.500 2 5 5 1637 0 27.0 4.000 1 5 5 1638 0 52.0 15.000 4 5 5 1650 0 32.0 10.000 4 6 5 1654 0 47.0 15.000 4 6 4 1665 0 27.0 7.000 2 1 2 1670 0 22.0 1.500 4 4 5 1671 0 32.0 10.000 2 5 4 1675 0 22.0 0.750 2 5 4 1688 0 22.0 1.500 2 5 5 1691 0 42.0 15.000 3 6 4 1695 0 27.0 7.000 5 4 5 1698 0 42.0 15.000 4 4 4 1704 0 57.0 15.000 3 5 2 1705 0 42.0 15.000 3 6 2 1711 0 32.0 7.000 2 1 2 1719 0 22.0 4.000 5 4 5 1723 0 22.0 1.500 1 6 5 1726 0 22.0 0.750 1 4 5 1749 0 32.0 15.000 4 1 5 1752 0 22.0 1.500 2 5 3 1754 0 27.0 4.000 5 2 5 1758 0 27.0 4.000 4 1 5 1761 0 42.0 15.000 5 5 4 1773 0 32.0 1.500 2 7 3 1775 0 57.0 15.000 4 3 1 1786 0 37.0 7.000 4 5 5 1793 0 52.0 15.000 2 5 4 1799 0 47.0 15.000 4 6 5 1803 0 27.0 7.000 2 5 4 1806 0 27.0 7.000 4 5 5 1807 0 22.0 4.000 2 3 3 1808 0 37.0 7.000 2 6 5 1814 0 27.0 7.000 4 4 3 1815 0 42.0 10.000 4 6 4 1818 0 22.0 1.500 3 1 5 1827 0 22.0 4.000 2 1 3 1834 0 57.0 15.000 4 6 5 1835 0 37.0 15.000 4 4 3 1843 0 27.0 7.000 3 5 5 1846 0 17.5 10.000 4 4 5 1850 0 22.0 4.000 4 5 5 1851 0 27.0 4.000 2 1 4 1854 0 37.0 15.000 2 5 1 1859 0 22.0 1.500 5 1 4 1861 0 27.0 7.000 2 5 4 1866 0 27.0 4.000 4 5 5 1873 0 22.0 0.125 1 3 5 1875 0 27.0 7.000 4 1 4 1885 0 32.0 15.000 5 5 3 1892 0 32.0 10.000 4 5 4 1895 0 32.0 15.000 2 3 4 1896 0 22.0 1.500 3 5 5 1897 0 27.0 4.000 4 4 4 1899 0 52.0 15.000 5 1 5 1904 0 27.0 7.000 2 1 2 1905 0 27.0 7.000 3 1 4 1908 0 42.0 15.000 2 1 4 1916 0 42.0 15.000 4 5 4 1918 0 27.0 7.000 4 3 3 1920 0 27.0 7.000 2 6 2 1930 0 42.0 15.000 3 3 3 1940 0 27.0 4.000 3 3 5 1947 0 27.0 7.000 3 1 4 1949 0 22.0 1.500 2 4 5 1951 0 27.0 4.000 4 1 4 1952 0 22.0 4.000 4 5 5 1960 0 22.0 1.500 2 4 5 9001 0 47.0 15.000 4 5 4 9012 0 37.0 10.000 2 6 2 9023 0 37.0 15.000 3 5 4 9029 0 27.0 4.000 2 1 4 6 3 27.0 1.500 3 4 4 12 3 27.0 4.000 3 1 5 43 7 37.0 15.000 5 6 2 53 12 32.0 10.000 3 5 2 67 1 22.0 0.125 4 5 5 79 1 22.0 1.500 2 1 5 122 12 37.0 15.000 4 5 2 126 7 22.0 1.500 2 3 4 133 2 37.0 15.000 2 6 4 138 3 32.0 15.000 4 3 2 154 1 37.0 15.000 4 4 2 159 7 42.0 15.000 3 1 4 174 12 42.0 15.000 5 4 1 176 12 37.0 10.000 2 6 2 181 12 32.0 15.000 3 1 2 182 3 27.0 4.000 1 6 5 186 7 37.0 10.000 2 7 3 189 7 27.0 4.000 3 5 5 204 1 42.0 15.000 4 5 5 215 1 47.0 15.000 5 4 5 232 7 27.0 4.000 3 5 4 233 1 27.0 7.000 5 1 4 252 12 27.0 1.500 3 5 4 253 12 27.0 7.000 4 6 2 274 3 42.0 15.000 4 5 4 275 7 27.0 10.000 4 7 3 287 1 27.0 1.500 2 5 2 288 1 32.0 4.000 4 6 4 325 1 27.0 7.000 3 1 3 328 3 32.0 10.000 4 1 4 344 3 27.0 4.000 2 7 2 353 1 17.5 0.750 5 4 5 354 1 32.0 10.000 4 1 5 367 7 32.0 7.000 2 6 4 369 7 37.0 15.000 2 6 4 390 7 37.0 10.000 1 5 3 392 12 32.0 10.000 2 5 5 423 7 52.0 15.000 2 6 4 432 7 42.0 15.000 1 1 3 436 1 52.0 15.000 2 6 3 483 2 37.0 15.000 3 6 5 513 12 22.0 4.000 3 3 4 516 12 27.0 7.000 1 6 2 518 1 27.0 4.000 3 5 5 520 12 47.0 15.000 4 6 5 526 12 42.0 15.000 4 1 1 528 7 27.0 4.000 3 3 4 553 7 32.0 7.000 4 4 5 576 1 32.0 0.417 3 3 4 611 3 47.0 15.000 5 5 4 625 12 37.0 15.000 2 5 4 635 7 22.0 4.000 2 6 4 646 1 27.0 4.000 2 4 5 657 7 52.0 15.000 5 1 3 659 1 27.0 4.000 3 3 3 666 1 27.0 10.000 4 1 4 679 1 32.0 7.000 3 7 4 729 7 32.0 7.000 2 4 1 755 3 22.0 1.500 1 3 2 758 7 22.0 4.000 3 6 4 770 7 42.0 15.000 4 6 4 786 2 57.0 15.000 1 5 4 797 7 32.0 4.000 3 5 2 811 1 27.0 4.000 1 4 4 834 7 32.0 7.000 4 1 4 858 2 57.0 15.000 1 4 4 885 7 42.0 15.000 4 5 2 893 7 37.0 10.000 1 5 3 927 3 42.0 15.000 3 6 1 928 1 52.0 15.000 3 4 4 933 2 27.0 7.000 3 5 3 951 12 32.0 7.000 2 4 2 968 1 22.0 4.000 4 2 5 972 3 27.0 7.000 3 6 4 975 12 37.0 15.000 1 5 5 977 7 32.0 15.000 3 1 3 981 7 27.0 7.000 2 5 5 986 1 32.0 7.000 3 5 3 1002 1 32.0 1.500 2 2 4 1007 12 42.0 15.000 4 1 2 1011 7 32.0 10.000 3 5 4 1035 7 37.0 4.000 1 6 3 1050 1 27.0 4.000 2 5 3 1056 12 42.0 15.000 3 4 3 1057 1 27.0 10.000 5 6 5 1075 12 37.0 10.000 2 6 2 1080 12 27.0 7.000 1 3 3 1125 3 27.0 7.000 4 1 2 1131 3 32.0 10.000 2 4 4 1138 12 17.5 0.750 2 1 3 1150 12 32.0 15.000 3 5 4 1163 2 22.0 7.000 4 4 3 1169 1 32.0 7.000 4 6 5 1198 7 27.0 4.000 2 6 2 1204 1 22.0 1.500 5 5 3 1218 12 32.0 15.000 3 5 1 1230 12 42.0 15.000 2 1 2 1236 7 42.0 15.000 3 5 4 1247 12 32.0 10.000 2 4 2 1259 12 32.0 15.000 3 1 1 1294 7 57.0 15.000 5 4 5 1353 12 47.0 15.000 4 6 4 1370 2 42.0 15.000 2 6 3 1427 12 37.0 15.000 3 6 3 1445 12 37.0 15.000 5 5 2 1460 7 27.0 10.000 2 6 4 1480 2 37.0 15.000 2 5 4 1505 12 32.0 15.000 1 5 2 1543 7 32.0 10.000 3 6 3 1548 2 37.0 15.000 4 5 1 1550 7 27.0 1.500 2 5 5 1561 3 47.0 15.000 2 5 2 1564 12 37.0 15.000 2 5 4 1573 12 27.0 4.000 2 5 5 1575 2 27.0 10.000 4 1 5 1599 1 22.0 4.000 3 1 3 1622 12 52.0 7.000 4 5 5 1629 2 27.0 4.000 1 3 5 1664 7 37.0 15.000 2 6 4 1669 2 27.0 4.000 1 3 1 1674 12 17.5 0.750 2 3 5 1682 7 32.0 15.000 5 5 4 1685 7 22.0 4.000 1 3 5 1697 2 32.0 4.000 4 6 4 1716 1 22.0 1.500 3 5 2 1730 3 42.0 15.000 2 5 4 1731 1 32.0 7.000 4 4 4 1732 12 37.0 15.000 3 6 2 1743 1 42.0 15.000 3 6 3 1751 1 27.0 4.000 1 5 4 1757 2 37.0 15.000 4 7 3 1763 7 37.0 15.000 3 6 4 1766 3 22.0 1.500 2 3 3 1772 3 32.0 4.000 3 6 2 1776 2 32.0 15.000 5 6 5 1782 12 52.0 15.000 1 5 5 1784 12 47.0 15.000 1 6 5 1791 3 32.0 15.000 4 4 4 1831 7 32.0 15.000 3 3 2 1840 7 27.0 7.000 4 1 2 1844 12 42.0 15.000 3 6 2 1856 7 42.0 15.000 2 3 2 1876 12 27.0 7.000 2 5 4 1929 3 32.0 10.000 4 4 3 1935 7 47.0 15.000 3 4 2 1938 1 22.0 1.500 1 2 5 1941 7 32.0 10.000 2 5 4 1954 2 32.0 10.000 2 6 5 1959 2 22.0 7.000 3 6 2 9010 1 32.0 15.000 3 1 5 > round( estfun( estResult )[ 20 * c(1:30), ], 2 ) (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.05 -1.38 -0.51 -0.10 -0.05 -0.26 -0.29 [2,] -0.02 -0.44 -0.01 -0.06 -0.02 -0.10 -0.23 [3,] -0.05 -1.56 -0.49 -0.20 -0.29 -0.20 -0.29 [4,] -0.10 -5.84 -1.54 -0.20 -0.72 -0.20 0.06 [5,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [6,] -0.04 -0.98 -0.25 -0.14 -0.07 -0.14 -0.29 [7,] -0.07 -1.77 -0.26 -0.07 -0.33 -0.26 -0.24 [8,] -0.03 -1.07 -0.13 -0.07 -0.17 -0.17 -0.28 [9,] -0.06 -2.21 -0.89 -0.24 -0.30 -0.24 -0.26 [10,] -0.02 -0.44 -0.03 -0.08 -0.10 -0.10 -0.23 [11,] -0.03 -1.20 -0.49 -0.16 -0.13 -0.16 -0.28 [12,] -0.06 -2.12 -0.86 -0.23 -0.23 -0.23 -0.27 [13,] -0.04 -1.09 -0.28 -0.08 -0.04 -0.20 -0.29 [14,] -0.04 -1.08 -0.06 -0.04 -0.20 -0.20 -0.29 [15,] -0.04 -1.30 -0.35 -0.18 -0.25 -0.14 -0.29 [16,] -0.04 -1.86 -0.54 -0.14 -0.07 -0.14 -0.29 [17,] -0.05 -1.38 -0.51 -0.10 -0.05 -0.26 -0.29 [18,] -0.02 -0.58 0.00 -0.06 -0.13 -0.11 -0.24 [19,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.07 -1.46 -0.10 -0.13 -0.33 -0.20 -0.23 [21,] -0.05 -0.81 -0.47 -0.19 -0.19 -0.23 -0.29 [22,] -0.11 -2.93 -0.76 -0.22 -0.65 -0.22 0.13 [23,] 0.04 1.47 0.60 0.08 0.24 0.16 -0.89 [24,] 0.08 2.13 0.55 0.24 0.08 0.24 -0.58 [25,] 0.17 5.44 0.07 0.51 0.51 0.68 0.96 [26,] -0.01 -0.35 -0.13 -0.03 -0.05 -0.01 -1.00 [27,] 0.09 2.93 0.92 0.18 0.37 0.37 -0.43 [28,] 0.13 4.23 1.32 0.40 0.79 0.40 0.19 [29,] 0.03 1.24 0.44 0.09 0.18 0.09 -0.94 [30,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 > round( meat( estResult ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 0.01 0.28 0.08 0.02 0.04 0.03 age 0.28 10.33 2.90 0.86 1.22 1.05 yearsmarried 0.08 2.90 0.93 0.24 0.33 0.27 religiousness 0.02 0.86 0.24 0.08 0.10 0.09 occupation 0.04 1.22 0.33 0.10 0.17 0.13 rating 0.03 1.05 0.27 0.09 0.13 0.13 logSigma 0.06 1.94 0.44 0.18 0.24 0.24 logSigma (Intercept) 0.06 age 1.94 yearsmarried 0.44 religiousness 0.18 occupation 0.24 rating 0.24 logSigma 0.74 > round( bread( estResult ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 4516.83 -71.93 53.70 -238.60 -106.12 -370.48 age -71.93 3.76 -4.84 0.32 -2.36 0.67 yearsmarried 53.70 -4.84 10.88 -6.05 2.15 2.87 religiousness -238.60 0.32 -6.05 97.97 3.13 -0.02 occupation -106.12 -2.36 2.15 3.13 38.90 -3.72 rating -370.48 0.67 2.87 -0.02 -3.72 99.96 logSigma 4.19 -0.33 0.96 -2.97 0.54 -4.13 logSigma (Intercept) 4.19 age -0.33 yearsmarried 0.96 religiousness -2.97 occupation 0.54 rating -4.13 logSigma 2.71 > round( sandwich( estResult ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 9.47 -0.19 0.16 -0.42 -0.16 -0.67 age -0.19 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.16 -0.01 0.02 -0.01 0.01 0.00 religiousness -0.42 0.00 -0.01 0.16 0.00 -0.01 occupation -0.16 0.00 0.01 0.00 0.06 0.00 rating -0.67 0.00 0.00 -0.01 0.00 0.15 logSigma -0.04 0.00 0.00 0.00 0.00 0.00 logSigma (Intercept) -0.04 age 0.00 yearsmarried 0.00 religiousness 0.00 occupation 0.00 rating 0.00 logSigma 0.00 > # all.equal( sandwich( estResult ), vcov( estResult ) ) > waldtest( estResult, . ~ . - age ) Wald test Model 1: affairs ~ age + yearsmarried + religiousness + occupation + rating Model 2: affairs ~ yearsmarried + religiousness + occupation + rating Res.Df Df Chisq Pr(>Chisq) 1 594 2 595 -1 5.14 0.023 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > waldtest( estResult, . ~ . - age, vcov = sandwich( estResult ) ) Wald test Model 1: affairs ~ age + yearsmarried + religiousness + occupation + rating Model 2: affairs ~ yearsmarried + religiousness + occupation + rating Res.Df Df Chisq Pr(>Chisq) 1 594 2 595 -1 4.07 0.044 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > ## usual tobit estimation, BHHH method > estResultBhhh <- censReg( affairsFormula, data = Affairs, method = "BHHH" ) > printAll( estResultBhhh, meReturnJacobian = TRUE ) $maximum [1] -705.58 $estimate (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 $gradient (Intercept) age yearsmarried religiousness occupation 0.001 0.036 0.016 0.004 0.006 rating logSigma 0.003 0.035 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.0 -169.4 -45.8 -14.9 -21.2 -18.7 age -169.4 -6208.2 -1742.3 -518.5 -732.8 -630.5 yearsmarried -45.8 -1742.3 -561.8 -143.2 -198.6 -163.9 religiousness -14.9 -518.5 -143.2 -51.0 -62.3 -55.4 occupation -21.2 -732.8 -198.6 -62.3 -104.9 -80.3 rating -18.7 -630.5 -163.9 -55.4 -80.3 -77.2 logSigma -33.5 -1166.3 -266.0 -107.1 -142.4 -144.7 logSigma (Intercept) -33.5 age -1166.3 yearsmarried -266.0 religiousness -107.1 occupation -142.4 rating -144.7 logSigma -443.2 attr(,"type") [1] "BHHH" $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "BHHH maximisation" $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.06 -2.09 -0.56 -0.17 -0.40 -0.23 -0.27 [2,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [3,] -0.10 -3.17 -1.49 -0.10 -0.10 -0.40 0.02 [4,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [5,] -0.07 -1.44 -0.05 -0.13 -0.39 -0.20 -0.24 [6,] -0.03 -0.85 -0.04 -0.05 -0.13 -0.13 -0.26 [7,] -0.05 -1.18 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.06 -3.18 -0.84 -0.11 -0.22 -0.22 -0.28 [9,] -0.09 -3.02 -1.42 -0.38 -0.09 -0.19 -0.02 [10,] -0.02 -0.41 -0.03 -0.08 -0.08 -0.09 -0.22 [11,] -0.14 -5.15 -2.09 -0.28 -0.97 -0.28 0.58 [12,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [13,] -0.04 -1.82 -0.58 -0.19 -0.23 -0.16 -0.29 [14,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [15,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [16,] -0.08 -3.04 -1.23 -0.08 -0.41 -0.41 -0.13 [17,] -0.11 -3.91 -1.58 -0.21 -0.42 -0.32 0.10 [18,] -0.04 -0.81 -0.03 -0.11 -0.18 -0.15 -0.29 [19,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.05 -1.38 -0.51 -0.10 -0.05 -0.26 -0.29 [21,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [22,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [23,] -0.05 -1.42 -0.53 -0.21 -0.26 -0.21 -0.28 [24,] -0.04 -1.13 -0.35 -0.11 -0.04 -0.18 -0.29 [25,] -0.04 -1.60 -0.17 -0.09 -0.26 -0.17 -0.29 [26,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [27,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [28,] -0.04 -1.86 -0.66 -0.22 -0.27 -0.18 -0.29 [29,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [30,] -0.04 -1.13 -0.17 -0.13 -0.21 -0.17 -0.29 [31,] -0.07 -3.07 -1.10 -0.29 -0.44 -0.22 -0.20 [32,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [33,] -0.02 -0.66 -0.01 -0.10 -0.15 -0.10 -0.25 [34,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [35,] -0.06 -1.95 -0.24 -0.06 -0.37 -0.24 -0.26 [36,] -0.04 -0.94 -0.06 -0.17 -0.21 -0.13 -0.29 [37,] -0.06 -2.35 -0.84 -0.17 -0.06 -0.22 -0.27 [38,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [39,] -0.04 -0.92 -0.06 -0.04 -0.12 -0.21 -0.29 [40,] -0.02 -0.44 -0.01 -0.06 -0.02 -0.10 -0.23 [41,] -0.03 -0.85 -0.26 -0.13 -0.16 -0.13 -0.26 [42,] -0.05 -2.46 -0.71 -0.24 -0.28 -0.14 -0.29 [43,] -0.02 -0.34 -0.01 -0.08 -0.02 -0.06 -0.20 [44,] -0.11 -3.10 -0.46 -0.23 -0.69 -0.11 0.21 [45,] -0.04 -1.28 -0.28 -0.20 -0.20 -0.12 -0.29 [46,] -0.03 -0.75 -0.14 -0.10 -0.17 -0.17 -0.29 [47,] -0.03 -0.83 -0.22 -0.12 -0.19 -0.15 -0.28 [48,] -0.08 -3.34 -1.19 -0.16 -0.40 -0.32 -0.15 [49,] -0.01 -0.40 -0.02 -0.06 -0.04 -0.07 -0.19 [50,] -0.08 -3.46 -1.23 -0.16 -0.49 -0.33 -0.13 [51,] -0.01 -0.25 -0.01 -0.06 -0.03 -0.06 -0.16 [52,] -0.06 -1.95 -0.43 -0.12 -0.37 -0.24 -0.26 [53,] -0.02 -0.46 -0.07 -0.08 -0.10 -0.08 -0.21 [54,] -0.05 -1.48 -0.55 -0.22 -0.33 -0.22 -0.28 [55,] -0.06 -1.21 -0.22 -0.06 -0.28 -0.28 -0.28 [56,] -0.11 -4.23 -1.71 -0.46 -0.34 -0.11 0.21 [57,] -0.02 -0.46 -0.03 -0.11 -0.08 -0.08 -0.24 [58,] -0.04 -1.34 -0.54 -0.15 -0.04 -0.18 -0.29 [59,] -0.02 -0.65 -0.02 -0.10 -0.12 -0.10 -0.25 [60,] -0.05 -1.56 -0.49 -0.20 -0.29 -0.20 -0.29 [61,] -0.07 -3.45 -1.10 -0.37 -0.51 -0.15 -0.20 [62,] -0.05 -2.00 -0.54 -0.16 -0.33 -0.22 -0.28 [63,] 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-0.29 [431,] -0.09 -2.86 -1.34 -0.18 -0.27 -0.36 -0.07 [432,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [433,] -0.03 -0.83 -0.12 -0.12 -0.12 -0.12 -0.28 [434,] -0.02 -0.88 -0.25 -0.08 -0.02 -0.08 -0.21 [435,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [436,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [437,] -0.07 -2.88 -1.03 -0.14 -0.07 -0.27 -0.22 [438,] -0.05 -2.23 -0.80 -0.21 -0.27 -0.21 -0.28 [439,] -0.05 -1.42 -0.37 -0.21 -0.16 -0.16 -0.28 [440,] -0.11 -2.93 -0.76 -0.22 -0.65 -0.22 0.13 [441,] -0.08 -3.32 -1.19 -0.24 -0.24 -0.24 -0.16 [442,] -0.03 -0.71 -0.11 -0.08 -0.08 -0.13 -0.26 [443,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [444,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [445,] -0.03 -0.70 -0.10 -0.10 -0.03 -0.10 -0.26 [446,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [447,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [448,] -0.05 -2.22 -0.71 -0.19 -0.24 -0.19 -0.29 [449,] -0.11 -3.97 -1.07 -0.21 -0.64 -0.21 0.12 [450,] -0.07 -2.70 -1.09 -0.22 -0.36 -0.29 -0.20 [451,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [452,] 0.17 4.66 0.26 0.52 0.69 0.69 1.02 [453,] 0.20 5.40 0.80 0.60 0.20 1.00 1.72 [454,] 0.12 4.46 1.81 0.60 0.72 0.24 -0.01 [455,] 0.18 5.66 1.77 0.53 0.88 0.35 1.13 [456,] 0.19 4.28 0.02 0.78 0.97 0.97 1.58 [457,] 0.15 3.37 0.23 0.31 0.15 0.77 0.59 [458,] 0.17 6.44 2.61 0.70 0.87 0.35 1.06 [459,] 0.20 4.36 0.30 0.40 0.59 0.79 1.67 [460,] 0.04 1.47 0.60 0.08 0.24 0.16 -0.89 [461,] 0.04 1.22 0.57 0.15 0.11 0.08 -0.90 [462,] 0.02 0.63 0.26 0.07 0.07 0.03 -0.98 [463,] 0.18 7.36 2.63 0.53 0.18 0.70 1.09 [464,] 0.18 7.70 2.75 0.92 0.73 0.18 1.28 [465,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [466,] 0.16 4.97 2.33 0.47 0.16 0.31 0.64 [467,] 0.13 3.42 0.51 0.13 0.76 0.63 0.09 [468,] 0.12 4.28 1.16 0.23 0.81 0.35 -0.09 [469,] 0.24 6.47 0.96 0.72 1.20 1.20 2.91 [470,] 0.13 5.30 1.89 0.50 0.63 0.63 0.08 [471,] 0.17 7.94 2.54 0.85 0.68 0.85 0.94 [472,] 0.21 5.57 0.82 0.62 1.03 0.82 1.89 [473,] 0.16 4.38 1.14 0.81 0.16 0.65 0.79 [474,] 0.30 8.10 0.45 0.90 1.50 1.20 5.12 [475,] 0.21 5.62 1.46 0.83 1.25 0.42 1.94 [476,] 0.12 5.13 1.83 0.49 0.61 0.49 0.01 [477,] 0.14 3.75 1.39 0.56 0.97 0.42 0.31 [478,] 0.05 1.25 0.07 0.09 0.23 0.09 -0.85 [479,] 0.15 4.84 0.60 0.60 0.91 0.60 0.55 [480,] 0.08 2.13 0.55 0.24 0.08 0.24 -0.57 [481,] 0.16 4.98 1.56 0.62 0.16 0.62 0.65 [482,] 0.05 1.24 0.18 0.09 0.32 0.09 -0.86 [483,] 0.21 3.63 0.16 1.04 0.83 1.04 1.92 [484,] 0.16 5.11 1.60 0.64 0.16 0.80 0.74 [485,] 0.17 5.29 1.16 0.33 0.99 0.66 0.86 [486,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [487,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [488,] 0.25 8.09 2.53 0.51 1.26 1.26 3.35 [489,] 0.15 7.95 2.29 0.31 0.92 0.61 0.59 [490,] 0.09 3.87 1.38 0.09 0.09 0.28 -0.42 [491,] 0.03 1.61 0.47 0.06 0.19 0.09 -0.93 [492,] 0.10 3.63 1.47 0.29 0.59 0.49 -0.34 [493,] 0.28 6.07 1.10 0.83 0.83 1.10 4.18 [494,] 0.13 3.61 0.94 0.13 0.80 0.27 0.22 [495,] 0.15 4.09 0.61 0.45 0.76 0.76 0.56 [496,] 0.30 13.93 4.45 1.19 1.78 1.48 4.97 [497,] 0.17 7.26 2.59 0.69 0.17 0.17 1.03 [498,] 0.22 5.83 0.86 0.65 0.65 0.86 2.17 [499,] 0.26 8.26 1.81 1.03 1.03 1.29 3.53 [500,] 0.17 5.44 0.07 0.51 0.51 0.68 0.96 [501,] 0.16 7.52 2.40 0.80 0.80 0.64 0.74 [502,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [503,] 0.16 3.59 0.65 0.33 0.98 0.65 0.82 [504,] 0.13 3.55 0.53 0.26 0.53 0.66 0.18 [505,] 0.22 11.32 3.26 1.09 0.22 0.65 2.22 [506,] 0.09 2.54 0.38 0.28 0.28 0.28 -0.40 [507,] 0.11 3.05 1.13 0.45 0.11 0.45 -0.13 [508,] 0.10 3.11 0.68 0.29 0.68 0.39 -0.36 [509,] 0.07 2.37 0.52 0.15 0.30 0.07 -0.63 [510,] 0.05 1.04 0.07 0.05 0.14 0.09 -0.85 [511,] 0.19 4.14 0.75 0.56 1.13 0.75 1.41 [512,] 0.18 7.40 2.64 0.70 1.06 0.70 1.11 [513,] 0.07 4.13 1.09 0.07 0.36 0.29 -0.64 [514,] 0.15 4.87 0.61 0.46 0.76 0.30 0.57 [515,] 0.07 1.97 0.29 0.07 0.29 0.29 -0.64 [516,] 0.24 7.64 1.67 0.96 0.24 0.96 2.88 [517,] 0.08 4.41 1.16 0.08 0.31 0.31 -0.59 [518,] 0.11 4.77 1.71 0.45 0.57 0.23 -0.12 [519,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [520,] -0.01 -0.35 -0.12 -0.02 -0.05 -0.01 -1.00 [521,] 0.10 5.15 1.48 0.30 0.40 0.40 -0.33 [522,] 0.07 2.01 0.52 0.22 0.37 0.22 -0.62 [523,] 0.18 5.80 1.27 0.36 0.72 0.36 1.23 [524,] 0.18 3.91 0.71 0.71 0.36 0.89 1.14 [525,] 0.12 3.19 0.83 0.35 0.71 0.47 -0.05 [526,] 0.20 7.42 3.01 0.20 1.00 1.00 1.73 [527,] 0.12 3.69 1.73 0.35 0.12 0.35 -0.10 [528,] 0.19 5.14 1.33 0.38 0.95 0.95 1.47 [529,] 0.07 2.34 0.51 0.22 0.37 0.22 -0.64 [530,] 0.14 4.51 0.21 0.28 0.28 0.56 0.35 [531,] 0.21 8.67 3.10 0.83 0.21 0.41 1.90 [532,] 0.17 5.45 1.70 0.51 0.85 0.68 0.98 [533,] 0.14 5.35 0.58 0.14 0.87 0.43 0.42 [534,] 0.06 1.61 0.24 0.12 0.30 0.18 -0.76 [535,] 0.20 8.43 3.01 0.60 0.80 0.60 1.74 [536,] 0.15 3.98 1.47 0.74 0.88 0.74 0.48 [537,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [538,] 0.18 4.90 1.27 0.18 0.54 0.54 1.24 [539,] 0.10 2.69 0.70 0.40 0.10 0.20 -0.32 [540,] 0.09 2.93 0.92 0.18 0.37 0.37 -0.43 [541,] 0.24 4.23 0.18 0.48 0.24 0.73 2.98 [542,] 0.20 6.50 3.05 0.61 1.02 0.81 1.81 [543,] 0.09 2.00 0.64 0.36 0.36 0.27 -0.44 [544,] 0.16 5.13 1.12 0.64 0.96 0.80 0.75 [545,] 0.11 2.95 0.44 0.22 0.66 0.22 -0.19 [546,] 0.14 3.10 0.21 0.71 0.71 0.42 0.35 [547,] 0.10 3.28 1.54 0.31 0.51 0.10 -0.29 [548,] 0.16 6.59 2.35 0.31 0.16 0.31 0.67 [549,] 0.16 6.56 2.34 0.47 0.78 0.62 0.66 [550,] 0.16 5.02 1.57 0.31 0.63 0.31 0.67 [551,] 0.12 3.89 1.82 0.36 0.12 0.12 0.01 [552,] 0.28 16.17 4.25 1.42 1.13 1.42 4.47 [553,] 0.26 12.35 3.94 1.05 1.58 1.05 3.70 [554,] 0.02 0.81 0.29 0.04 0.12 0.06 -0.97 [555,] 0.18 6.59 2.67 0.53 1.07 0.53 1.16 [556,] 0.20 7.36 2.98 0.99 0.99 0.40 1.69 [557,] 0.13 3.45 1.28 0.26 0.77 0.51 0.11 [558,] 0.04 1.65 0.67 0.09 0.22 0.18 -0.86 [559,] 0.09 2.77 1.30 0.09 0.43 0.17 -0.49 [560,] 0.13 4.23 1.32 0.40 0.79 0.40 0.19 [561,] -0.01 -0.24 -0.10 -0.03 -0.03 -0.01 -1.00 [562,] 0.24 6.35 0.35 0.47 1.18 1.18 2.77 [563,] 0.02 0.87 0.28 0.04 0.09 0.04 -0.98 [564,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [565,] 0.29 7.79 1.15 0.58 1.44 1.44 4.66 [566,] 0.16 4.36 1.61 0.65 0.16 0.81 0.77 [567,] 0.09 1.99 0.36 0.27 0.09 0.27 -0.45 [568,] 0.38 19.74 2.66 1.52 1.90 1.90 8.80 [569,] 0.13 3.41 0.50 0.13 0.38 0.63 0.08 [570,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [571,] -0.01 -0.22 -0.03 -0.01 -0.02 -0.01 -1.00 [572,] 0.30 5.24 0.22 0.60 0.90 1.50 5.10 [573,] 0.18 5.74 2.69 0.90 0.90 0.72 1.19 [574,] 0.19 4.10 0.75 0.19 0.56 0.93 1.37 [575,] 0.17 5.31 0.66 0.66 0.99 0.66 0.87 [576,] 0.06 1.27 0.09 0.17 0.29 0.12 -0.77 [577,] 0.07 3.04 1.09 0.14 0.36 0.29 -0.64 [578,] 0.14 4.36 0.95 0.55 0.55 0.55 0.26 [579,] 0.14 5.34 2.17 0.43 0.87 0.29 0.42 [580,] 0.03 1.24 0.44 0.09 0.18 0.09 -0.94 [581,] 0.07 1.85 0.27 0.07 0.34 0.27 -0.68 [582,] 0.05 1.89 0.76 0.20 0.36 0.15 -0.82 [583,] 0.14 5.11 2.07 0.41 0.83 0.55 0.30 [584,] 0.11 2.33 0.16 0.21 0.32 0.32 -0.24 [585,] 0.09 2.83 0.35 0.27 0.53 0.18 -0.47 [586,] 0.13 4.31 2.02 0.67 0.81 0.67 0.23 [587,] 0.24 12.48 3.60 0.24 1.20 1.20 2.92 [588,] 0.22 10.43 3.33 0.22 1.33 1.11 2.35 [589,] 0.10 3.22 1.51 0.40 0.40 0.40 -0.31 [590,] 0.07 2.31 1.08 0.22 0.22 0.14 -0.65 [591,] 0.16 4.28 1.11 0.63 0.16 0.32 0.71 [592,] 0.16 6.62 2.36 0.47 0.95 0.32 0.69 [593,] 0.07 3.09 1.11 0.15 0.22 0.15 -0.63 [594,] 0.23 6.22 1.61 0.46 1.15 0.92 2.61 [595,] 0.11 3.44 1.08 0.43 0.43 0.32 -0.21 [596,] 0.11 5.02 1.60 0.32 0.43 0.21 -0.22 [597,] 0.12 2.72 0.19 0.12 0.25 0.62 0.04 [598,] 0.15 4.66 1.46 0.29 0.73 0.58 0.44 [599,] 0.10 3.23 1.01 0.20 0.61 0.50 -0.31 [600,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 [601,] 0.09 3.02 1.41 0.28 0.09 0.47 -0.40 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 150 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsFormula, data = Affairs, method = "BHHH") $terms affairs ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairs 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairs age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 451 150 0 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation 5.608161 -0.050347 0.161852 -0.476324 0.106006 rating logSigma -0.712242 2.244542 $left [1] 0 $right [1] Inf class [1] "censReg" "maxLik" "maxim" "list" print( x, digits = 2 ) Call: censReg(formula = affairsFormula, data = Affairs, method = "BHHH") Coefficients: (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating -0.04 0.13 -0.39 0.08 -0.53 printME( margEff( x ) ) age yearsmarried religiousness occupation rating -0.042 0.129 -0.394 0.076 -0.534 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0.000 0.000 0.000 0.000 yearsmarried 0 0.001 0.000 0.000 0.000 religiousness 0 0.000 0.009 0.001 0.001 occupation 0 0.000 0.001 0.004 -0.001 rating 0 0.000 0.001 -0.001 0.011 attr(,"jacobian") (Intercept) age yearsmarried religiousness occupation rating age -0.007 0.017 -0.054 -0.021 -0.028 -0.026 yearsmarried 0.021 0.669 0.402 0.064 0.086 0.081 religiousness -0.063 -2.035 -0.512 0.039 -0.263 -0.246 occupation 0.012 0.394 0.099 0.038 0.285 0.048 rating -0.085 -2.758 -0.694 -0.265 -0.356 -0.100 sigma age -0.005 yearsmarried 0.015 religiousness -0.046 occupation 0.009 rating -0.062 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.042 0.018 -2.4 0.02 * yearsmarried 0.129 0.033 4.0 8e-05 *** religiousness -0.394 0.095 -4.2 4e-05 *** occupation 0.076 0.062 1.2 0.22 rating -0.534 0.103 -5.2 3e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation BHHH maximisation, 0 iterations Return code 0: removed message Log-Likelihood: -705.58 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 8.170 2.608 3.1 0.002 ** age -0.179 0.076 -2.4 0.018 * yearsmarried 0.554 0.141 3.9 8e-05 *** religiousness -1.686 0.414 -4.1 5e-05 *** occupation 0.326 0.265 1.2 0.218 rating -2.284 0.444 -5.1 3e-07 *** logSigma 2.110 0.087 24.1 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsFormula, data = Affairs, method = "BHHH") Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 8.170 2.608 3.1 0.002 ** age -0.179 0.076 -2.4 0.018 * yearsmarried 0.554 0.141 3.9 8e-05 *** religiousness -1.686 0.414 -4.1 5e-05 *** occupation 0.326 0.265 1.2 0.218 rating -2.284 0.444 -5.1 3e-07 *** logSigma 2.110 0.087 24.1 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 BHHH maximisation, 0 iterations Return code 0: removed message Log-likelihood: -705.58 on 7 Df > all.equal( -crossprod( estfun( estResultBhhh ) ), + hessian( estResultBhhh ), check.attributes = FALSE ) [1] TRUE > all.equal( sandwich( estResultBhhh ), vcov( estResultBhhh ) ) [1] TRUE > > ## usual tobit estimation, BFGS method > estResultBfgs <- censReg( affairsFormula, data = Affairs, method = "BFGS" ) > printAll( estResultBfgs, meCalcVCov = FALSE ) $maximum [1] -705.58 $estimate (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 $gradient (Intercept) age yearsmarried religiousness occupation 0.000 -0.001 0.000 0.000 0.000 rating logSigma 0.000 0.000 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.0 -165.5 -44.9 -14.8 -21.3 -18.5 age -165.5 -5890.2 -1675.4 -500.7 -717.1 -602.6 yearsmarried -44.9 -1675.4 -550.7 -140.7 -193.4 -159.0 religiousness -14.8 -500.7 -140.7 -50.8 -62.7 -54.8 occupation -21.3 -717.1 -193.4 -62.7 -106.7 -78.9 rating -18.5 -602.6 -159.0 -54.8 -78.9 -74.9 logSigma -37.1 -1206.3 -301.5 -116.1 -155.3 -148.5 logSigma (Intercept) -37.1 age -1206.3 yearsmarried -301.5 religiousness -116.1 occupation -155.3 rating -148.5 logSigma -530.5 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "BFGS maximization" $constraints NULL $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.06 -2.09 -0.56 -0.17 -0.40 -0.23 -0.27 [2,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [3,] -0.10 -3.17 -1.49 -0.10 -0.10 -0.40 0.02 [4,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [5,] -0.07 -1.44 -0.05 -0.13 -0.39 -0.20 -0.24 [6,] -0.03 -0.85 -0.04 -0.05 -0.13 -0.13 -0.26 [7,] -0.05 -1.18 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.06 -3.18 -0.84 -0.11 -0.22 -0.22 -0.28 [9,] -0.09 -3.02 -1.42 -0.38 -0.09 -0.19 -0.02 [10,] -0.02 -0.41 -0.03 -0.08 -0.08 -0.09 -0.22 [11,] -0.14 -5.15 -2.09 -0.28 -0.97 -0.28 0.58 [12,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [13,] -0.04 -1.82 -0.58 -0.19 -0.23 -0.16 -0.29 [14,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [15,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [16,] -0.08 -3.03 -1.23 -0.08 -0.41 -0.41 -0.13 [17,] -0.11 -3.91 -1.58 -0.21 -0.42 -0.32 0.10 [18,] -0.04 -0.81 -0.03 -0.11 -0.18 -0.15 -0.29 [19,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.05 -1.38 -0.51 -0.10 -0.05 -0.26 -0.29 [21,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [22,] -0.04 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-0.22 [391,] -0.02 -0.55 -0.14 -0.10 -0.08 -0.10 -0.23 [392,] -0.05 -2.14 -0.76 -0.20 -0.20 -0.20 -0.29 [393,] -0.08 -4.62 -1.22 -0.24 -0.41 -0.16 -0.14 [394,] -0.11 -4.58 -1.64 -0.33 -0.65 -0.22 0.14 [395,] -0.08 -2.72 -0.59 -0.17 -0.08 -0.17 -0.11 [396,] -0.02 -0.39 -0.07 -0.09 -0.07 -0.09 -0.21 [397,] -0.05 -1.05 -0.07 -0.05 -0.29 -0.24 -0.29 [398,] -0.04 -0.90 -0.03 -0.04 -0.16 -0.21 -0.29 [399,] -0.04 -1.33 -0.62 -0.17 -0.04 -0.21 -0.29 [400,] -0.07 -1.46 -0.10 -0.13 -0.33 -0.20 -0.23 [401,] -0.01 -0.35 -0.05 -0.06 -0.03 -0.06 -0.18 [402,] -0.02 -0.46 -0.07 -0.07 -0.02 -0.09 -0.21 [403,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [404,] -0.06 -1.84 -0.09 -0.12 -0.40 -0.17 -0.27 [405,] -0.08 -4.60 -1.21 -0.32 -0.24 -0.08 -0.14 [406,] -0.02 -0.79 -0.15 -0.09 -0.11 -0.11 -0.24 [407,] -0.06 -3.37 -0.97 -0.13 -0.32 -0.26 -0.24 [408,] -0.04 -1.66 -0.53 -0.14 -0.21 -0.18 -0.29 [409,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [410,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.27 [411,] -0.07 -1.59 -0.29 -0.14 -0.22 -0.22 -0.20 [412,] -0.04 -1.46 -0.28 -0.08 -0.24 -0.20 -0.29 [413,] -0.05 -1.48 -0.38 -0.22 -0.22 -0.16 -0.28 [414,] -0.04 -1.58 -0.38 -0.15 -0.23 -0.15 -0.29 [415,] -0.02 -0.47 -0.03 -0.06 -0.02 -0.11 -0.24 [416,] -0.07 -1.47 -0.27 -0.13 -0.07 -0.20 -0.23 [417,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [418,] -0.08 -2.78 -1.13 -0.30 -0.30 -0.23 -0.18 [419,] -0.04 -1.03 -0.27 -0.11 -0.19 -0.19 -0.29 [420,] -0.05 -0.81 -0.47 -0.19 -0.19 -0.23 -0.29 [421,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [422,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [423,] -0.16 -5.81 -2.36 -0.31 -0.79 -0.16 0.91 [424,] -0.02 -0.38 -0.03 -0.09 -0.02 -0.07 -0.21 [425,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [426,] -0.02 -0.59 -0.09 -0.09 -0.11 -0.11 -0.24 [427,] -0.04 -0.82 0.00 -0.04 -0.11 -0.19 -0.29 [428,] -0.03 -0.93 -0.24 -0.14 -0.03 -0.14 -0.29 [429,] -0.07 -2.28 -1.07 -0.36 -0.36 -0.21 -0.21 [430,] -0.05 -1.49 -0.47 -0.19 -0.23 -0.19 -0.29 [431,] -0.09 -2.86 -1.34 -0.18 -0.27 -0.36 -0.07 [432,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [433,] -0.03 -0.83 -0.12 -0.12 -0.12 -0.12 -0.28 [434,] -0.02 -0.88 -0.25 -0.08 -0.02 -0.08 -0.21 [435,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [436,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [437,] -0.07 -2.88 -1.03 -0.14 -0.07 -0.27 -0.22 [438,] -0.05 -2.23 -0.80 -0.21 -0.27 -0.21 -0.28 [439,] -0.05 -1.42 -0.37 -0.21 -0.16 -0.16 -0.28 [440,] -0.11 -2.93 -0.76 -0.22 -0.65 -0.22 0.13 [441,] -0.08 -3.32 -1.18 -0.24 -0.24 -0.24 -0.16 [442,] -0.03 -0.71 -0.11 -0.08 -0.08 -0.13 -0.26 [443,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [444,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [445,] -0.03 -0.70 -0.10 -0.10 -0.03 -0.10 -0.26 [446,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [447,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [448,] -0.05 -2.22 -0.71 -0.19 -0.24 -0.19 -0.29 [449,] -0.11 -3.97 -1.07 -0.21 -0.64 -0.21 0.12 [450,] -0.07 -2.69 -1.09 -0.22 -0.36 -0.29 -0.20 [451,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [452,] 0.17 4.66 0.26 0.52 0.69 0.69 1.02 [453,] 0.20 5.40 0.80 0.60 0.20 1.00 1.72 [454,] 0.12 4.46 1.81 0.60 0.72 0.24 -0.01 [455,] 0.18 5.66 1.77 0.53 0.88 0.35 1.12 [456,] 0.19 4.28 0.02 0.78 0.97 0.97 1.58 [457,] 0.15 3.37 0.23 0.31 0.15 0.77 0.59 [458,] 0.17 6.44 2.61 0.70 0.87 0.35 1.06 [459,] 0.20 4.36 0.30 0.40 0.59 0.79 1.67 [460,] 0.04 1.47 0.60 0.08 0.24 0.16 -0.89 [461,] 0.04 1.22 0.57 0.15 0.11 0.08 -0.90 [462,] 0.02 0.63 0.26 0.07 0.07 0.03 -0.98 [463,] 0.18 7.36 2.63 0.53 0.18 0.70 1.09 [464,] 0.18 7.69 2.75 0.92 0.73 0.18 1.28 [465,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [466,] 0.16 4.97 2.33 0.47 0.16 0.31 0.64 [467,] 0.13 3.42 0.51 0.13 0.76 0.63 0.09 [468,] 0.12 4.28 1.16 0.23 0.81 0.35 -0.09 [469,] 0.24 6.47 0.96 0.72 1.20 1.20 2.91 [470,] 0.13 5.30 1.89 0.50 0.63 0.63 0.08 [471,] 0.17 7.94 2.53 0.84 0.68 0.84 0.94 [472,] 0.21 5.57 0.82 0.62 1.03 0.82 1.89 [473,] 0.16 4.38 1.14 0.81 0.16 0.65 0.79 [474,] 0.30 8.10 0.45 0.90 1.50 1.20 5.12 [475,] 0.21 5.62 1.46 0.83 1.25 0.42 1.94 [476,] 0.12 5.13 1.83 0.49 0.61 0.49 0.01 [477,] 0.14 3.75 1.39 0.56 0.97 0.42 0.31 [478,] 0.05 1.25 0.07 0.09 0.23 0.09 -0.85 [479,] 0.15 4.83 0.60 0.60 0.91 0.60 0.55 [480,] 0.08 2.13 0.55 0.24 0.08 0.24 -0.58 [481,] 0.16 4.98 1.56 0.62 0.16 0.62 0.65 [482,] 0.05 1.24 0.18 0.09 0.32 0.09 -0.86 [483,] 0.21 3.63 0.16 1.04 0.83 1.04 1.92 [484,] 0.16 5.11 1.60 0.64 0.16 0.80 0.74 [485,] 0.17 5.29 1.16 0.33 0.99 0.66 0.86 [486,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [487,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [488,] 0.25 8.09 2.53 0.51 1.26 1.26 3.34 [489,] 0.15 7.95 2.29 0.31 0.92 0.61 0.59 [490,] 0.09 3.87 1.38 0.09 0.09 0.28 -0.42 [491,] 0.03 1.61 0.47 0.06 0.19 0.09 -0.93 [492,] 0.10 3.63 1.47 0.29 0.59 0.49 -0.34 [493,] 0.28 6.07 1.10 0.83 0.83 1.10 4.18 [494,] 0.13 3.61 0.94 0.13 0.80 0.27 0.21 [495,] 0.15 4.09 0.61 0.45 0.76 0.76 0.56 [496,] 0.30 13.93 4.45 1.19 1.78 1.48 4.97 [497,] 0.17 7.26 2.59 0.69 0.17 0.17 1.03 [498,] 0.22 5.82 0.86 0.65 0.65 0.86 2.16 [499,] 0.26 8.26 1.81 1.03 1.03 1.29 3.53 [500,] 0.17 5.44 0.07 0.51 0.51 0.68 0.96 [501,] 0.16 7.52 2.40 0.80 0.80 0.64 0.74 [502,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [503,] 0.16 3.59 0.65 0.33 0.98 0.65 0.81 [504,] 0.13 3.55 0.53 0.26 0.53 0.66 0.18 [505,] 0.22 11.31 3.26 1.09 0.22 0.65 2.22 [506,] 0.09 2.54 0.38 0.28 0.28 0.28 -0.40 [507,] 0.11 3.05 1.13 0.45 0.11 0.45 -0.13 [508,] 0.10 3.11 0.68 0.29 0.68 0.39 -0.36 [509,] 0.07 2.37 0.52 0.15 0.30 0.07 -0.63 [510,] 0.05 1.04 0.07 0.05 0.14 0.09 -0.85 [511,] 0.19 4.14 0.75 0.56 1.13 0.75 1.41 [512,] 0.18 7.39 2.64 0.70 1.06 0.70 1.11 [513,] 0.07 4.13 1.09 0.07 0.36 0.29 -0.64 [514,] 0.15 4.87 0.61 0.46 0.76 0.30 0.57 [515,] 0.07 1.97 0.29 0.07 0.29 0.29 -0.64 [516,] 0.24 7.64 1.67 0.96 0.24 0.96 2.88 [517,] 0.08 4.41 1.16 0.08 0.31 0.31 -0.59 [518,] 0.11 4.77 1.70 0.45 0.57 0.23 -0.12 [519,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [520,] -0.01 -0.35 -0.13 -0.03 -0.05 -0.01 -1.00 [521,] 0.10 5.15 1.48 0.30 0.40 0.40 -0.33 [522,] 0.07 2.01 0.52 0.22 0.37 0.22 -0.62 [523,] 0.18 5.80 1.27 0.36 0.72 0.36 1.23 [524,] 0.18 3.90 0.71 0.71 0.35 0.89 1.14 [525,] 0.12 3.19 0.83 0.35 0.71 0.47 -0.05 [526,] 0.20 7.41 3.01 0.20 1.00 1.00 1.73 [527,] 0.12 3.69 1.73 0.35 0.12 0.35 -0.10 [528,] 0.19 5.14 1.33 0.38 0.95 0.95 1.47 [529,] 0.07 2.34 0.51 0.22 0.37 0.22 -0.64 [530,] 0.14 4.51 0.21 0.28 0.28 0.56 0.35 [531,] 0.21 8.67 3.10 0.83 0.21 0.41 1.90 [532,] 0.17 5.45 1.70 0.51 0.85 0.68 0.98 [533,] 0.14 5.35 0.58 0.14 0.87 0.43 0.42 [534,] 0.06 1.61 0.24 0.12 0.30 0.18 -0.76 [535,] 0.20 8.43 3.01 0.60 0.80 0.60 1.74 [536,] 0.15 3.98 1.47 0.74 0.88 0.74 0.48 [537,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [538,] 0.18 4.90 1.27 0.18 0.54 0.54 1.24 [539,] 0.10 2.69 0.70 0.40 0.10 0.20 -0.32 [540,] 0.09 2.93 0.92 0.18 0.37 0.37 -0.43 [541,] 0.24 4.23 0.18 0.48 0.24 0.73 2.98 [542,] 0.20 6.50 3.05 0.61 1.02 0.81 1.81 [543,] 0.09 2.00 0.64 0.36 0.36 0.27 -0.44 [544,] 0.16 5.13 1.12 0.64 0.96 0.80 0.75 [545,] 0.11 2.95 0.44 0.22 0.66 0.22 -0.19 [546,] 0.14 3.10 0.21 0.71 0.71 0.42 0.35 [547,] 0.10 3.28 1.54 0.31 0.51 0.10 -0.29 [548,] 0.16 6.58 2.35 0.31 0.16 0.31 0.67 [549,] 0.16 6.55 2.34 0.47 0.78 0.62 0.66 [550,] 0.16 5.02 1.57 0.31 0.63 0.31 0.67 [551,] 0.12 3.89 1.82 0.36 0.12 0.12 0.01 [552,] 0.28 16.16 4.25 1.42 1.13 1.42 4.47 [553,] 0.26 12.35 3.94 1.05 1.58 1.05 3.70 [554,] 0.02 0.81 0.29 0.04 0.12 0.06 -0.97 [555,] 0.18 6.59 2.67 0.53 1.07 0.53 1.15 [556,] 0.20 7.35 2.98 0.99 0.99 0.40 1.69 [557,] 0.13 3.45 1.28 0.26 0.77 0.51 0.11 [558,] 0.04 1.65 0.67 0.09 0.22 0.18 -0.86 [559,] 0.09 2.77 1.30 0.09 0.43 0.17 -0.49 [560,] 0.13 4.23 1.32 0.40 0.79 0.40 0.19 [561,] -0.01 -0.25 -0.10 -0.03 -0.03 -0.01 -1.00 [562,] 0.24 6.35 0.35 0.47 1.18 1.18 2.77 [563,] 0.02 0.87 0.28 0.04 0.09 0.04 -0.98 [564,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [565,] 0.29 7.79 1.15 0.58 1.44 1.44 4.66 [566,] 0.16 4.35 1.61 0.65 0.16 0.81 0.77 [567,] 0.09 1.99 0.36 0.27 0.09 0.27 -0.45 [568,] 0.38 19.73 2.66 1.52 1.90 1.90 8.80 [569,] 0.13 3.41 0.50 0.13 0.38 0.63 0.08 [570,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [571,] -0.01 -0.22 -0.03 -0.01 -0.02 -0.01 -1.00 [572,] 0.30 5.24 0.22 0.60 0.90 1.50 5.10 [573,] 0.18 5.74 2.69 0.90 0.90 0.72 1.19 [574,] 0.19 4.10 0.75 0.19 0.56 0.93 1.37 [575,] 0.17 5.31 0.66 0.66 0.99 0.66 0.87 [576,] 0.06 1.27 0.09 0.17 0.29 0.12 -0.77 [577,] 0.07 3.04 1.09 0.14 0.36 0.29 -0.64 [578,] 0.14 4.36 0.95 0.54 0.54 0.54 0.26 [579,] 0.14 5.34 2.17 0.43 0.87 0.29 0.42 [580,] 0.03 1.24 0.44 0.09 0.18 0.09 -0.94 [581,] 0.07 1.84 0.27 0.07 0.34 0.27 -0.68 [582,] 0.05 1.89 0.76 0.20 0.36 0.15 -0.82 [583,] 0.14 5.11 2.07 0.41 0.83 0.55 0.30 [584,] 0.11 2.33 0.16 0.21 0.32 0.32 -0.24 [585,] 0.09 2.83 0.35 0.27 0.53 0.18 -0.47 [586,] 0.13 4.31 2.02 0.67 0.81 0.67 0.23 [587,] 0.24 12.48 3.60 0.24 1.20 1.20 2.92 [588,] 0.22 10.43 3.33 0.22 1.33 1.11 2.35 [589,] 0.10 3.21 1.51 0.40 0.40 0.40 -0.31 [590,] 0.07 2.31 1.08 0.22 0.22 0.14 -0.65 [591,] 0.16 4.28 1.11 0.63 0.16 0.32 0.71 [592,] 0.16 6.62 2.36 0.47 0.95 0.32 0.69 [593,] 0.07 3.09 1.10 0.15 0.22 0.15 -0.63 [594,] 0.23 6.22 1.61 0.46 1.15 0.92 2.61 [595,] 0.11 3.44 1.08 0.43 0.43 0.32 -0.21 [596,] 0.11 5.02 1.60 0.32 0.43 0.21 -0.22 [597,] 0.12 2.72 0.19 0.12 0.25 0.62 0.04 [598,] 0.15 4.66 1.46 0.29 0.73 0.58 0.44 [599,] 0.10 3.23 1.01 0.20 0.61 0.50 -0.31 [600,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 [601,] 0.09 3.02 1.41 0.28 0.09 0.47 -0.40 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 200 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsFormula, data = Affairs, method = "BFGS") $terms affairs ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairs 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairs age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 451 150 0 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation 5.608161 -0.050347 0.161852 -0.476324 0.106006 rating logSigma -0.712242 2.244542 $left [1] 0 $right [1] Inf class [1] "censReg" "maxLik" "maxim" print( x, digits = 2 ) Call: censReg(formula = affairsFormula, data = Affairs, method = "BFGS") Coefficients: (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating -0.04 0.13 -0.39 0.08 -0.53 printME( margEff( x ) ) age yearsmarried religiousness occupation rating -0.042 0.130 -0.394 0.076 -0.534 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.042 0.018 -2.3 0.02 * yearsmarried 0.130 0.031 4.2 4e-05 *** religiousness -0.394 0.093 -4.2 3e-05 *** occupation 0.076 0.059 1.3 0.20 rating -0.534 0.095 -5.6 3e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation BFGS maximization, 0 iterations Return code 0: removed message Log-Likelihood: -705.58 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 8.174 2.741 3.0 0.003 ** age -0.179 0.079 -2.3 0.023 * yearsmarried 0.554 0.135 4.1 4e-05 *** religiousness -1.686 0.404 -4.2 3e-05 *** occupation 0.326 0.254 1.3 0.200 rating -2.285 0.408 -5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsFormula, data = Affairs, method = "BFGS") Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 8.174 2.741 3.0 0.003 ** age -0.179 0.079 -2.3 0.023 * yearsmarried 0.554 0.135 4.1 4e-05 *** religiousness -1.686 0.404 -4.2 3e-05 *** occupation 0.326 0.254 1.3 0.200 rating -2.285 0.408 -5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 BFGS maximization, 0 iterations Return code 0: removed message Log-likelihood: -705.58 on 7 Df > > ## usual tobit estimation, NM method > estResultNm <- censReg( affairsFormula, data = Affairs, method = "NM" ) > printAll( estResultNm ) $maximum [1] -705.6 $estimate (Intercept) age yearsmarried religiousness occupation 8.56 -0.18 0.55 -1.73 0.30 rating logSigma -2.31 2.11 $gradient (Intercept) age yearsmarried religiousness occupation 0.144 7.155 2.521 0.731 1.005 rating logSigma 0.601 0.774 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.0 -164.2 -44.5 -14.7 -21.1 -18.3 age -164.2 -5841.4 -1661.1 -496.4 -711.1 -597.8 yearsmarried -44.5 -1661.1 -546.1 -139.5 -191.7 -157.7 religiousness -14.7 -496.4 -139.5 -50.4 -62.1 -54.3 occupation -21.1 -711.1 -191.7 -62.1 -105.8 -78.2 rating -18.3 -597.8 -157.7 -54.3 -78.2 -74.4 logSigma -37.2 -1214.5 -304.9 -117.0 -156.5 -149.0 logSigma (Intercept) -37.2 age -1214.5 yearsmarried -304.9 religiousness -117.0 occupation -156.5 rating -149.0 logSigma -532.9 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "Nelder-Mead maximization" $constraints NULL $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.06 -2.05 -0.55 -0.17 -0.39 -0.22 -0.28 [2,] -0.03 -0.91 -0.13 -0.13 -0.20 -0.13 -0.29 [3,] -0.10 -3.19 -1.50 -0.10 -0.10 -0.40 0.03 [4,] -0.02 -1.05 -0.28 -0.09 -0.11 -0.09 -0.22 [5,] -0.07 -1.44 -0.05 -0.13 -0.39 -0.20 -0.24 [6,] -0.03 -0.85 -0.04 -0.05 -0.13 -0.13 -0.26 [7,] -0.05 -1.20 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.05 -3.13 -0.82 -0.11 -0.22 -0.22 -0.28 [9,] -0.09 -3.02 -1.42 -0.38 -0.09 -0.19 -0.02 [10,] -0.02 -0.41 -0.03 -0.07 -0.07 -0.09 -0.22 [11,] -0.14 -5.11 -2.07 -0.28 -0.97 -0.28 0.57 [12,] -0.03 -0.91 -0.13 -0.13 -0.20 -0.13 -0.29 [13,] -0.04 -1.76 -0.56 -0.19 -0.22 -0.15 -0.29 [14,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [15,] -0.03 -0.86 -0.13 -0.13 -0.16 -0.13 -0.28 [16,] -0.08 -3.02 -1.22 -0.08 -0.41 -0.41 -0.14 [17,] -0.11 -3.89 -1.58 -0.21 -0.42 -0.32 0.10 [18,] -0.04 -0.80 -0.03 -0.11 -0.18 -0.15 -0.29 [19,] -0.04 -0.79 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.05 -1.39 -0.52 -0.10 -0.05 -0.26 -0.29 [21,] -0.04 -0.79 -0.05 -0.07 -0.18 -0.18 -0.29 [22,] -0.04 -0.79 -0.05 -0.07 -0.18 -0.18 -0.29 [23,] -0.05 -1.40 -0.52 -0.21 -0.26 -0.21 -0.28 [24,] -0.04 -1.13 -0.35 -0.11 -0.04 -0.18 -0.29 [25,] -0.04 -1.59 -0.17 -0.09 -0.26 -0.17 -0.29 [26,] -0.04 -0.79 -0.05 -0.07 -0.18 -0.18 -0.29 [27,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [28,] -0.04 -1.80 -0.64 -0.21 -0.26 -0.17 -0.29 [29,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [30,] -0.04 -1.13 -0.17 -0.13 -0.21 -0.17 -0.29 [31,] -0.07 -3.01 -1.07 -0.29 -0.43 -0.21 -0.20 [32,] -0.03 -0.59 -0.04 -0.08 -0.13 -0.13 -0.26 [33,] -0.02 -0.65 -0.01 -0.10 -0.14 -0.10 -0.25 [34,] -0.04 -1.72 -0.61 -0.20 -0.20 -0.16 -0.29 [35,] -0.06 -1.95 -0.24 -0.06 -0.37 -0.24 -0.26 [36,] -0.04 -0.93 -0.06 -0.17 -0.21 -0.13 -0.29 [37,] -0.06 -2.34 -0.83 -0.17 -0.06 -0.22 -0.28 [38,] -0.03 -0.56 -0.10 -0.10 -0.13 -0.13 -0.26 [39,] -0.04 -0.93 -0.06 -0.04 -0.13 -0.21 -0.29 [40,] -0.02 -0.44 -0.02 -0.06 -0.02 -0.10 -0.23 [41,] -0.03 -0.82 -0.26 -0.13 -0.15 -0.13 -0.26 [42,] -0.05 -2.38 -0.69 -0.23 -0.27 -0.14 -0.29 [43,] -0.02 -0.34 -0.01 -0.08 -0.02 -0.06 -0.20 [44,] -0.11 -3.10 -0.46 -0.23 -0.69 -0.11 0.22 [45,] -0.04 -1.26 -0.27 -0.20 -0.20 -0.12 -0.29 [46,] -0.03 -0.74 -0.14 -0.10 -0.17 -0.17 -0.29 [47,] -0.03 -0.82 -0.21 -0.12 -0.18 -0.15 -0.28 [48,] -0.08 -3.31 -1.18 -0.16 -0.39 -0.31 -0.16 [49,] -0.01 -0.39 -0.02 -0.06 -0.04 -0.07 -0.19 [50,] -0.08 -3.41 -1.22 -0.16 -0.49 -0.33 -0.14 [51,] -0.01 -0.25 -0.01 -0.06 -0.03 -0.06 -0.16 [52,] -0.06 -1.93 -0.42 -0.12 -0.36 -0.24 -0.26 [53,] -0.02 -0.44 -0.07 -0.08 -0.10 -0.08 -0.21 [54,] -0.05 -1.46 -0.54 -0.22 -0.32 -0.22 -0.28 [55,] -0.06 -1.22 -0.22 -0.06 -0.28 -0.28 -0.28 [56,] -0.11 -4.20 -1.70 -0.45 -0.34 -0.11 0.20 [57,] -0.02 -0.46 -0.03 -0.10 -0.08 -0.08 -0.23 [58,] -0.04 -1.33 -0.54 -0.14 -0.04 -0.18 -0.29 [59,] -0.02 -0.64 -0.02 -0.09 -0.12 -0.09 -0.25 [60,] -0.05 -1.53 -0.48 -0.19 -0.29 -0.19 -0.29 [61,] -0.07 -3.35 -1.07 -0.36 -0.50 -0.14 -0.21 [62,] -0.05 -1.97 -0.53 -0.16 -0.32 -0.21 -0.28 [63,] -0.03 -0.74 -0.03 -0.07 -0.17 -0.17 -0.28 [64,] -0.04 -0.99 -0.15 -0.07 -0.15 -0.18 -0.29 [65,] -0.04 -1.21 -0.26 -0.15 -0.23 -0.15 -0.29 [66,] -0.06 -2.35 -0.84 -0.11 -0.17 -0.28 -0.27 [67,] -0.04 -1.56 -0.42 -0.17 -0.25 -0.17 -0.29 [68,] -0.04 -2.08 -0.66 -0.13 -0.27 -0.22 -0.29 [69,] -0.01 -0.31 -0.02 -0.07 -0.07 -0.07 -0.19 [70,] -0.05 -1.24 -0.07 -0.09 -0.27 -0.18 -0.29 [71,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [72,] -0.02 -0.74 -0.23 -0.12 -0.09 -0.12 -0.25 [73,] -0.03 -0.70 0.00 -0.06 -0.16 -0.16 -0.28 [74,] -0.06 -2.81 -0.90 -0.24 -0.24 -0.18 -0.26 [75,] -0.09 -2.87 -1.35 -0.09 -0.45 -0.45 -0.07 [76,] -0.03 -0.78 -0.20 -0.12 -0.14 -0.14 -0.27 [77,] -0.03 -0.59 -0.04 -0.08 -0.13 -0.13 -0.26 [78,] -0.03 -0.83 -0.12 -0.09 -0.18 -0.15 -0.28 [79,] -0.03 -0.59 -0.04 -0.08 -0.13 -0.13 -0.26 [80,] -0.10 -5.75 -1.51 -0.20 -0.71 -0.20 0.05 [81,] -0.03 -0.57 -0.05 -0.10 -0.19 -0.16 -0.28 [82,] -0.03 -1.44 -0.38 -0.10 -0.15 -0.13 -0.26 [83,] -0.04 -0.95 -0.03 -0.09 -0.13 -0.17 -0.29 [84,] -0.03 -1.13 -0.11 -0.11 -0.08 -0.08 -0.26 [85,] -0.02 -0.34 -0.02 -0.06 -0.02 -0.08 -0.20 [86,] -0.06 -1.32 -0.02 -0.06 -0.36 -0.24 -0.26 [87,] -0.05 -1.56 -0.73 -0.20 -0.24 -0.24 -0.29 [88,] -0.06 -1.72 -0.10 -0.19 -0.32 -0.13 -0.24 [89,] -0.02 -0.48 -0.03 -0.07 -0.02 -0.11 -0.24 [90,] -0.06 -2.31 -0.94 -0.19 -0.06 -0.25 -0.25 [91,] -0.06 -1.95 -0.91 -0.24 -0.18 -0.24 -0.26 [92,] -0.08 -3.06 -0.83 -0.17 -0.41 -0.25 -0.13 [93,] -0.04 -1.49 -0.40 -0.16 -0.20 -0.16 -0.29 [94,] -0.04 -2.19 -0.58 -0.19 -0.19 -0.12 -0.29 [95,] -0.05 -1.27 -0.02 -0.05 -0.14 -0.19 -0.29 [96,] -0.02 -0.97 -0.35 -0.12 -0.02 -0.12 -0.25 [97,] -0.10 -5.90 -1.55 -0.31 -0.62 -0.10 0.08 [98,] -0.08 -2.97 -0.80 -0.08 -0.48 -0.32 -0.15 [99,] -0.05 -2.01 -0.82 -0.16 -0.27 -0.27 -0.28 [100,] -0.04 -1.66 -0.67 -0.18 -0.27 -0.22 -0.29 [101,] -0.02 -0.62 -0.23 -0.12 -0.02 -0.12 -0.25 [102,] -0.07 -2.45 -0.66 -0.13 -0.40 -0.26 -0.23 [103,] -0.02 -0.35 0.00 -0.06 -0.06 -0.08 -0.20 [104,] -0.02 -1.05 -0.28 -0.09 -0.11 -0.09 -0.22 [105,] -0.06 -2.25 -0.91 -0.24 -0.36 -0.24 -0.26 [106,] -0.04 -0.85 -0.15 -0.15 -0.23 -0.15 -0.29 [107,] -0.04 -1.11 -0.29 -0.17 -0.21 -0.17 -0.29 [108,] -0.04 -2.00 -0.53 -0.14 -0.18 -0.14 -0.29 [109,] -0.10 -3.30 -1.55 -0.31 -0.62 -0.31 0.07 [110,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [111,] -0.02 -0.63 -0.14 -0.08 -0.02 -0.10 -0.23 [112,] -0.04 -1.66 -0.67 -0.18 -0.27 -0.22 -0.29 [113,] -0.01 -0.30 -0.01 -0.05 -0.05 -0.05 -0.14 [114,] -0.03 -1.23 -0.29 -0.15 -0.20 -0.12 -0.27 [115,] -0.05 -1.42 -0.37 -0.16 -0.26 -0.21 -0.28 [116,] -0.04 -1.66 -0.67 -0.18 -0.27 -0.22 -0.29 [117,] -0.09 -3.39 -1.37 -0.37 -0.27 -0.18 -0.05 [118,] -0.04 -1.19 -0.37 -0.19 -0.22 -0.15 -0.29 [119,] -0.01 -0.31 -0.01 -0.06 -0.01 -0.07 -0.19 [120,] -0.04 -0.97 -0.25 -0.14 -0.07 -0.14 -0.29 [121,] -0.04 -1.15 -0.30 -0.09 -0.09 -0.21 -0.29 [122,] -0.04 -1.72 -0.61 -0.20 -0.20 -0.16 -0.29 [123,] -0.07 -2.91 -1.04 -0.28 -0.35 -0.21 -0.22 [124,] -0.09 -2.54 -0.66 -0.19 -0.09 -0.19 -0.03 [125,] -0.03 -0.59 -0.04 -0.08 -0.13 -0.13 -0.26 [126,] -0.03 -1.29 -0.52 -0.17 -0.21 -0.17 -0.29 [127,] -0.03 -0.66 0.00 -0.06 -0.12 -0.15 -0.28 [128,] -0.02 -0.45 -0.02 -0.07 -0.08 -0.08 -0.21 [129,] -0.03 -0.89 -0.04 -0.06 -0.17 -0.14 -0.27 [130,] -0.03 -0.88 -0.05 -0.06 -0.19 -0.16 -0.28 [131,] -0.06 -1.61 -0.60 -0.24 -0.06 -0.18 -0.26 [132,] -0.04 -1.65 -0.59 -0.16 -0.24 -0.20 -0.29 [133,] -0.03 -0.88 -0.05 -0.06 -0.19 -0.16 -0.28 [134,] -0.07 -1.96 -0.29 -0.15 -0.44 -0.22 -0.20 [135,] -0.08 -2.42 -0.76 -0.23 -0.38 -0.23 -0.18 [136,] -0.08 -2.55 -1.19 -0.24 -0.40 -0.32 -0.15 [137,] -0.04 -0.77 -0.03 -0.07 -0.21 -0.18 -0.29 [138,] -0.08 -2.82 -1.14 -0.15 -0.08 -0.30 -0.18 [139,] -0.02 -0.58 -0.09 -0.09 -0.11 -0.11 -0.24 [140,] -0.07 -1.77 -0.26 -0.07 -0.33 -0.26 -0.24 [141,] -0.07 -1.85 -0.69 -0.14 -0.07 -0.27 -0.22 [142,] -0.05 -1.76 -0.82 -0.27 -0.33 -0.22 -0.28 [143,] -0.04 -1.21 -0.31 -0.22 -0.22 -0.13 -0.29 [144,] -0.06 -3.32 -0.96 -0.13 -0.32 -0.26 -0.24 [145,] -0.06 -1.60 -0.24 -0.18 -0.36 -0.18 -0.26 [146,] -0.05 -1.93 -0.21 -0.05 -0.26 -0.21 -0.28 [147,] -0.03 -0.86 -0.13 -0.13 -0.16 -0.13 -0.28 [148,] -0.04 -1.91 -0.55 -0.18 -0.04 -0.11 -0.29 [149,] -0.04 -2.10 -0.55 -0.15 -0.22 -0.15 -0.29 [150,] -0.08 -2.14 -0.55 -0.08 -0.40 -0.32 -0.15 [151,] -0.05 -1.71 -0.32 -0.18 -0.28 -0.14 -0.29 [152,] -0.06 -1.34 -0.05 -0.12 -0.24 -0.18 -0.26 [153,] -0.06 -2.02 -0.25 -0.13 -0.32 -0.19 -0.25 [154,] -0.08 -2.93 -1.19 -0.32 -0.48 -0.24 -0.15 [155,] -0.06 -1.34 -0.05 -0.12 -0.24 -0.18 -0.26 [156,] -0.07 -3.01 -1.07 -0.29 -0.43 -0.21 -0.20 [157,] -0.07 -3.53 -1.02 -0.34 -0.07 -0.07 -0.23 [158,] -0.09 -3.18 -1.29 -0.34 -0.09 -0.17 -0.10 [159,] -0.06 -1.53 -0.40 -0.23 -0.28 -0.17 -0.27 [160,] -0.03 -1.06 -0.13 -0.07 -0.17 -0.17 -0.28 [161,] -0.04 -1.08 -0.16 -0.08 -0.24 -0.20 -0.29 [162,] -0.04 -1.03 -0.15 -0.08 -0.19 -0.19 -0.29 [163,] -0.03 -1.29 -0.52 -0.17 -0.21 -0.17 -0.29 [164,] -0.04 -1.68 -0.54 -0.18 -0.18 -0.14 -0.29 [165,] -0.05 -1.58 -0.49 -0.15 -0.05 -0.20 -0.29 [166,] -0.04 -1.17 -0.07 -0.17 -0.04 -0.09 -0.29 [167,] -0.10 -5.43 -1.43 -0.19 -0.48 -0.19 -0.01 [168,] -0.03 -0.65 -0.04 -0.12 -0.15 -0.12 -0.27 [169,] -0.06 -2.52 -0.90 -0.18 -0.18 -0.24 -0.26 [170,] -0.06 -3.35 -0.88 -0.24 -0.12 -0.12 -0.27 [171,] -0.03 -1.44 -0.38 -0.10 -0.15 -0.13 -0.26 [172,] -0.02 -0.35 0.00 -0.06 -0.06 -0.08 -0.20 [173,] -0.03 -0.85 -0.27 -0.11 -0.03 -0.13 -0.26 [174,] -0.06 -2.72 -0.97 -0.19 -0.32 -0.26 -0.24 [175,] -0.03 -0.88 -0.05 -0.06 -0.19 -0.16 -0.28 [176,] -0.06 -2.06 -0.01 -0.13 -0.32 -0.13 -0.24 [177,] -0.04 -1.13 -0.17 -0.13 -0.21 -0.17 -0.29 [178,] -0.07 -1.85 -0.69 -0.14 -0.07 -0.27 -0.22 [179,] -0.04 -1.35 -0.30 -0.17 -0.04 -0.13 -0.29 [180,] -0.06 -2.17 -0.88 -0.23 -0.29 -0.23 -0.27 [181,] -0.08 -3.21 -1.15 -0.38 -0.46 -0.15 -0.17 [182,] -0.01 -0.47 -0.02 -0.06 -0.09 -0.07 -0.19 [183,] -0.05 -1.62 -0.20 -0.15 -0.25 -0.15 -0.29 [184,] -0.02 -0.77 -0.15 -0.08 -0.10 -0.10 -0.24 [185,] -0.02 -0.48 -0.01 -0.07 -0.07 -0.11 -0.24 [186,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [187,] -0.02 -0.57 -0.02 -0.06 -0.11 -0.11 -0.24 [188,] -0.04 -1.03 -0.15 -0.08 -0.19 -0.19 -0.29 [189,] -0.03 -0.99 -0.31 -0.12 -0.12 -0.15 -0.28 [190,] -0.09 -2.87 -1.35 -0.09 -0.45 -0.45 -0.07 [191,] -0.02 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-0.09 -2.74 -0.60 -0.17 -0.09 -0.17 -0.10 [396,] -0.02 -0.38 -0.07 -0.09 -0.07 -0.09 -0.21 [397,] -0.05 -1.05 -0.07 -0.05 -0.29 -0.24 -0.29 [398,] -0.04 -0.91 -0.03 -0.04 -0.17 -0.21 -0.29 [399,] -0.04 -1.32 -0.62 -0.16 -0.04 -0.21 -0.29 [400,] -0.07 -1.46 -0.10 -0.13 -0.33 -0.20 -0.23 [401,] -0.01 -0.34 -0.05 -0.06 -0.03 -0.06 -0.18 [402,] -0.02 -0.46 -0.07 -0.07 -0.02 -0.09 -0.21 [403,] -0.04 -1.72 -0.61 -0.20 -0.20 -0.16 -0.29 [404,] -0.06 -1.83 -0.09 -0.11 -0.40 -0.17 -0.27 [405,] -0.08 -4.53 -1.19 -0.32 -0.24 -0.08 -0.15 [406,] -0.02 -0.77 -0.15 -0.08 -0.10 -0.10 -0.24 [407,] -0.06 -3.32 -0.96 -0.13 -0.32 -0.26 -0.24 [408,] -0.03 -1.61 -0.51 -0.14 -0.21 -0.17 -0.29 [409,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [410,] -0.03 -0.78 -0.20 -0.12 -0.14 -0.14 -0.27 [411,] -0.07 -1.60 -0.29 -0.15 -0.22 -0.22 -0.20 [412,] -0.04 -1.45 -0.27 -0.08 -0.23 -0.20 -0.29 [413,] -0.05 -1.47 -0.38 -0.22 -0.22 -0.16 -0.28 [414,] -0.04 -1.54 -0.37 -0.15 -0.22 -0.15 -0.29 [415,] -0.02 -0.48 -0.03 -0.07 -0.02 -0.11 -0.24 [416,] -0.07 -1.49 -0.27 -0.14 -0.07 -0.20 -0.23 [417,] -0.03 -1.44 -0.38 -0.10 -0.15 -0.13 -0.26 [418,] -0.07 -2.74 -1.11 -0.30 -0.30 -0.22 -0.19 [419,] -0.04 -1.02 -0.27 -0.11 -0.19 -0.19 -0.29 [420,] -0.05 -0.81 -0.46 -0.18 -0.18 -0.23 -0.29 [421,] -0.03 -0.56 -0.10 -0.10 -0.13 -0.13 -0.26 [422,] -0.04 -1.21 -0.18 -0.09 -0.04 -0.18 -0.29 [423,] -0.16 -5.79 -2.35 -0.31 -0.78 -0.16 0.91 [424,] -0.02 -0.38 -0.03 -0.09 -0.02 -0.07 -0.21 [425,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [426,] -0.02 -0.58 -0.09 -0.09 -0.11 -0.11 -0.24 [427,] -0.04 -0.83 0.00 -0.04 -0.11 -0.19 -0.29 [428,] -0.03 -0.93 -0.24 -0.14 -0.03 -0.14 -0.29 [429,] -0.07 -2.24 -1.05 -0.35 -0.35 -0.21 -0.21 [430,] -0.05 -1.47 -0.46 -0.18 -0.23 -0.18 -0.29 [431,] -0.09 -2.86 -1.34 -0.18 -0.27 -0.36 -0.07 [432,] -0.03 -0.59 -0.04 -0.08 -0.13 -0.13 -0.26 [433,] -0.03 -0.82 -0.12 -0.12 -0.12 -0.12 -0.28 [434,] -0.02 -0.85 -0.24 -0.08 -0.02 -0.08 -0.20 [435,] -0.09 -2.54 -0.66 -0.19 -0.09 -0.19 -0.03 [436,] -0.04 -1.20 -0.31 -0.13 -0.04 -0.18 -0.29 [437,] -0.07 -2.88 -1.03 -0.14 -0.07 -0.27 -0.22 [438,] -0.05 -2.19 -0.78 -0.21 -0.26 -0.21 -0.28 [439,] -0.05 -1.41 -0.37 -0.21 -0.16 -0.16 -0.28 [440,] -0.11 -2.93 -0.76 -0.22 -0.65 -0.22 0.13 [441,] -0.08 -3.29 -1.18 -0.24 -0.24 -0.24 -0.16 [442,] -0.03 -0.71 -0.11 -0.08 -0.08 -0.13 -0.26 [443,] -0.04 -1.20 -0.31 -0.13 -0.04 -0.18 -0.29 [444,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [445,] -0.03 -0.71 -0.10 -0.10 -0.03 -0.10 -0.26 [446,] -0.03 -0.56 -0.10 -0.10 -0.13 -0.13 -0.26 [447,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [448,] -0.05 -2.16 -0.69 -0.18 -0.23 -0.18 -0.29 [449,] -0.11 -3.95 -1.07 -0.21 -0.64 -0.21 0.11 [450,] -0.07 -2.66 -1.08 -0.22 -0.36 -0.29 -0.20 [451,] -0.04 -1.21 -0.18 -0.09 -0.04 -0.18 -0.29 [452,] 0.17 4.64 0.26 0.52 0.69 0.69 1.02 [453,] 0.20 5.37 0.79 0.60 0.20 0.99 1.70 [454,] 0.12 4.52 1.83 0.61 0.73 0.24 0.02 [455,] 0.18 5.64 1.76 0.53 0.88 0.35 1.12 [456,] 0.19 4.29 0.02 0.78 0.97 0.97 1.60 [457,] 0.15 3.32 0.23 0.30 0.15 0.75 0.56 [458,] 0.17 6.46 2.62 0.70 0.87 0.35 1.08 [459,] 0.20 4.31 0.29 0.39 0.59 0.78 1.63 [460,] 0.04 1.50 0.61 0.08 0.24 0.16 -0.89 [461,] 0.04 1.23 0.58 0.15 0.12 0.08 -0.90 [462,] 0.02 0.67 0.27 0.07 0.07 0.04 -0.98 [463,] 0.17 7.34 2.62 0.52 0.17 0.70 1.09 [464,] 0.18 7.73 2.76 0.92 0.74 0.18 1.31 [465,] 0.16 5.92 1.60 0.32 0.96 0.32 0.75 [466,] 0.15 4.92 2.30 0.46 0.15 0.31 0.61 [467,] 0.13 3.40 0.50 0.13 0.76 0.63 0.08 [468,] 0.12 4.29 1.16 0.23 0.81 0.35 -0.08 [469,] 0.24 6.46 0.96 0.72 1.20 1.20 2.92 [470,] 0.13 5.38 1.92 0.51 0.64 0.64 0.12 [471,] 0.17 8.05 2.57 0.86 0.69 0.86 1.01 [472,] 0.21 5.55 0.82 0.62 1.03 0.82 1.89 [473,] 0.16 4.38 1.13 0.81 0.16 0.65 0.80 [474,] 0.30 8.07 0.45 0.90 1.50 1.20 5.11 [475,] 0.21 5.61 1.46 0.83 1.25 0.42 1.96 [476,] 0.12 5.20 1.86 0.49 0.62 0.49 0.05 [477,] 0.14 3.77 1.40 0.56 0.98 0.42 0.34 [478,] 0.05 1.23 0.07 0.09 0.23 0.09 -0.86 [479,] 0.15 4.87 0.61 0.61 0.91 0.61 0.58 [480,] 0.08 2.10 0.54 0.23 0.08 0.23 -0.59 [481,] 0.16 4.97 1.55 0.62 0.16 0.62 0.65 [482,] 0.05 1.23 0.18 0.09 0.32 0.09 -0.86 [483,] 0.21 3.63 0.16 1.04 0.83 1.04 1.94 [484,] 0.16 5.11 1.60 0.64 0.16 0.80 0.74 [485,] 0.17 5.28 1.16 0.33 0.99 0.66 0.86 [486,] 0.11 4.21 1.71 0.23 0.68 0.46 -0.12 [487,] 0.10 3.69 1.00 0.10 0.50 0.30 -0.32 [488,] 0.25 8.07 2.52 0.50 1.26 1.26 3.35 [489,] 0.15 8.01 2.31 0.31 0.92 0.62 0.62 [490,] 0.09 3.80 1.36 0.09 0.09 0.27 -0.44 [491,] 0.03 1.69 0.49 0.07 0.20 0.10 -0.93 [492,] 0.10 3.69 1.49 0.30 0.60 0.50 -0.32 [493,] 0.27 6.04 1.10 0.82 0.82 1.10 4.14 [494,] 0.13 3.57 0.92 0.13 0.79 0.26 0.19 [495,] 0.15 4.09 0.61 0.45 0.76 0.76 0.57 [496,] 0.30 14.01 4.47 1.19 1.79 1.49 5.07 [497,] 0.17 7.23 2.58 0.69 0.17 0.17 1.02 [498,] 0.21 5.79 0.86 0.64 0.64 0.86 2.15 [499,] 0.26 8.27 1.81 1.03 1.03 1.29 3.56 [500,] 0.17 5.42 0.07 0.51 0.51 0.68 0.96 [501,] 0.16 7.63 2.44 0.81 0.81 0.65 0.80 [502,] 0.19 7.08 2.87 0.38 0.96 0.77 1.50 [503,] 0.16 3.58 0.65 0.33 0.98 0.65 0.81 [504,] 0.13 3.53 0.52 0.26 0.52 0.65 0.17 [505,] 0.22 11.36 3.28 1.09 0.22 0.66 2.26 [506,] 0.09 2.51 0.37 0.28 0.28 0.28 -0.41 [507,] 0.11 3.04 1.12 0.45 0.11 0.45 -0.13 [508,] 0.10 3.14 0.69 0.29 0.69 0.39 -0.34 [509,] 0.07 2.33 0.51 0.15 0.29 0.07 -0.64 [510,] 0.04 0.99 0.07 0.04 0.13 0.09 -0.86 [511,] 0.19 4.13 0.75 0.56 1.13 0.75 1.41 [512,] 0.18 7.47 2.67 0.71 1.07 0.71 1.16 [513,] 0.07 4.19 1.10 0.07 0.37 0.29 -0.63 [514,] 0.15 4.85 0.61 0.45 0.76 0.30 0.57 [515,] 0.07 1.94 0.29 0.07 0.29 0.29 -0.65 [516,] 0.24 7.61 1.67 0.95 0.24 0.95 2.87 [517,] 0.08 4.44 1.17 0.08 0.31 0.31 -0.59 [518,] 0.11 4.82 1.72 0.46 0.57 0.23 -0.10 [519,] 0.10 3.69 1.00 0.10 0.50 0.30 -0.32 [520,] -0.01 -0.30 -0.11 -0.02 -0.04 -0.01 -1.00 [521,] 0.10 5.22 1.51 0.30 0.40 0.40 -0.31 [522,] 0.07 2.01 0.52 0.22 0.37 0.22 -0.62 [523,] 0.18 5.75 1.26 0.36 0.72 0.36 1.21 [524,] 0.18 3.89 0.71 0.71 0.35 0.88 1.14 [525,] 0.12 3.20 0.83 0.36 0.71 0.47 -0.04 [526,] 0.20 7.39 3.00 0.20 1.00 1.00 1.73 [527,] 0.11 3.66 1.71 0.34 0.11 0.34 -0.11 [528,] 0.19 5.13 1.33 0.38 0.95 0.95 1.46 [529,] 0.07 2.35 0.51 0.22 0.37 0.22 -0.63 [530,] 0.14 4.47 0.21 0.28 0.28 0.56 0.33 [531,] 0.21 8.64 3.09 0.82 0.21 0.41 1.89 [532,] 0.17 5.46 1.71 0.51 0.85 0.68 0.99 [533,] 0.14 5.32 0.58 0.14 0.86 0.43 0.41 [534,] 0.06 1.59 0.24 0.12 0.29 0.18 -0.76 [535,] 0.20 8.43 3.01 0.60 0.80 0.60 1.76 [536,] 0.15 4.03 1.49 0.75 0.90 0.75 0.52 [537,] 0.16 5.92 1.60 0.32 0.96 0.32 0.75 [538,] 0.18 4.84 1.25 0.18 0.54 0.54 1.19 [539,] 0.10 2.66 0.69 0.39 0.10 0.20 -0.34 [540,] 0.09 2.92 0.91 0.18 0.36 0.36 -0.43 [541,] 0.24 4.17 0.18 0.48 0.24 0.72 2.89 [542,] 0.20 6.50 3.05 0.61 1.02 0.81 1.82 [543,] 0.09 2.00 0.64 0.36 0.36 0.27 -0.44 [544,] 0.16 5.17 1.13 0.65 0.97 0.81 0.79 [545,] 0.11 2.93 0.43 0.22 0.65 0.22 -0.20 [546,] 0.14 3.12 0.21 0.71 0.71 0.42 0.37 [547,] 0.10 3.27 1.53 0.31 0.51 0.10 -0.29 [548,] 0.16 6.52 2.33 0.31 0.16 0.31 0.65 [549,] 0.16 6.59 2.35 0.47 0.78 0.63 0.68 [550,] 0.16 4.97 1.55 0.31 0.62 0.31 0.65 [551,] 0.12 3.84 1.80 0.36 0.12 0.12 -0.02 [552,] 0.29 16.30 4.29 1.43 1.14 1.43 4.59 [553,] 0.26 12.42 3.96 1.06 1.59 1.06 3.78 [554,] 0.02 0.85 0.30 0.04 0.12 0.06 -0.97 [555,] 0.18 6.60 2.68 0.54 1.07 0.54 1.18 [556,] 0.20 7.39 3.00 1.00 1.00 0.40 1.73 [557,] 0.13 3.44 1.27 0.25 0.76 0.51 0.11 [558,] 0.05 1.67 0.68 0.09 0.23 0.18 -0.86 [559,] 0.09 2.73 1.28 0.09 0.43 0.17 -0.50 [560,] 0.13 4.24 1.32 0.40 0.79 0.40 0.20 [561,] -0.01 -0.20 -0.08 -0.02 -0.03 -0.01 -1.00 [562,] 0.23 6.33 0.35 0.47 1.17 1.17 2.75 [563,] 0.02 0.90 0.29 0.04 0.10 0.04 -0.98 [564,] 0.19 7.08 2.87 0.38 0.96 0.77 1.50 [565,] 0.29 7.76 1.15 0.57 1.44 1.44 4.64 [566,] 0.16 4.34 1.61 0.64 0.16 0.80 0.77 [567,] 0.09 1.95 0.35 0.27 0.09 0.27 -0.46 [568,] 0.38 19.79 2.66 1.52 1.90 1.90 8.90 [569,] 0.12 3.36 0.50 0.12 0.37 0.62 0.06 [570,] 0.11 4.21 1.71 0.23 0.68 0.46 -0.12 [571,] -0.01 -0.28 -0.04 -0.01 -0.03 -0.01 -0.99 [572,] 0.30 5.20 0.22 0.59 0.89 1.49 5.03 [573,] 0.18 5.78 2.71 0.90 0.90 0.72 1.23 [574,] 0.18 4.06 0.74 0.18 0.55 0.92 1.32 [575,] 0.17 5.34 0.67 0.67 1.00 0.67 0.90 [576,] 0.06 1.26 0.09 0.17 0.29 0.11 -0.78 [577,] 0.07 3.07 1.10 0.15 0.37 0.29 -0.63 [578,] 0.14 4.38 0.96 0.55 0.55 0.55 0.28 [579,] 0.14 5.36 2.17 0.43 0.87 0.29 0.43 [580,] 0.03 1.30 0.46 0.09 0.19 0.09 -0.93 [581,] 0.07 1.82 0.27 0.07 0.34 0.27 -0.69 [582,] 0.05 1.96 0.80 0.21 0.37 0.16 -0.81 [583,] 0.14 5.15 2.09 0.42 0.83 0.56 0.32 [584,] 0.10 2.29 0.16 0.21 0.31 0.31 -0.26 [585,] 0.09 2.84 0.35 0.27 0.53 0.18 -0.46 [586,] 0.14 4.38 2.05 0.68 0.82 0.68 0.28 [587,] 0.24 12.48 3.60 0.24 1.20 1.20 2.94 [588,] 0.22 10.44 3.33 0.22 1.33 1.11 2.37 [589,] 0.10 3.24 1.52 0.41 0.41 0.41 -0.30 [590,] 0.07 2.29 1.07 0.21 0.21 0.14 -0.65 [591,] 0.16 4.24 1.10 0.63 0.16 0.31 0.69 [592,] 0.16 6.64 2.37 0.47 0.95 0.32 0.71 [593,] 0.07 3.07 1.10 0.15 0.22 0.15 -0.63 [594,] 0.23 6.19 1.60 0.46 1.15 0.92 2.59 [595,] 0.11 3.46 1.08 0.43 0.43 0.32 -0.20 [596,] 0.11 5.05 1.61 0.32 0.43 0.21 -0.21 [597,] 0.12 2.67 0.18 0.12 0.24 0.61 0.00 [598,] 0.15 4.65 1.45 0.29 0.73 0.58 0.44 [599,] 0.10 3.24 1.01 0.20 0.61 0.51 -0.30 [600,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 [601,] 0.09 3.00 1.41 0.28 0.09 0.47 -0.40 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 500 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsFormula, data = Affairs, method = "NM") $terms affairs ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairs 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairs age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 451 150 0 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation 5.608161 -0.050347 0.161852 -0.476324 0.106006 rating logSigma -0.712242 2.244542 $left [1] 0 $right [1] Inf class [1] "censReg" "maxLik" "maxim" print( x, digits = 2 ) Call: censReg(formula = affairsFormula, data = Affairs, method = "NM") Coefficients: (Intercept) age yearsmarried religiousness occupation 8.56 -0.18 0.55 -1.73 0.30 rating logSigma -2.31 2.11 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating -0.04 0.13 -0.40 0.07 -0.54 printME( margEff( x ) ) age yearsmarried religiousness occupation rating -0.043 0.128 -0.402 0.071 -0.536 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0.000 0.000 0.000 0.000 yearsmarried 0 0.001 0.000 0.000 0.000 religiousness 0 0.000 0.009 0.000 0.000 occupation 0 0.000 0.000 0.004 0.000 rating 0 0.000 0.000 0.000 0.009 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.043 0.018 -2.3 0.02 * yearsmarried 0.128 0.031 4.1 4e-05 *** religiousness -0.402 0.093 -4.3 2e-05 *** occupation 0.071 0.059 1.2 0.23 rating -0.536 0.095 -5.7 2e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation Nelder-Mead maximization, 0 iterations Return code 0: removed message Log-Likelihood: -705.6 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 8.560 2.756 3.1 0.002 ** age -0.183 0.080 -2.3 0.021 * yearsmarried 0.553 0.135 4.1 4e-05 *** religiousness -1.729 0.406 -4.3 2e-05 *** occupation 0.305 0.255 1.2 0.232 rating -2.305 0.410 -5.6 2e-08 *** logSigma 2.112 0.067 31.3 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsFormula, data = Affairs, method = "NM") Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 8.560 2.756 3.1 0.002 ** age -0.183 0.080 -2.3 0.021 * yearsmarried 0.553 0.135 4.1 4e-05 *** religiousness -1.729 0.406 -4.3 2e-05 *** occupation 0.305 0.255 1.2 0.232 rating -2.305 0.410 -5.6 2e-08 *** logSigma 2.112 0.067 31.3 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Nelder-Mead maximization, 0 iterations Return code 0: removed message Log-likelihood: -705.6 on 7 Df > > ## usual tobit estimation, SANN method > estResultSann <- censReg( affairsFormula, data = Affairs, method = "SANN" ) > printAll( estResultSann ) $maximum [1] -706.96 $estimate (Intercept) age yearsmarried religiousness occupation 4.92 -0.18 0.57 -1.60 0.55 rating logSigma -2.01 2.17 $gradient (Intercept) age yearsmarried religiousness occupation 1.144 33.096 9.296 2.486 1.743 rating logSigma 2.050 -6.086 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -4.4 -145.3 -39.4 -13.0 -18.8 -16.2 age -145.3 -5179.1 -1473.2 -440.8 -634.2 -531.0 yearsmarried -39.4 -1473.2 -484.1 -123.9 -171.1 -140.1 religiousness -13.0 -440.8 -123.9 -44.8 -55.5 -48.3 occupation -18.8 -634.2 -171.1 -55.5 -94.5 -69.9 rating -16.2 -531.0 -140.1 -48.3 -69.9 -66.1 logSigma -37.1 -1199.6 -302.1 -114.1 -150.2 -143.7 logSigma (Intercept) -37.1 age -1199.6 yearsmarried -302.1 religiousness -114.1 occupation -150.2 rating -143.7 logSigma -527.8 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "SANN maximization" $constraints NULL $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.06 -2.04 -0.55 -0.17 -0.39 -0.22 -0.27 [2,] -0.03 -0.90 -0.13 -0.13 -0.20 -0.13 -0.29 [3,] -0.08 -2.61 -1.22 -0.08 -0.08 -0.33 -0.10 [4,] -0.02 -1.27 -0.33 -0.11 -0.13 -0.11 -0.25 [5,] -0.06 -1.27 -0.04 -0.12 -0.35 -0.17 -0.26 [6,] -0.03 -0.82 -0.04 -0.05 -0.13 -0.13 -0.27 [7,] -0.04 -0.90 -0.03 -0.08 -0.04 -0.12 -0.29 [8,] -0.05 -2.89 -0.76 -0.10 -0.20 -0.20 -0.28 [9,] -0.08 -2.41 -1.13 -0.30 -0.08 -0.15 -0.15 [10,] -0.02 -0.40 -0.03 -0.07 -0.07 -0.09 -0.23 [11,] -0.12 -4.56 -1.85 -0.25 -0.86 -0.25 0.45 [12,] -0.03 -0.90 -0.13 -0.13 -0.20 -0.13 -0.29 [13,] -0.04 -1.85 -0.59 -0.20 -0.24 -0.16 -0.29 [14,] -0.04 -0.98 -0.07 -0.09 -0.22 -0.18 -0.29 [15,] -0.03 -0.83 -0.12 -0.12 -0.15 -0.12 -0.28 [16,] -0.08 -2.81 -1.14 -0.08 -0.38 -0.38 -0.14 [17,] -0.09 -3.37 -1.36 -0.18 -0.36 -0.27 0.00 [18,] -0.03 -0.74 -0.03 -0.10 -0.17 -0.13 -0.29 [19,] -0.03 -0.74 -0.05 -0.07 -0.17 -0.17 -0.29 [20,] -0.04 -1.17 -0.43 -0.09 -0.04 -0.22 -0.29 [21,] -0.03 -0.74 -0.05 -0.07 -0.17 -0.17 -0.29 [22,] -0.03 -0.74 -0.05 -0.07 -0.17 -0.17 -0.29 [23,] -0.05 -1.33 -0.49 -0.20 -0.25 -0.20 -0.28 [24,] -0.03 -0.98 -0.30 -0.09 -0.03 -0.15 -0.28 [25,] -0.04 -1.51 -0.16 -0.08 -0.24 -0.16 -0.29 [26,] -0.03 -0.74 -0.05 -0.07 -0.17 -0.17 -0.29 [27,] -0.02 -0.55 -0.14 -0.08 -0.02 -0.10 -0.24 [28,] -0.04 -1.86 -0.66 -0.22 -0.27 -0.18 -0.29 [29,] -0.03 -0.78 -0.12 -0.09 -0.14 -0.14 -0.28 [30,] -0.04 -1.04 -0.15 -0.12 -0.19 -0.15 -0.29 [31,] -0.07 -2.85 -1.02 -0.27 -0.41 -0.20 -0.20 [32,] -0.03 -0.58 -0.04 -0.08 -0.13 -0.13 -0.27 [33,] -0.02 -0.65 -0.01 -0.10 -0.14 -0.10 -0.26 [34,] -0.04 -1.73 -0.62 -0.21 -0.21 -0.16 -0.29 [35,] -0.06 -1.78 -0.22 -0.06 -0.33 -0.22 -0.26 [36,] -0.04 -0.83 -0.06 -0.15 -0.19 -0.11 -0.29 [37,] -0.05 -1.97 -0.71 -0.14 -0.05 -0.19 -0.29 [38,] -0.03 -0.56 -0.10 -0.10 -0.13 -0.13 -0.27 [39,] -0.04 -0.79 -0.05 -0.04 -0.11 -0.18 -0.29 [40,] -0.02 -0.37 -0.01 -0.05 -0.02 -0.08 -0.22 [41,] -0.03 -0.91 -0.28 -0.14 -0.17 -0.14 -0.28 [42,] -0.05 -2.38 -0.69 -0.23 -0.27 -0.14 -0.29 [43,] -0.01 -0.28 -0.01 -0.06 -0.01 -0.05 -0.18 [44,] -0.10 -2.58 -0.38 -0.19 -0.57 -0.10 0.05 [45,] -0.04 -1.18 -0.26 -0.18 -0.18 -0.11 -0.29 [46,] -0.03 -0.72 -0.13 -0.10 -0.16 -0.16 -0.29 [47,] -0.03 -0.86 -0.22 -0.13 -0.19 -0.16 -0.29 [48,] -0.07 -3.05 -1.09 -0.15 -0.36 -0.29 -0.17 [49,] -0.01 -0.38 -0.02 -0.06 -0.04 -0.07 -0.19 [50,] -0.08 -3.22 -1.15 -0.15 -0.46 -0.31 -0.14 [51,] -0.01 -0.25 -0.01 -0.06 -0.03 -0.06 -0.17 [52,] -0.06 -1.80 -0.39 -0.11 -0.34 -0.23 -0.26 [53,] -0.02 -0.50 -0.07 -0.09 -0.11 -0.09 -0.23 [54,] -0.05 -1.42 -0.53 -0.21 -0.32 -0.21 -0.27 [55,] -0.05 -1.11 -0.20 -0.05 -0.25 -0.25 -0.28 [56,] -0.09 -3.45 -1.40 -0.37 -0.28 -0.09 0.03 [57,] -0.02 -0.43 -0.03 -0.10 -0.08 -0.08 -0.24 [58,] -0.03 -1.20 -0.48 -0.13 -0.03 -0.16 -0.29 [59,] -0.02 -0.61 -0.02 -0.09 -0.11 -0.09 -0.25 [60,] -0.05 -1.51 -0.47 -0.19 -0.28 -0.19 -0.29 [61,] -0.07 -3.22 -1.03 -0.34 -0.48 -0.14 -0.20 [62,] -0.05 -1.91 -0.52 -0.15 -0.31 -0.21 -0.28 [63,] -0.03 -0.69 -0.02 -0.06 -0.16 -0.16 -0.28 [64,] -0.03 -0.91 -0.13 -0.07 -0.13 -0.17 -0.29 [65,] -0.04 -1.20 -0.26 -0.15 -0.22 -0.15 -0.29 [66,] -0.05 -2.15 -0.77 -0.10 -0.15 -0.26 -0.28 [67,] -0.04 -1.56 -0.42 -0.17 -0.25 -0.17 -0.29 [68,] -0.05 -2.17 -0.69 -0.14 -0.28 -0.23 -0.29 [69,] -0.02 -0.33 -0.02 -0.08 -0.08 -0.08 -0.20 [70,] -0.04 -1.15 -0.06 -0.08 -0.25 -0.17 -0.29 [71,] -0.03 -0.78 -0.12 -0.09 -0.14 -0.14 -0.28 [72,] -0.02 -0.76 -0.24 -0.12 -0.10 -0.12 -0.26 [73,] -0.03 -0.66 0.00 -0.06 -0.15 -0.15 -0.28 [74,] -0.05 -2.55 -0.81 -0.22 -0.22 -0.16 -0.27 [75,] -0.08 -2.65 -1.24 -0.08 -0.41 -0.41 -0.08 [76,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.28 [77,] -0.03 -0.58 -0.04 -0.08 -0.13 -0.13 -0.27 [78,] -0.03 -0.84 -0.13 -0.09 -0.19 -0.16 -0.28 [79,] -0.03 -0.58 -0.04 -0.08 -0.13 -0.13 -0.27 [80,] -0.09 -5.25 -1.38 -0.18 -0.65 -0.18 0.01 [81,] -0.03 -0.57 -0.05 -0.10 -0.19 -0.16 -0.29 [82,] -0.03 -1.64 -0.43 -0.12 -0.17 -0.14 -0.28 [83,] -0.04 -0.79 -0.03 -0.07 -0.11 -0.14 -0.29 [84,] -0.02 -0.98 -0.09 -0.09 -0.07 -0.07 -0.26 [85,] -0.01 -0.30 -0.02 -0.05 -0.01 -0.07 -0.19 [86,] -0.05 -1.18 -0.02 -0.05 -0.32 -0.21 -0.27 [87,] -0.05 -1.57 -0.74 -0.20 -0.25 -0.25 -0.28 [88,] -0.05 -1.45 -0.08 -0.16 -0.27 -0.11 -0.27 [89,] -0.02 -0.40 -0.03 -0.05 -0.02 -0.09 -0.23 [90,] -0.05 -1.94 -0.79 -0.16 -0.05 -0.21 -0.28 [91,] -0.05 -1.75 -0.82 -0.22 -0.16 -0.22 -0.27 [92,] -0.07 -2.69 -0.73 -0.15 -0.36 -0.22 -0.17 [93,] -0.04 -1.45 -0.39 -0.16 -0.20 -0.16 -0.29 [94,] -0.04 -2.16 -0.57 -0.19 -0.19 -0.11 -0.29 [95,] -0.04 -1.05 -0.02 -0.04 -0.12 -0.16 -0.29 [96,] -0.02 -0.92 -0.33 -0.11 -0.02 -0.11 -0.25 [97,] -0.09 -5.19 -1.37 -0.27 -0.55 -0.09 0.00 [98,] -0.07 -2.73 -0.74 -0.07 -0.44 -0.29 -0.16 [99,] -0.05 -1.98 -0.80 -0.16 -0.27 -0.27 -0.27 [100,] -0.05 -1.74 -0.71 -0.19 -0.28 -0.24 -0.29 [101,] -0.02 -0.57 -0.21 -0.10 -0.02 -0.10 -0.24 [102,] -0.06 -2.30 -0.62 -0.12 -0.37 -0.25 -0.24 [103,] -0.02 -0.35 0.00 -0.06 -0.06 -0.08 -0.21 [104,] -0.02 -1.27 -0.33 -0.11 -0.13 -0.11 -0.25 [105,] -0.06 -2.22 -0.90 -0.24 -0.36 -0.24 -0.25 [106,] -0.04 -0.83 -0.15 -0.15 -0.23 -0.15 -0.29 [107,] -0.04 -1.06 -0.27 -0.16 -0.20 -0.16 -0.29 [108,] -0.04 -2.04 -0.54 -0.14 -0.18 -0.14 -0.29 [109,] -0.09 -3.01 -1.41 -0.28 -0.56 -0.28 0.04 [110,] -0.04 -0.98 -0.07 -0.09 -0.22 -0.18 -0.29 [111,] -0.02 -0.56 -0.12 -0.07 -0.02 -0.09 -0.22 [112,] -0.05 -1.74 -0.71 -0.19 -0.28 -0.24 -0.29 [113,] -0.01 -0.34 -0.02 -0.05 -0.05 -0.05 -0.16 [114,] -0.03 -1.35 -0.32 -0.16 -0.22 -0.13 -0.29 [115,] -0.05 -1.31 -0.34 -0.15 -0.24 -0.19 -0.29 [116,] -0.05 -1.74 -0.71 -0.19 -0.28 -0.24 -0.29 [117,] -0.08 -2.85 -1.16 -0.31 -0.23 -0.15 -0.13 [118,] -0.04 -1.22 -0.38 -0.19 -0.23 -0.15 -0.29 [119,] -0.01 -0.27 -0.01 -0.05 -0.01 -0.06 -0.18 [120,] -0.03 -0.84 -0.22 -0.12 -0.06 -0.12 -0.28 [121,] -0.04 -0.99 -0.26 -0.07 -0.07 -0.18 -0.29 [122,] -0.04 -1.73 -0.62 -0.21 -0.21 -0.16 -0.29 [123,] -0.06 -2.68 -0.96 -0.26 -0.32 -0.19 -0.23 [124,] -0.07 -1.93 -0.50 -0.14 -0.07 -0.14 -0.18 [125,] -0.03 -0.58 -0.04 -0.08 -0.13 -0.13 -0.27 [126,] -0.04 -1.41 -0.57 -0.19 -0.23 -0.19 -0.29 [127,] -0.03 -0.60 0.00 -0.05 -0.11 -0.14 -0.27 [128,] -0.02 -0.47 -0.03 -0.07 -0.09 -0.09 -0.22 [129,] -0.03 -0.90 -0.04 -0.06 -0.17 -0.14 -0.27 [130,] -0.03 -0.86 -0.05 -0.06 -0.19 -0.16 -0.29 [131,] -0.05 -1.30 -0.48 -0.19 -0.05 -0.14 -0.29 [132,] -0.04 -1.76 -0.63 -0.17 -0.25 -0.21 -0.29 [133,] -0.03 -0.86 -0.05 -0.06 -0.19 -0.16 -0.29 [134,] -0.06 -1.74 -0.26 -0.13 -0.39 -0.19 -0.22 [135,] -0.07 -2.15 -0.67 -0.20 -0.34 -0.20 -0.21 [136,] -0.07 -2.36 -1.11 -0.22 -0.37 -0.29 -0.16 [137,] -0.03 -0.75 -0.03 -0.07 -0.21 -0.17 -0.29 [138,] -0.06 -2.33 -0.95 -0.13 -0.06 -0.25 -0.23 [139,] -0.02 -0.60 -0.09 -0.09 -0.11 -0.11 -0.25 [140,] -0.06 -1.56 -0.23 -0.06 -0.29 -0.23 -0.26 [141,] -0.06 -1.50 -0.55 -0.11 -0.06 -0.22 -0.26 [142,] -0.06 -1.76 -0.83 -0.28 -0.33 -0.22 -0.27 [143,] -0.04 -1.12 -0.29 -0.21 -0.21 -0.12 -0.29 [144,] -0.06 -3.12 -0.90 -0.12 -0.30 -0.24 -0.25 [145,] -0.05 -1.45 -0.21 -0.16 -0.32 -0.16 -0.27 [146,] -0.05 -1.73 -0.19 -0.05 -0.23 -0.19 -0.29 [147,] -0.03 -0.83 -0.12 -0.12 -0.15 -0.12 -0.28 [148,] -0.03 -1.63 -0.47 -0.16 -0.03 -0.09 -0.28 [149,] -0.04 -2.20 -0.58 -0.15 -0.23 -0.15 -0.29 [150,] -0.07 -1.89 -0.49 -0.07 -0.35 -0.28 -0.19 [151,] -0.04 -1.62 -0.31 -0.17 -0.26 -0.13 -0.29 [152,] -0.05 -1.11 -0.04 -0.10 -0.20 -0.15 -0.28 [153,] -0.05 -1.75 -0.22 -0.11 -0.27 -0.16 -0.27 [154,] -0.07 -2.75 -1.11 -0.30 -0.45 -0.22 -0.16 [155,] -0.05 -1.11 -0.04 -0.10 -0.20 -0.15 -0.28 [156,] -0.07 -2.85 -1.02 -0.27 -0.41 -0.20 -0.20 [157,] -0.05 -2.79 -0.81 -0.27 -0.05 -0.05 -0.27 [158,] -0.07 -2.55 -1.03 -0.28 -0.07 -0.14 -0.20 [159,] -0.05 -1.37 -0.36 -0.20 -0.25 -0.15 -0.28 [160,] -0.03 -1.03 -0.13 -0.06 -0.16 -0.16 -0.29 [161,] -0.04 -1.06 -0.16 -0.08 -0.24 -0.20 -0.29 [162,] -0.04 -0.98 -0.15 -0.07 -0.18 -0.18 -0.29 [163,] -0.04 -1.41 -0.57 -0.19 -0.23 -0.19 -0.29 [164,] -0.04 -1.72 -0.55 -0.18 -0.18 -0.15 -0.29 [165,] -0.04 -1.30 -0.41 -0.12 -0.04 -0.16 -0.29 [166,] -0.03 -0.88 -0.05 -0.13 -0.03 -0.06 -0.29 [167,] -0.08 -4.74 -1.25 -0.17 -0.42 -0.17 -0.08 [168,] -0.03 -0.62 -0.04 -0.11 -0.14 -0.11 -0.28 [169,] -0.05 -2.26 -0.81 -0.16 -0.16 -0.22 -0.27 [170,] -0.05 -2.80 -0.74 -0.20 -0.10 -0.10 -0.28 [171,] -0.03 -1.64 -0.43 -0.12 -0.17 -0.14 -0.28 [172,] -0.02 -0.35 0.00 -0.06 -0.06 -0.08 -0.21 [173,] -0.02 -0.76 -0.24 -0.09 -0.02 -0.12 -0.26 [174,] -0.06 -2.56 -0.92 -0.18 -0.31 -0.24 -0.24 [175,] -0.03 -0.86 -0.05 -0.06 -0.19 -0.16 -0.29 [176,] -0.05 -1.71 -0.01 -0.11 -0.27 -0.11 -0.27 [177,] -0.04 -1.04 -0.15 -0.12 -0.19 -0.15 -0.29 [178,] -0.06 -1.50 -0.55 -0.11 -0.06 -0.22 -0.26 [179,] -0.03 -1.08 -0.24 -0.14 -0.03 -0.10 -0.29 [180,] -0.06 -2.08 -0.84 -0.22 -0.28 -0.22 -0.26 [181,] -0.07 -2.97 -1.06 -0.35 -0.42 -0.14 -0.18 [182,] -0.02 -0.52 -0.02 -0.07 -0.10 -0.08 -0.21 [183,] -0.04 -1.44 -0.18 -0.13 -0.22 -0.13 -0.29 [184,] -0.02 -0.82 -0.15 -0.09 -0.11 -0.11 -0.25 [185,] -0.02 -0.44 -0.01 -0.06 -0.06 -0.10 -0.24 [186,] -0.02 -0.55 -0.14 -0.08 -0.02 -0.10 -0.24 [187,] -0.02 -0.57 -0.02 -0.06 -0.11 -0.11 -0.24 [188,] -0.04 -0.98 -0.15 -0.07 -0.18 -0.18 -0.29 [189,] -0.03 -0.98 -0.31 -0.12 -0.12 -0.15 -0.28 [190,] -0.08 -2.65 -1.24 -0.08 -0.41 -0.41 -0.08 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-0.19 0.06 [395,] -0.07 -2.09 -0.46 -0.13 -0.07 -0.13 -0.22 [396,] -0.02 -0.39 -0.07 -0.09 -0.07 -0.09 -0.22 [397,] -0.05 -0.99 -0.07 -0.05 -0.27 -0.23 -0.29 [398,] -0.04 -0.81 -0.03 -0.04 -0.15 -0.18 -0.29 [399,] -0.04 -1.17 -0.55 -0.15 -0.04 -0.18 -0.29 [400,] -0.06 -1.25 -0.09 -0.11 -0.28 -0.17 -0.26 [401,] -0.01 -0.33 -0.05 -0.06 -0.02 -0.06 -0.18 [402,] -0.02 -0.41 -0.06 -0.06 -0.02 -0.08 -0.20 [403,] -0.04 -1.73 -0.62 -0.21 -0.21 -0.16 -0.29 [404,] -0.05 -1.68 -0.08 -0.11 -0.37 -0.16 -0.28 [405,] -0.07 -3.76 -0.99 -0.26 -0.20 -0.07 -0.21 [406,] -0.02 -0.82 -0.15 -0.09 -0.11 -0.11 -0.25 [407,] -0.06 -3.12 -0.90 -0.12 -0.30 -0.24 -0.25 [408,] -0.04 -1.75 -0.56 -0.15 -0.22 -0.19 -0.29 [409,] -0.06 -1.58 -0.41 -0.12 -0.29 -0.23 -0.25 [410,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.28 [411,] -0.06 -1.30 -0.24 -0.12 -0.18 -0.18 -0.25 [412,] -0.04 -1.45 -0.27 -0.08 -0.23 -0.20 -0.29 [413,] -0.05 -1.28 -0.33 -0.19 -0.19 -0.14 -0.29 [414,] -0.04 -1.57 -0.37 -0.15 -0.22 -0.15 -0.29 [415,] -0.02 -0.40 -0.03 -0.05 -0.02 -0.09 -0.23 [416,] -0.05 -1.14 -0.21 -0.10 -0.05 -0.16 -0.28 [417,] -0.03 -1.64 -0.43 -0.12 -0.17 -0.14 -0.28 [418,] -0.07 -2.45 -0.99 -0.26 -0.26 -0.20 -0.21 [419,] -0.04 -1.00 -0.26 -0.11 -0.19 -0.19 -0.29 [420,] -0.04 -0.77 -0.44 -0.18 -0.18 -0.22 -0.29 [421,] -0.03 -0.56 -0.10 -0.10 -0.13 -0.13 -0.27 [422,] -0.04 -0.96 -0.14 -0.07 -0.04 -0.14 -0.29 [423,] -0.13 -4.88 -1.98 -0.26 -0.66 -0.13 0.60 [424,] -0.01 -0.32 -0.02 -0.07 -0.01 -0.06 -0.20 [425,] -0.06 -1.58 -0.41 -0.12 -0.29 -0.23 -0.25 [426,] -0.02 -0.60 -0.09 -0.09 -0.11 -0.11 -0.25 [427,] -0.03 -0.71 0.00 -0.03 -0.10 -0.16 -0.29 [428,] -0.03 -0.77 -0.20 -0.11 -0.03 -0.11 -0.28 [429,] -0.07 -2.08 -0.98 -0.33 -0.33 -0.20 -0.22 [430,] -0.04 -1.41 -0.44 -0.18 -0.22 -0.18 -0.29 [431,] -0.08 -2.48 -1.16 -0.16 -0.23 -0.31 -0.13 [432,] -0.03 -0.58 -0.04 -0.08 -0.13 -0.13 -0.27 [433,] -0.03 -0.76 -0.11 -0.11 -0.11 -0.11 -0.28 [434,] -0.02 -0.83 -0.24 -0.08 -0.02 -0.08 -0.21 [435,] -0.07 -1.93 -0.50 -0.14 -0.07 -0.14 -0.18 [436,] -0.04 -0.98 -0.25 -0.11 -0.04 -0.14 -0.29 [437,] -0.06 -2.40 -0.86 -0.11 -0.06 -0.23 -0.26 [438,] -0.05 -2.13 -0.76 -0.20 -0.25 -0.20 -0.28 [439,] -0.04 -1.20 -0.31 -0.18 -0.13 -0.13 -0.29 [440,] -0.09 -2.52 -0.65 -0.19 -0.56 -0.19 0.02 [441,] -0.07 -2.83 -1.01 -0.20 -0.20 -0.20 -0.20 [442,] -0.02 -0.65 -0.10 -0.07 -0.07 -0.12 -0.26 [443,] -0.04 -0.98 -0.25 -0.11 -0.04 -0.14 -0.29 [444,] -0.03 -0.68 -0.05 -0.06 -0.12 -0.16 -0.28 [445,] -0.02 -0.58 -0.09 -0.09 -0.02 -0.09 -0.25 [446,] -0.03 -0.56 -0.10 -0.10 -0.13 -0.13 -0.27 [447,] -0.03 -0.68 -0.05 -0.06 -0.12 -0.16 -0.28 [448,] -0.05 -2.13 -0.68 -0.18 -0.23 -0.18 -0.29 [449,] -0.09 -3.44 -0.93 -0.19 -0.56 -0.19 0.02 [450,] -0.07 -2.49 -1.01 -0.20 -0.34 -0.27 -0.21 [451,] -0.04 -0.96 -0.14 -0.07 -0.04 -0.14 -0.29 [452,] 0.16 4.43 0.25 0.49 0.66 0.66 1.07 [453,] 0.19 5.21 0.77 0.58 0.19 0.96 1.86 [454,] 0.11 4.18 1.70 0.57 0.68 0.23 -0.02 [455,] 0.17 5.42 1.70 0.51 0.85 0.34 1.21 [456,] 0.18 3.89 0.02 0.71 0.88 0.88 1.40 [457,] 0.15 3.37 0.23 0.31 0.15 0.77 0.81 [458,] 0.16 6.08 2.47 0.66 0.82 0.33 1.08 [459,] 0.19 4.20 0.29 0.38 0.57 0.76 1.80 [460,] 0.04 1.40 0.57 0.08 0.23 0.15 -0.89 [461,] 0.05 1.61 0.75 0.20 0.15 0.10 -0.81 [462,] 0.03 1.05 0.43 0.11 0.11 0.06 -0.94 [463,] 0.17 7.17 2.56 0.51 0.17 0.68 1.24 [464,] 0.18 7.46 2.66 0.89 0.71 0.18 1.42 [465,] 0.15 5.66 1.53 0.31 0.92 0.31 0.80 [466,] 0.16 5.14 2.41 0.48 0.16 0.32 0.98 [467,] 0.12 3.12 0.46 0.12 0.69 0.58 0.03 [468,] 0.11 3.96 1.07 0.21 0.75 0.32 -0.12 [469,] 0.22 5.84 0.87 0.65 1.08 1.08 2.60 [470,] 0.11 4.67 1.67 0.44 0.56 0.56 -0.05 [471,] 0.15 7.08 2.26 0.75 0.60 0.75 0.74 [472,] 0.19 5.14 0.76 0.57 0.95 0.76 1.78 [473,] 0.16 4.32 1.12 0.80 0.16 0.64 0.97 [474,] 0.27 7.40 0.41 0.82 1.37 1.10 4.77 [475,] 0.19 5.24 1.36 0.78 1.17 0.39 1.90 [476,] 0.11 4.67 1.67 0.44 0.56 0.44 -0.05 [477,] 0.13 3.40 1.26 0.50 0.88 0.38 0.22 [478,] 0.06 1.56 0.09 0.12 0.29 0.12 -0.74 [479,] 0.14 4.39 0.55 0.55 0.82 0.55 0.45 [480,] 0.09 2.49 0.65 0.28 0.09 0.28 -0.35 [481,] 0.15 4.93 1.54 0.62 0.15 0.62 0.83 [482,] 0.05 1.37 0.20 0.10 0.36 0.10 -0.80 [483,] 0.19 3.32 0.14 0.95 0.76 0.95 1.77 [484,] 0.15 4.94 1.54 0.62 0.15 0.77 0.83 [485,] 0.15 4.84 1.06 0.30 0.91 0.61 0.76 [486,] 0.10 3.81 1.54 0.21 0.62 0.41 -0.18 [487,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.22 [488,] 0.23 7.27 2.27 0.45 1.14 1.14 2.97 [489,] 0.14 7.13 2.06 0.27 0.82 0.55 0.45 [490,] 0.10 4.33 1.55 0.10 0.10 0.31 -0.18 [491,] 0.03 1.72 0.50 0.07 0.20 0.10 -0.92 [492,] 0.08 3.14 1.27 0.25 0.51 0.42 -0.45 [493,] 0.26 5.68 1.03 0.77 0.77 1.03 4.12 [494,] 0.13 3.56 0.92 0.13 0.79 0.26 0.34 [495,] 0.14 3.74 0.55 0.42 0.69 0.69 0.47 [496,] 0.26 12.15 3.88 1.03 1.55 1.29 4.14 [497,] 0.18 7.48 2.67 0.71 0.18 0.18 1.44 [498,] 0.20 5.52 0.82 0.61 0.61 0.82 2.22 [499,] 0.23 7.47 1.63 0.93 0.93 1.17 3.19 [500,] 0.16 5.27 0.07 0.49 0.49 0.66 1.09 [501,] 0.14 6.74 2.15 0.72 0.72 0.57 0.58 [502,] 0.18 6.48 2.63 0.35 0.88 0.70 1.36 [503,] 0.15 3.32 0.60 0.30 0.91 0.60 0.75 [504,] 0.12 3.37 0.50 0.25 0.50 0.62 0.20 [505,] 0.21 10.87 3.14 1.05 0.21 0.63 2.36 [506,] 0.10 2.71 0.40 0.30 0.30 0.30 -0.23 [507,] 0.12 3.15 1.17 0.47 0.12 0.47 0.05 [508,] 0.09 2.78 0.61 0.26 0.61 0.35 -0.42 [509,] 0.09 2.79 0.61 0.17 0.35 0.09 -0.42 [510,] 0.07 1.45 0.10 0.07 0.20 0.13 -0.67 [511,] 0.17 3.78 0.69 0.52 1.03 0.69 1.27 [512,] 0.16 6.55 2.34 0.62 0.94 0.62 0.87 [513,] 0.07 3.98 1.05 0.07 0.35 0.28 -0.62 [514,] 0.15 4.78 0.60 0.45 0.75 0.30 0.72 [515,] 0.08 2.10 0.31 0.08 0.31 0.31 -0.54 [516,] 0.23 7.31 1.60 0.91 0.23 0.91 3.02 [517,] 0.08 4.39 1.16 0.08 0.31 0.31 -0.54 [518,] 0.11 4.65 1.66 0.44 0.55 0.22 -0.06 [519,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.22 [520,] 0.00 0.20 0.07 0.01 0.03 0.00 -1.00 [521,] 0.09 4.90 1.41 0.28 0.38 0.38 -0.32 [522,] 0.08 2.07 0.54 0.23 0.38 0.23 -0.55 [523,] 0.18 5.70 1.25 0.36 0.71 0.36 1.44 [524,] 0.17 3.72 0.68 0.68 0.34 0.85 1.20 [525,] 0.11 2.93 0.76 0.33 0.65 0.43 -0.09 [526,] 0.18 6.68 2.71 0.18 0.90 0.90 1.50 [527,] 0.12 3.90 1.83 0.37 0.12 0.37 0.14 [528,] 0.17 4.68 1.21 0.35 0.87 0.87 1.31 [529,] 0.08 2.40 0.53 0.23 0.38 0.23 -0.57 [530,] 0.14 4.57 0.21 0.29 0.29 0.57 0.57 [531,] 0.20 8.58 3.06 0.82 0.20 0.41 2.21 [532,] 0.16 5.02 1.57 0.47 0.78 0.63 0.89 [533,] 0.14 5.11 0.55 0.14 0.83 0.41 0.47 [534,] 0.07 1.76 0.26 0.13 0.33 0.20 -0.67 [535,] 0.19 7.91 2.82 0.56 0.75 0.56 1.73 [536,] 0.13 3.46 1.28 0.64 0.77 0.64 0.26 [537,] 0.15 5.66 1.53 0.31 0.92 0.31 0.80 [538,] 0.18 4.84 1.26 0.18 0.54 0.54 1.47 [539,] 0.11 3.05 0.79 0.45 0.11 0.23 -0.02 [540,] 0.09 2.92 0.91 0.18 0.36 0.36 -0.36 [541,] 0.24 4.19 0.18 0.48 0.24 0.72 3.41 [542,] 0.18 5.90 2.77 0.55 0.92 0.74 1.62 [543,] 0.09 2.05 0.65 0.37 0.37 0.28 -0.33 [544,] 0.14 4.51 0.99 0.56 0.85 0.71 0.53 [545,] 0.11 2.97 0.44 0.22 0.66 0.22 -0.07 [546,] 0.13 2.97 0.20 0.67 0.67 0.40 0.40 [547,] 0.11 3.39 1.59 0.32 0.53 0.11 -0.14 [548,] 0.16 6.83 2.44 0.33 0.16 0.33 1.04 [549,] 0.14 5.98 2.13 0.43 0.71 0.57 0.56 [550,] 0.16 4.99 1.56 0.31 0.62 0.31 0.87 [551,] 0.13 4.30 2.02 0.40 0.13 0.13 0.39 [552,] 0.25 14.33 3.77 1.26 1.01 1.26 3.86 [553,] 0.23 10.92 3.49 0.93 1.39 0.93 3.15 [554,] 0.02 0.97 0.35 0.05 0.14 0.07 -0.96 [555,] 0.16 6.02 2.44 0.49 0.98 0.49 1.03 [556,] 0.19 6.85 2.78 0.93 0.93 0.37 1.64 [557,] 0.12 3.17 1.17 0.23 0.70 0.47 0.06 [558,] 0.05 1.67 0.68 0.09 0.23 0.18 -0.84 [559,] 0.09 2.90 1.36 0.09 0.45 0.18 -0.37 [560,] 0.12 3.95 1.24 0.37 0.74 0.37 0.17 [561,] 0.01 0.30 0.12 0.03 0.04 0.01 -0.99 [562,] 0.21 5.79 0.32 0.43 1.07 1.07 2.53 [563,] 0.03 1.34 0.43 0.06 0.14 0.06 -0.94 [564,] 0.18 6.48 2.63 0.35 0.88 0.70 1.36 [565,] 0.26 7.04 1.04 0.52 1.30 1.30 4.22 [566,] 0.16 4.21 1.56 0.62 0.16 0.78 0.87 [567,] 0.10 2.27 0.41 0.31 0.10 0.31 -0.18 [568,] 0.34 17.52 2.36 1.35 1.68 1.68 7.73 [569,] 0.12 3.35 0.50 0.12 0.37 0.62 0.18 [570,] 0.10 3.81 1.54 0.21 0.62 0.41 -0.18 [571,] 0.02 0.52 0.08 0.02 0.06 0.02 -0.97 [572,] 0.28 4.86 0.21 0.55 0.83 1.39 4.92 [573,] 0.16 5.15 2.42 0.81 0.81 0.64 1.00 [574,] 0.18 3.91 0.71 0.18 0.53 0.89 1.43 [575,] 0.15 4.81 0.60 0.60 0.90 0.60 0.74 [576,] 0.07 1.48 0.10 0.20 0.34 0.13 -0.65 [577,] 0.07 2.92 1.04 0.14 0.35 0.28 -0.63 [578,] 0.13 4.13 0.90 0.52 0.52 0.52 0.28 [579,] 0.14 5.05 2.05 0.41 0.82 0.27 0.43 [580,] 0.03 1.30 0.46 0.09 0.19 0.09 -0.93 [581,] 0.07 1.91 0.28 0.07 0.35 0.28 -0.62 [582,] 0.05 1.71 0.69 0.19 0.32 0.14 -0.84 [583,] 0.12 4.58 1.86 0.37 0.74 0.50 0.18 [584,] 0.11 2.48 0.17 0.23 0.34 0.34 -0.02 [585,] 0.09 2.89 0.36 0.27 0.54 0.18 -0.37 [586,] 0.12 3.68 1.73 0.58 0.69 0.58 0.02 [587,] 0.21 11.16 3.22 0.21 1.07 1.07 2.54 [588,] 0.20 9.22 2.94 0.20 1.18 0.98 1.96 [589,] 0.10 3.05 1.43 0.38 0.38 0.38 -0.30 [590,] 0.08 2.60 1.22 0.24 0.24 0.16 -0.49 [591,] 0.16 4.45 1.15 0.66 0.16 0.33 1.09 [592,] 0.15 6.21 2.22 0.44 0.89 0.30 0.68 [593,] 0.08 3.50 1.25 0.17 0.25 0.17 -0.46 [594,] 0.21 5.73 1.48 0.42 1.06 0.85 2.46 [595,] 0.11 3.41 1.07 0.43 0.43 0.32 -0.13 [596,] 0.11 5.10 1.63 0.33 0.43 0.22 -0.09 [597,] 0.13 2.76 0.19 0.13 0.25 0.63 0.21 [598,] 0.14 4.35 1.36 0.27 0.68 0.54 0.42 [599,] 0.09 2.88 0.90 0.18 0.54 0.45 -0.38 [600,] 0.03 0.70 0.22 0.10 0.19 0.06 -0.92 [601,] 0.10 3.07 1.44 0.29 0.10 0.48 -0.29 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 10000 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsFormula, data = Affairs, method = "SANN") $terms affairs ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairs 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairs age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 451 150 0 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation 5.608161 -0.050347 0.161852 -0.476324 0.106006 rating logSigma -0.712242 2.244542 $left [1] 0 $right [1] Inf class [1] "censReg" "maxLik" "maxim" print( x, digits = 2 ) Call: censReg(formula = affairsFormula, data = Affairs, method = "SANN") Coefficients: (Intercept) age yearsmarried religiousness occupation 4.92 -0.18 0.57 -1.60 0.55 rating logSigma -2.01 2.17 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating -0.04 0.13 -0.36 0.12 -0.45 printME( margEff( x ) ) age yearsmarried religiousness occupation rating -0.039 0.128 -0.357 0.122 -0.449 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0.000 0.000 0.000 0.000 yearsmarried 0 0.001 0.000 0.000 0.000 religiousness 0 0.000 0.009 0.000 0.000 occupation 0 0.000 0.000 0.004 0.000 rating 0 0.000 0.000 0.000 0.009 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.039 0.019 -2.1 0.04 * yearsmarried 0.128 0.032 4.0 6e-05 *** religiousness -0.357 0.094 -3.8 2e-04 *** occupation 0.122 0.062 2.0 0.05 * rating -0.449 0.095 -4.7 3e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation SANN maximization, 0 iterations Return code 0: removed message Log-Likelihood: -706.96 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 4.918 2.945 1.7 0.09 . age -0.176 0.084 -2.1 0.04 * yearsmarried 0.575 0.144 4.0 7e-05 *** religiousness -1.600 0.427 -3.7 2e-04 *** occupation 0.548 0.278 2.0 0.05 * rating -2.012 0.427 -4.7 2e-06 *** logSigma 2.171 0.072 30.0 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsFormula, data = Affairs, method = "SANN") Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 4.918 2.945 1.7 0.09 . age -0.176 0.084 -2.1 0.04 * yearsmarried 0.575 0.144 4.0 7e-05 *** religiousness -1.600 0.427 -3.7 2e-04 *** occupation 0.548 0.278 2.0 0.05 * rating -2.012 0.427 -4.7 2e-06 *** logSigma 2.171 0.072 30.0 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 SANN maximization, 0 iterations Return code 0: removed message Log-likelihood: -706.96 on 7 Df > > ## usual tobit estimation with user-defined starting values > estResultStart <- censReg( affairsFormula, data = Affairs, + start = c( 8.17, -0.18, 0.55, -1.69, 0.33, -2.3, 2.13 ) ) > printAll( estResultStart, sumMeCalcVCov = FALSE, sumMeReturnJacobian = TRUE ) $maximum [1] -705.58 $estimate (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 $gradient (Intercept) age yearsmarried religiousness occupation 0 0 0 0 0 rating logSigma 0 0 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.0 -165.5 -44.9 -14.8 -21.3 -18.5 age -165.5 -5890.2 -1675.4 -500.7 -717.1 -602.6 yearsmarried -44.9 -1675.4 -550.7 -140.7 -193.4 -159.0 religiousness -14.8 -500.7 -140.7 -50.8 -62.7 -54.8 occupation -21.3 -717.1 -193.4 -62.7 -106.7 -78.9 rating -18.5 -602.6 -159.0 -54.8 -78.9 -74.9 logSigma -37.1 -1206.3 -301.5 -116.1 -155.3 -148.5 logSigma (Intercept) -37.1 age -1206.3 yearsmarried -301.5 religiousness -116.1 occupation -155.3 rating -148.5 logSigma -530.5 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "Newton-Raphson maximisation" $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.06 -2.09 -0.56 -0.17 -0.40 -0.23 -0.27 [2,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [3,] -0.10 -3.17 -1.49 -0.10 -0.10 -0.40 0.02 [4,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [5,] -0.07 -1.44 -0.05 -0.13 -0.39 -0.20 -0.24 [6,] -0.03 -0.85 -0.04 -0.05 -0.13 -0.13 -0.26 [7,] -0.05 -1.18 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.06 -3.18 -0.84 -0.11 -0.22 -0.22 -0.28 [9,] -0.09 -3.02 -1.42 -0.38 -0.09 -0.19 -0.02 [10,] -0.02 -0.41 -0.03 -0.08 -0.08 -0.09 -0.22 [11,] -0.14 -5.15 -2.09 -0.28 -0.97 -0.28 0.58 [12,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [13,] -0.04 -1.82 -0.58 -0.19 -0.23 -0.16 -0.29 [14,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [15,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [16,] -0.08 -3.03 -1.23 -0.08 -0.41 -0.41 -0.13 [17,] -0.11 -3.91 -1.58 -0.21 -0.42 -0.32 0.10 [18,] -0.04 -0.81 -0.03 -0.11 -0.18 -0.15 -0.29 [19,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.05 -1.38 -0.51 -0.10 -0.05 -0.26 -0.29 [21,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [22,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [23,] -0.05 -1.42 -0.53 -0.21 -0.26 -0.21 -0.28 [24,] -0.04 -1.13 -0.35 -0.11 -0.04 -0.18 -0.29 [25,] -0.04 -1.60 -0.17 -0.09 -0.26 -0.17 -0.29 [26,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [27,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [28,] -0.04 -1.86 -0.66 -0.22 -0.27 -0.18 -0.29 [29,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [30,] -0.04 -1.13 -0.17 -0.13 -0.21 -0.17 -0.29 [31,] -0.07 -3.07 -1.10 -0.29 -0.44 -0.22 -0.20 [32,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [33,] -0.02 -0.66 -0.01 -0.10 -0.15 -0.10 -0.25 [34,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [35,] -0.06 -1.95 -0.24 -0.06 -0.37 -0.24 -0.26 [36,] -0.04 -0.94 -0.06 -0.17 -0.21 -0.13 -0.29 [37,] -0.06 -2.35 -0.84 -0.17 -0.06 -0.22 -0.27 [38,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [39,] -0.04 -0.92 -0.06 -0.04 -0.12 -0.21 -0.29 [40,] -0.02 -0.44 -0.01 -0.06 -0.02 -0.10 -0.23 [41,] -0.03 -0.85 -0.26 -0.13 -0.16 -0.13 -0.26 [42,] -0.05 -2.46 -0.71 -0.24 -0.28 -0.14 -0.29 [43,] -0.02 -0.34 -0.01 -0.08 -0.02 -0.06 -0.20 [44,] -0.11 -3.10 -0.46 -0.23 -0.69 -0.11 0.21 [45,] -0.04 -1.28 -0.28 -0.20 -0.20 -0.12 -0.29 [46,] -0.03 -0.75 -0.14 -0.10 -0.17 -0.17 -0.29 [47,] -0.03 -0.83 -0.22 -0.12 -0.19 -0.15 -0.28 [48,] -0.08 -3.34 -1.19 -0.16 -0.40 -0.32 -0.15 [49,] -0.01 -0.40 -0.02 -0.06 -0.04 -0.07 -0.19 [50,] -0.08 -3.46 -1.23 -0.16 -0.49 -0.33 -0.13 [51,] -0.01 -0.25 -0.01 -0.06 -0.03 -0.06 -0.16 [52,] -0.06 -1.95 -0.43 -0.12 -0.37 -0.24 -0.26 [53,] -0.02 -0.46 -0.07 -0.08 -0.10 -0.08 -0.21 [54,] -0.05 -1.48 -0.55 -0.22 -0.33 -0.22 -0.28 [55,] -0.06 -1.21 -0.22 -0.06 -0.28 -0.28 -0.28 [56,] -0.11 -4.23 -1.71 -0.46 -0.34 -0.11 0.21 [57,] -0.02 -0.46 -0.03 -0.11 -0.08 -0.08 -0.24 [58,] -0.04 -1.34 -0.54 -0.15 -0.04 -0.18 -0.29 [59,] -0.02 -0.65 -0.02 -0.10 -0.12 -0.10 -0.25 [60,] -0.05 -1.56 -0.49 -0.20 -0.29 -0.20 -0.29 [61,] -0.07 -3.44 -1.10 -0.37 -0.51 -0.15 -0.20 [62,] -0.05 -2.00 -0.54 -0.16 -0.32 -0.22 -0.28 [63,] -0.03 -0.73 -0.03 -0.07 -0.17 -0.17 -0.28 [64,] -0.04 -0.99 -0.15 -0.07 -0.15 -0.18 -0.29 [65,] -0.04 -1.23 -0.27 -0.15 -0.23 -0.15 -0.29 [66,] -0.06 -2.36 -0.84 -0.11 -0.17 -0.28 -0.27 [67,] -0.04 -1.59 -0.43 -0.17 -0.26 -0.17 -0.29 [68,] -0.05 -2.13 -0.68 -0.14 -0.27 -0.23 -0.29 [69,] -0.01 -0.31 -0.02 -0.07 -0.07 -0.07 -0.19 [70,] -0.05 -1.24 -0.07 -0.09 -0.28 -0.18 -0.29 [71,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [72,] -0.02 -0.76 -0.24 -0.12 -0.09 -0.12 -0.25 [73,] -0.03 -0.69 0.00 -0.06 -0.16 -0.16 -0.28 [74,] -0.06 -2.86 -0.91 -0.24 -0.24 -0.18 -0.26 [75,] -0.09 -2.88 -1.35 -0.09 -0.45 -0.45 -0.07 [76,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.27 [77,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [78,] -0.03 -0.84 -0.12 -0.09 -0.19 -0.16 -0.28 [79,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [80,] -0.10 -5.84 -1.54 -0.20 -0.72 -0.20 0.06 [81,] -0.03 -0.57 -0.05 -0.10 -0.20 -0.16 -0.28 [82,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [83,] -0.04 -0.94 -0.03 -0.09 -0.13 -0.17 -0.29 [84,] -0.03 -1.14 -0.11 -0.11 -0.08 -0.08 -0.27 [85,] -0.02 -0.34 -0.02 -0.06 -0.02 -0.08 -0.20 [86,] -0.06 -1.31 -0.02 -0.06 -0.36 -0.24 -0.26 [87,] -0.05 -1.59 -0.75 -0.20 -0.25 -0.25 -0.29 [88,] -0.06 -1.73 -0.10 -0.19 -0.32 -0.13 -0.25 [89,] -0.02 -0.47 -0.03 -0.06 -0.02 -0.11 -0.24 [90,] -0.06 -2.31 -0.94 -0.19 -0.06 -0.25 -0.25 [91,] -0.06 -1.97 -0.92 -0.25 -0.18 -0.25 -0.26 [92,] -0.08 -3.07 -0.83 -0.17 -0.42 -0.25 -0.13 [93,] -0.04 -1.52 -0.41 -0.16 -0.21 -0.16 -0.29 [94,] -0.04 -2.26 -0.60 -0.20 -0.20 -0.12 -0.29 [95,] -0.05 -1.26 -0.02 -0.05 -0.14 -0.19 -0.29 [96,] -0.02 -0.99 -0.35 -0.12 -0.02 -0.12 -0.25 [97,] -0.11 -5.99 -1.58 -0.32 -0.63 -0.11 0.09 [98,] -0.08 -2.98 -0.81 -0.08 -0.48 -0.32 -0.14 [99,] -0.06 -2.05 -0.83 -0.17 -0.28 -0.28 -0.28 [100,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [101,] -0.02 -0.63 -0.23 -0.12 -0.02 -0.12 -0.25 [102,] -0.07 -2.47 -0.67 -0.13 -0.40 -0.27 -0.23 [103,] -0.02 -0.36 0.00 -0.06 -0.06 -0.08 -0.20 [104,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [105,] -0.06 -2.30 -0.93 -0.25 -0.37 -0.25 -0.25 [106,] -0.04 -0.86 -0.16 -0.16 -0.24 -0.16 -0.29 [107,] -0.04 -1.13 -0.29 -0.17 -0.21 -0.17 -0.29 [108,] -0.04 -2.07 -0.54 -0.15 -0.18 -0.15 -0.29 [109,] -0.10 -3.34 -1.56 -0.31 -0.63 -0.31 0.08 [110,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [111,] -0.02 -0.63 -0.14 -0.08 -0.02 -0.10 -0.23 [112,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [113,] -0.01 -0.31 -0.01 -0.05 -0.05 -0.05 -0.14 [114,] -0.03 -1.28 -0.30 -0.15 -0.21 -0.12 -0.28 [115,] -0.05 -1.43 -0.37 -0.16 -0.26 -0.21 -0.28 [116,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [117,] -0.09 -3.41 -1.38 -0.37 -0.28 -0.18 -0.04 [118,] -0.04 -1.23 -0.38 -0.19 -0.23 -0.15 -0.29 [119,] -0.01 -0.31 -0.01 -0.06 -0.01 -0.07 -0.19 [120,] -0.04 -0.98 -0.25 -0.14 -0.07 -0.14 -0.29 [121,] -0.04 -1.15 -0.30 -0.08 -0.08 -0.21 -0.29 [122,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [123,] -0.07 -2.96 -1.06 -0.28 -0.35 -0.21 -0.21 [124,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [125,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [126,] -0.04 -1.33 -0.54 -0.18 -0.22 -0.18 -0.29 [127,] -0.03 -0.66 0.00 -0.06 -0.12 -0.15 -0.28 [128,] -0.02 -0.45 -0.03 -0.07 -0.08 -0.08 -0.21 [129,] -0.03 -0.90 -0.04 -0.06 -0.17 -0.14 -0.27 [130,] -0.03 -0.88 -0.05 -0.07 -0.20 -0.16 -0.28 [131,] -0.06 -1.61 -0.60 -0.24 -0.06 -0.18 -0.26 [132,] -0.04 -1.70 -0.61 -0.16 -0.24 -0.20 -0.29 [133,] -0.03 -0.88 -0.05 -0.07 -0.20 -0.16 -0.28 [134,] -0.07 -1.97 -0.29 -0.15 -0.44 -0.22 -0.20 [135,] -0.08 -2.44 -0.76 -0.23 -0.38 -0.23 -0.18 [136,] -0.08 -2.57 -1.21 -0.24 -0.40 -0.32 -0.15 [137,] -0.04 -0.77 -0.03 -0.07 -0.21 -0.18 -0.29 [138,] -0.08 -2.81 -1.14 -0.15 -0.08 -0.30 -0.18 [139,] -0.02 -0.59 -0.09 -0.09 -0.11 -0.11 -0.24 [140,] -0.07 -1.77 -0.26 -0.07 -0.33 -0.26 -0.24 [141,] -0.07 -1.84 -0.68 -0.14 -0.07 -0.27 -0.23 [142,] -0.06 -1.80 -0.84 -0.28 -0.34 -0.23 -0.27 [143,] -0.05 -1.23 -0.32 -0.23 -0.23 -0.14 -0.29 [144,] -0.06 -3.37 -0.97 -0.13 -0.32 -0.26 -0.24 [145,] -0.06 -1.61 -0.24 -0.18 -0.36 -0.18 -0.26 [146,] -0.05 -1.93 -0.21 -0.05 -0.26 -0.21 -0.28 [147,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [148,] -0.04 -1.94 -0.56 -0.19 -0.04 -0.11 -0.29 [149,] -0.04 -2.17 -0.57 -0.15 -0.23 -0.15 -0.29 [150,] -0.08 -2.13 -0.55 -0.08 -0.39 -0.32 -0.16 [151,] -0.05 -1.74 -0.33 -0.19 -0.28 -0.14 -0.29 [152,] -0.06 -1.33 -0.05 -0.12 -0.24 -0.18 -0.26 [153,] -0.06 -2.02 -0.25 -0.13 -0.32 -0.19 -0.25 [154,] -0.08 -2.98 -1.21 -0.32 -0.48 -0.24 -0.14 [155,] -0.06 -1.33 -0.05 -0.12 -0.24 -0.18 -0.26 [156,] -0.07 -3.07 -1.10 -0.29 -0.44 -0.22 -0.20 [157,] -0.07 -3.57 -1.03 -0.34 -0.07 -0.07 -0.22 [158,] -0.09 -3.20 -1.30 -0.35 -0.09 -0.17 -0.10 [159,] -0.06 -1.54 -0.40 -0.23 -0.29 -0.17 -0.27 [160,] -0.03 -1.07 -0.13 -0.07 -0.17 -0.17 -0.28 [161,] -0.04 -1.09 -0.16 -0.08 -0.24 -0.20 -0.29 [162,] -0.04 -1.04 -0.15 -0.08 -0.19 -0.19 -0.29 [163,] -0.04 -1.33 -0.54 -0.18 -0.22 -0.18 -0.29 [164,] -0.04 -1.73 -0.55 -0.18 -0.18 -0.15 -0.29 [165,] -0.05 -1.57 -0.49 -0.15 -0.05 -0.20 -0.29 [166,] -0.04 -1.16 -0.06 -0.17 -0.04 -0.09 -0.29 [167,] -0.10 -5.49 -1.44 -0.19 -0.48 -0.19 0.00 [168,] -0.03 -0.66 -0.04 -0.12 -0.15 -0.12 -0.28 [169,] -0.06 -2.55 -0.91 -0.18 -0.18 -0.24 -0.26 [170,] -0.06 -3.40 -0.90 -0.24 -0.12 -0.12 -0.26 [171,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [172,] -0.02 -0.36 0.00 -0.06 -0.06 -0.08 -0.20 [173,] -0.03 -0.86 -0.27 -0.11 -0.03 -0.13 -0.26 [174,] -0.07 -2.76 -0.98 -0.20 -0.33 -0.26 -0.24 [175,] -0.03 -0.88 -0.05 -0.07 -0.20 -0.16 -0.28 [176,] -0.06 -2.05 -0.01 -0.13 -0.32 -0.13 -0.24 [177,] -0.04 -1.13 -0.17 -0.13 -0.21 -0.17 -0.29 [178,] -0.07 -1.84 -0.68 -0.14 -0.07 -0.27 -0.23 [179,] -0.04 -1.35 -0.30 -0.17 -0.04 -0.13 -0.29 [180,] -0.06 -2.21 -0.89 -0.24 -0.30 -0.24 -0.26 [181,] -0.08 -3.28 -1.17 -0.39 -0.47 -0.16 -0.16 [182,] -0.01 -0.48 -0.02 -0.06 -0.09 -0.07 -0.19 [183,] -0.05 -1.63 -0.20 -0.15 -0.25 -0.15 -0.29 [184,] -0.02 -0.79 -0.15 -0.09 -0.11 -0.11 -0.24 [185,] -0.02 -0.48 -0.01 -0.07 -0.07 -0.11 -0.24 [186,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [187,] -0.02 -0.58 -0.02 -0.06 -0.11 -0.11 -0.24 [188,] -0.04 -1.04 -0.15 -0.08 -0.19 -0.19 -0.29 [189,] -0.03 -1.01 -0.31 -0.13 -0.13 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-0.41 -0.16 -0.14 [394,] -0.11 -4.58 -1.64 -0.33 -0.65 -0.22 0.14 [395,] -0.08 -2.72 -0.59 -0.17 -0.08 -0.17 -0.11 [396,] -0.02 -0.39 -0.07 -0.09 -0.07 -0.09 -0.21 [397,] -0.05 -1.05 -0.07 -0.05 -0.29 -0.24 -0.29 [398,] -0.04 -0.90 -0.03 -0.04 -0.16 -0.21 -0.29 [399,] -0.04 -1.33 -0.62 -0.17 -0.04 -0.21 -0.29 [400,] -0.07 -1.46 -0.10 -0.13 -0.33 -0.20 -0.23 [401,] -0.01 -0.35 -0.05 -0.06 -0.03 -0.06 -0.18 [402,] -0.02 -0.46 -0.07 -0.07 -0.02 -0.09 -0.21 [403,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [404,] -0.06 -1.84 -0.09 -0.12 -0.40 -0.17 -0.27 [405,] -0.08 -4.60 -1.21 -0.32 -0.24 -0.08 -0.14 [406,] -0.02 -0.79 -0.15 -0.09 -0.11 -0.11 -0.24 [407,] -0.06 -3.37 -0.97 -0.13 -0.32 -0.26 -0.24 [408,] -0.04 -1.66 -0.53 -0.14 -0.21 -0.18 -0.29 [409,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [410,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.27 [411,] -0.07 -1.59 -0.29 -0.14 -0.22 -0.22 -0.20 [412,] -0.04 -1.46 -0.28 -0.08 -0.24 -0.20 -0.29 [413,] -0.05 -1.48 -0.38 -0.22 -0.22 -0.16 -0.28 [414,] -0.04 -1.58 -0.38 -0.15 -0.23 -0.15 -0.29 [415,] -0.02 -0.47 -0.03 -0.06 -0.02 -0.11 -0.24 [416,] -0.07 -1.47 -0.27 -0.13 -0.07 -0.20 -0.23 [417,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [418,] -0.08 -2.78 -1.13 -0.30 -0.30 -0.23 -0.18 [419,] -0.04 -1.03 -0.27 -0.11 -0.19 -0.19 -0.29 [420,] -0.05 -0.81 -0.47 -0.19 -0.19 -0.23 -0.29 [421,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [422,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [423,] -0.16 -5.81 -2.36 -0.31 -0.79 -0.16 0.91 [424,] -0.02 -0.38 -0.03 -0.09 -0.02 -0.07 -0.21 [425,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [426,] -0.02 -0.59 -0.09 -0.09 -0.11 -0.11 -0.24 [427,] -0.04 -0.82 0.00 -0.04 -0.11 -0.19 -0.29 [428,] -0.03 -0.93 -0.24 -0.14 -0.03 -0.14 -0.29 [429,] -0.07 -2.28 -1.07 -0.36 -0.36 -0.21 -0.21 [430,] -0.05 -1.49 -0.47 -0.19 -0.23 -0.19 -0.29 [431,] -0.09 -2.86 -1.34 -0.18 -0.27 -0.36 -0.07 [432,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [433,] -0.03 -0.83 -0.12 -0.12 -0.12 -0.12 -0.28 [434,] -0.02 -0.88 -0.25 -0.08 -0.02 -0.08 -0.21 [435,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [436,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [437,] -0.07 -2.88 -1.03 -0.14 -0.07 -0.27 -0.22 [438,] -0.05 -2.23 -0.80 -0.21 -0.27 -0.21 -0.28 [439,] -0.05 -1.42 -0.37 -0.21 -0.16 -0.16 -0.28 [440,] -0.11 -2.93 -0.76 -0.22 -0.65 -0.22 0.13 [441,] -0.08 -3.32 -1.18 -0.24 -0.24 -0.24 -0.16 [442,] -0.03 -0.71 -0.11 -0.08 -0.08 -0.13 -0.26 [443,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [444,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [445,] -0.03 -0.70 -0.10 -0.10 -0.03 -0.10 -0.26 [446,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [447,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [448,] -0.05 -2.22 -0.71 -0.19 -0.24 -0.19 -0.29 [449,] -0.11 -3.97 -1.07 -0.21 -0.64 -0.21 0.12 [450,] -0.07 -2.69 -1.09 -0.22 -0.36 -0.29 -0.20 [451,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [452,] 0.17 4.66 0.26 0.52 0.69 0.69 1.02 [453,] 0.20 5.40 0.80 0.60 0.20 1.00 1.72 [454,] 0.12 4.46 1.81 0.60 0.72 0.24 -0.01 [455,] 0.18 5.66 1.77 0.53 0.88 0.35 1.12 [456,] 0.19 4.28 0.02 0.78 0.97 0.97 1.58 [457,] 0.15 3.37 0.23 0.31 0.15 0.77 0.59 [458,] 0.17 6.44 2.61 0.70 0.87 0.35 1.06 [459,] 0.20 4.36 0.30 0.40 0.59 0.79 1.67 [460,] 0.04 1.47 0.60 0.08 0.24 0.16 -0.89 [461,] 0.04 1.22 0.57 0.15 0.11 0.08 -0.90 [462,] 0.02 0.63 0.26 0.07 0.07 0.03 -0.98 [463,] 0.18 7.36 2.63 0.53 0.18 0.70 1.09 [464,] 0.18 7.69 2.75 0.92 0.73 0.18 1.28 [465,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [466,] 0.16 4.97 2.33 0.47 0.16 0.31 0.64 [467,] 0.13 3.42 0.51 0.13 0.76 0.63 0.09 [468,] 0.12 4.28 1.16 0.23 0.81 0.35 -0.09 [469,] 0.24 6.47 0.96 0.72 1.20 1.20 2.91 [470,] 0.13 5.30 1.89 0.50 0.63 0.63 0.08 [471,] 0.17 7.94 2.53 0.84 0.68 0.84 0.94 [472,] 0.21 5.57 0.82 0.62 1.03 0.82 1.89 [473,] 0.16 4.38 1.14 0.81 0.16 0.65 0.79 [474,] 0.30 8.10 0.45 0.90 1.50 1.20 5.12 [475,] 0.21 5.62 1.46 0.83 1.25 0.42 1.94 [476,] 0.12 5.13 1.83 0.49 0.61 0.49 0.01 [477,] 0.14 3.75 1.39 0.56 0.97 0.42 0.31 [478,] 0.05 1.25 0.07 0.09 0.23 0.09 -0.85 [479,] 0.15 4.83 0.60 0.60 0.91 0.60 0.55 [480,] 0.08 2.13 0.55 0.24 0.08 0.24 -0.58 [481,] 0.16 4.98 1.56 0.62 0.16 0.62 0.65 [482,] 0.05 1.24 0.18 0.09 0.32 0.09 -0.86 [483,] 0.21 3.63 0.16 1.04 0.83 1.04 1.92 [484,] 0.16 5.11 1.60 0.64 0.16 0.80 0.74 [485,] 0.17 5.29 1.16 0.33 0.99 0.66 0.86 [486,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [487,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [488,] 0.25 8.09 2.53 0.51 1.26 1.26 3.34 [489,] 0.15 7.95 2.29 0.31 0.92 0.61 0.59 [490,] 0.09 3.87 1.38 0.09 0.09 0.28 -0.42 [491,] 0.03 1.61 0.47 0.06 0.19 0.09 -0.93 [492,] 0.10 3.63 1.47 0.29 0.59 0.49 -0.34 [493,] 0.28 6.07 1.10 0.83 0.83 1.10 4.18 [494,] 0.13 3.61 0.94 0.13 0.80 0.27 0.21 [495,] 0.15 4.09 0.61 0.45 0.76 0.76 0.56 [496,] 0.30 13.93 4.45 1.19 1.78 1.48 4.97 [497,] 0.17 7.26 2.59 0.69 0.17 0.17 1.03 [498,] 0.22 5.82 0.86 0.65 0.65 0.86 2.16 [499,] 0.26 8.26 1.81 1.03 1.03 1.29 3.53 [500,] 0.17 5.44 0.07 0.51 0.51 0.68 0.96 [501,] 0.16 7.52 2.40 0.80 0.80 0.64 0.74 [502,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [503,] 0.16 3.59 0.65 0.33 0.98 0.65 0.81 [504,] 0.13 3.55 0.53 0.26 0.53 0.66 0.18 [505,] 0.22 11.31 3.26 1.09 0.22 0.65 2.22 [506,] 0.09 2.54 0.38 0.28 0.28 0.28 -0.40 [507,] 0.11 3.05 1.13 0.45 0.11 0.45 -0.13 [508,] 0.10 3.11 0.68 0.29 0.68 0.39 -0.36 [509,] 0.07 2.37 0.52 0.15 0.30 0.07 -0.63 [510,] 0.05 1.04 0.07 0.05 0.14 0.09 -0.85 [511,] 0.19 4.14 0.75 0.56 1.13 0.75 1.41 [512,] 0.18 7.39 2.64 0.70 1.06 0.70 1.11 [513,] 0.07 4.13 1.09 0.07 0.36 0.29 -0.64 [514,] 0.15 4.87 0.61 0.46 0.76 0.30 0.57 [515,] 0.07 1.97 0.29 0.07 0.29 0.29 -0.64 [516,] 0.24 7.64 1.67 0.96 0.24 0.96 2.88 [517,] 0.08 4.41 1.16 0.08 0.31 0.31 -0.59 [518,] 0.11 4.77 1.70 0.45 0.57 0.23 -0.12 [519,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [520,] -0.01 -0.35 -0.13 -0.03 -0.05 -0.01 -1.00 [521,] 0.10 5.15 1.48 0.30 0.40 0.40 -0.33 [522,] 0.07 2.01 0.52 0.22 0.37 0.22 -0.62 [523,] 0.18 5.80 1.27 0.36 0.72 0.36 1.23 [524,] 0.18 3.90 0.71 0.71 0.35 0.89 1.14 [525,] 0.12 3.19 0.83 0.35 0.71 0.47 -0.05 [526,] 0.20 7.41 3.01 0.20 1.00 1.00 1.73 [527,] 0.12 3.69 1.73 0.35 0.12 0.35 -0.10 [528,] 0.19 5.14 1.33 0.38 0.95 0.95 1.47 [529,] 0.07 2.34 0.51 0.22 0.37 0.22 -0.64 [530,] 0.14 4.51 0.21 0.28 0.28 0.56 0.35 [531,] 0.21 8.67 3.10 0.83 0.21 0.41 1.90 [532,] 0.17 5.45 1.70 0.51 0.85 0.68 0.98 [533,] 0.14 5.35 0.58 0.14 0.87 0.43 0.42 [534,] 0.06 1.61 0.24 0.12 0.30 0.18 -0.76 [535,] 0.20 8.43 3.01 0.60 0.80 0.60 1.74 [536,] 0.15 3.98 1.47 0.74 0.88 0.74 0.48 [537,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [538,] 0.18 4.90 1.27 0.18 0.54 0.54 1.24 [539,] 0.10 2.69 0.70 0.40 0.10 0.20 -0.32 [540,] 0.09 2.93 0.92 0.18 0.37 0.37 -0.43 [541,] 0.24 4.23 0.18 0.48 0.24 0.73 2.98 [542,] 0.20 6.50 3.05 0.61 1.02 0.81 1.81 [543,] 0.09 2.00 0.64 0.36 0.36 0.27 -0.44 [544,] 0.16 5.13 1.12 0.64 0.96 0.80 0.75 [545,] 0.11 2.95 0.44 0.22 0.66 0.22 -0.19 [546,] 0.14 3.10 0.21 0.71 0.71 0.42 0.35 [547,] 0.10 3.28 1.54 0.31 0.51 0.10 -0.29 [548,] 0.16 6.58 2.35 0.31 0.16 0.31 0.67 [549,] 0.16 6.55 2.34 0.47 0.78 0.62 0.66 [550,] 0.16 5.02 1.57 0.31 0.63 0.31 0.67 [551,] 0.12 3.89 1.82 0.36 0.12 0.12 0.01 [552,] 0.28 16.16 4.25 1.42 1.13 1.42 4.47 [553,] 0.26 12.35 3.94 1.05 1.58 1.05 3.70 [554,] 0.02 0.81 0.29 0.04 0.12 0.06 -0.97 [555,] 0.18 6.59 2.67 0.53 1.07 0.53 1.15 [556,] 0.20 7.35 2.98 0.99 0.99 0.40 1.69 [557,] 0.13 3.45 1.28 0.26 0.77 0.51 0.11 [558,] 0.04 1.65 0.67 0.09 0.22 0.18 -0.86 [559,] 0.09 2.77 1.30 0.09 0.43 0.17 -0.49 [560,] 0.13 4.23 1.32 0.40 0.79 0.40 0.19 [561,] -0.01 -0.25 -0.10 -0.03 -0.03 -0.01 -1.00 [562,] 0.24 6.35 0.35 0.47 1.18 1.18 2.77 [563,] 0.02 0.87 0.28 0.04 0.09 0.04 -0.98 [564,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [565,] 0.29 7.79 1.15 0.58 1.44 1.44 4.66 [566,] 0.16 4.35 1.61 0.65 0.16 0.81 0.77 [567,] 0.09 1.99 0.36 0.27 0.09 0.27 -0.45 [568,] 0.38 19.73 2.66 1.52 1.90 1.90 8.80 [569,] 0.13 3.41 0.50 0.13 0.38 0.63 0.08 [570,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [571,] -0.01 -0.22 -0.03 -0.01 -0.02 -0.01 -1.00 [572,] 0.30 5.24 0.22 0.60 0.90 1.50 5.10 [573,] 0.18 5.74 2.69 0.90 0.90 0.72 1.19 [574,] 0.19 4.10 0.75 0.19 0.56 0.93 1.37 [575,] 0.17 5.31 0.66 0.66 0.99 0.66 0.87 [576,] 0.06 1.27 0.09 0.17 0.29 0.12 -0.77 [577,] 0.07 3.04 1.09 0.14 0.36 0.29 -0.64 [578,] 0.14 4.36 0.95 0.54 0.54 0.54 0.26 [579,] 0.14 5.34 2.17 0.43 0.87 0.29 0.42 [580,] 0.03 1.24 0.44 0.09 0.18 0.09 -0.94 [581,] 0.07 1.84 0.27 0.07 0.34 0.27 -0.68 [582,] 0.05 1.89 0.76 0.20 0.36 0.15 -0.82 [583,] 0.14 5.11 2.07 0.41 0.83 0.55 0.30 [584,] 0.11 2.33 0.16 0.21 0.32 0.32 -0.24 [585,] 0.09 2.83 0.35 0.27 0.53 0.18 -0.47 [586,] 0.13 4.31 2.02 0.67 0.81 0.67 0.23 [587,] 0.24 12.48 3.60 0.24 1.20 1.20 2.92 [588,] 0.22 10.43 3.33 0.22 1.33 1.11 2.35 [589,] 0.10 3.21 1.51 0.40 0.40 0.40 -0.31 [590,] 0.07 2.31 1.08 0.22 0.22 0.14 -0.65 [591,] 0.16 4.28 1.11 0.63 0.16 0.32 0.71 [592,] 0.16 6.62 2.36 0.47 0.95 0.32 0.69 [593,] 0.07 3.09 1.10 0.15 0.22 0.15 -0.63 [594,] 0.23 6.22 1.61 0.46 1.15 0.92 2.61 [595,] 0.11 3.44 1.08 0.43 0.43 0.32 -0.21 [596,] 0.11 5.02 1.60 0.32 0.43 0.21 -0.22 [597,] 0.12 2.72 0.19 0.12 0.25 0.62 0.04 [598,] 0.15 4.66 1.46 0.29 0.73 0.58 0.44 [599,] 0.10 3.23 1.01 0.20 0.61 0.50 -0.31 [600,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 [601,] 0.09 3.02 1.41 0.28 0.09 0.47 -0.40 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 150 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsFormula, data = Affairs, start = c(8.17, -0.18, 0.55, -1.69, 0.33, -2.3, 2.13)) $terms affairs ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairs 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairs age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 451 150 0 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.30 2.13 $left [1] 0 $right [1] Inf class [1] "censReg" "maxLik" "maxim" "list" print( x, digits = 2 ) Call: censReg(formula = affairsFormula, data = Affairs, start = c(8.17, -0.18, 0.55, -1.69, 0.33, -2.3, 2.13)) Coefficients: (Intercept) age yearsmarried religiousness occupation 8.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating -0.04 0.13 -0.39 0.08 -0.53 printME( margEff( x ) ) age yearsmarried religiousness occupation rating -0.042 0.130 -0.394 0.076 -0.534 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0.000 0.000 0.000 0.000 yearsmarried 0 0.001 0.000 0.000 0.000 religiousness 0 0.000 0.009 0.000 0.000 occupation 0 0.000 0.000 0.004 0.000 rating 0 0.000 0.000 0.000 0.009 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.042 NA NA NA yearsmarried 0.130 NA NA NA religiousness -0.394 NA NA NA occupation 0.076 NA NA NA rating -0.534 NA NA NA print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-Likelihood: -705.58 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 8.174 2.741 3.0 0.003 ** age -0.179 0.079 -2.3 0.023 * yearsmarried 0.554 0.135 4.1 4e-05 *** religiousness -1.686 0.404 -4.2 3e-05 *** occupation 0.326 0.254 1.3 0.200 rating -2.285 0.408 -5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsFormula, data = Affairs, start = c(8.17, -0.18, 0.55, -1.69, 0.33, -2.3, 2.13)) Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 8.174 2.741 3.0 0.003 ** age -0.179 0.079 -2.3 0.023 * yearsmarried 0.554 0.135 4.1 4e-05 *** religiousness -1.686 0.404 -4.2 3e-05 *** occupation 0.326 0.254 1.3 0.200 rating -2.285 0.408 -5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -705.58 on 7 Df Warning message: In summary.margEff.censReg(margEff(x, calcVCov = sumMeCalcVCov, : cannot calculate standard errors, t-values, and P-values, because the marginal effects do not have an attribute 'vcov': please set attribute 'calcVCov' of the 'margEff' method to 'TRUE' > logLik( estResultStart ) 'log Lik.' -705.58 (df=7) > nobs( estResultStart ) [1] 601 > formula( estResultStart ) affairs ~ age + yearsmarried + religiousness + occupation + rating > > ## usual tobit estimation using a "subset" of the data set > estResultSub <- censReg( affairsFormula, data = Affairs, subset = 3:600 ) > estResultSubMan <- censReg( affairsFormula, data = Affairs[ 3:600, ] ) > all.equal( estResultSub[ names( estResultSub ) != "call" ], + estResultSubMan[ names( estResultSubMan ) != "call" ] ) [1] TRUE > > # usual tobit estimation: margEff() with argument "vcov" > me0 <- margEff( estResult ) > me2 <- margEff( estResult, vcov = 2 * vcov( estResult ) ) > all.equal( me0, me2, check.attributes = FALSE ) [1] TRUE > all.equal( attr( me0, "vcov" ), attr( me2, "vcov" ) / 2 ) [1] TRUE > all.equal( summary( me0 )[ , 2 ], summary( me2 )[ , 2 ] / sqrt( 2 ) ) [1] TRUE > all.equal( summary( me0 )[ , 3 ], summary( me2 )[ , 3 ] * sqrt( 2 ) ) [1] TRUE > me3 <- margEff( estResult, vcov = 2 * vcov( estResult, logSigma = FALSE ), + vcovLogSigma = FALSE ) > all.equal( me2, me3 ) [1] TRUE > all.equal( summary( me2 ), summary( me3 ) ) [1] TRUE > > ## estimation with left-censoring at 5 > Affairs$affairsAdd <- Affairs$affairs + 5 > estResultAdd <- censReg( affairsAdd ~ age + yearsmarried + religiousness + + occupation + rating, data = Affairs, left = 5 ) > printAll( estResultAdd, sumMeReturnJacobian = TRUE ) $maximum [1] -705.58 $estimate (Intercept) age yearsmarried religiousness occupation 13.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 $gradient (Intercept) age yearsmarried religiousness occupation 0 0 0 0 0 rating logSigma 0 0 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.0 -165.5 -44.9 -14.8 -21.3 -18.5 age -165.5 -5890.2 -1675.4 -500.7 -717.1 -602.6 yearsmarried -44.9 -1675.4 -550.7 -140.7 -193.4 -159.0 religiousness -14.8 -500.7 -140.7 -50.8 -62.7 -54.8 occupation -21.3 -717.1 -193.4 -62.7 -106.7 -78.9 rating -18.5 -602.6 -159.0 -54.8 -78.9 -74.9 logSigma -37.1 -1206.3 -301.5 -116.1 -155.3 -148.5 logSigma (Intercept) -37.1 age -1206.3 yearsmarried -301.5 religiousness -116.1 occupation -155.3 rating -148.5 logSigma -530.5 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "Newton-Raphson maximisation" $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.06 -2.09 -0.56 -0.17 -0.40 -0.23 -0.27 [2,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [3,] -0.10 -3.17 -1.49 -0.10 -0.10 -0.40 0.02 [4,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [5,] -0.07 -1.44 -0.05 -0.13 -0.39 -0.20 -0.24 [6,] -0.03 -0.85 -0.04 -0.05 -0.13 -0.13 -0.26 [7,] -0.05 -1.18 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.06 -3.18 -0.84 -0.11 -0.22 -0.22 -0.28 [9,] -0.09 -3.02 -1.42 -0.38 -0.09 -0.19 -0.02 [10,] -0.02 -0.41 -0.03 -0.08 -0.08 -0.09 -0.22 [11,] -0.14 -5.15 -2.09 -0.28 -0.97 -0.28 0.58 [12,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [13,] -0.04 -1.82 -0.58 -0.19 -0.23 -0.16 -0.29 [14,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [15,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [16,] -0.08 -3.03 -1.23 -0.08 -0.41 -0.41 -0.13 [17,] -0.11 -3.91 -1.58 -0.21 -0.42 -0.32 0.10 [18,] -0.04 -0.81 -0.03 -0.11 -0.18 -0.15 -0.29 [19,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.05 -1.38 -0.51 -0.10 -0.05 -0.26 -0.29 [21,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [22,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [23,] -0.05 -1.42 -0.53 -0.21 -0.26 -0.21 -0.28 [24,] -0.04 -1.13 -0.35 -0.11 -0.04 -0.18 -0.29 [25,] -0.04 -1.60 -0.17 -0.09 -0.26 -0.17 -0.29 [26,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [27,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [28,] -0.04 -1.86 -0.66 -0.22 -0.27 -0.18 -0.29 [29,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [30,] -0.04 -1.13 -0.17 -0.13 -0.21 -0.17 -0.29 [31,] -0.07 -3.07 -1.10 -0.29 -0.44 -0.22 -0.20 [32,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [33,] -0.02 -0.66 -0.01 -0.10 -0.15 -0.10 -0.25 [34,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [35,] -0.06 -1.95 -0.24 -0.06 -0.37 -0.24 -0.26 [36,] -0.04 -0.94 -0.06 -0.17 -0.21 -0.13 -0.29 [37,] -0.06 -2.35 -0.84 -0.17 -0.06 -0.22 -0.27 [38,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [39,] -0.04 -0.92 -0.06 -0.04 -0.12 -0.21 -0.29 [40,] -0.02 -0.44 -0.01 -0.06 -0.02 -0.10 -0.23 [41,] -0.03 -0.85 -0.26 -0.13 -0.16 -0.13 -0.26 [42,] -0.05 -2.46 -0.71 -0.24 -0.28 -0.14 -0.29 [43,] -0.02 -0.34 -0.01 -0.08 -0.02 -0.06 -0.20 [44,] -0.11 -3.10 -0.46 -0.23 -0.69 -0.11 0.21 [45,] -0.04 -1.28 -0.28 -0.20 -0.20 -0.12 -0.29 [46,] -0.03 -0.75 -0.14 -0.10 -0.17 -0.17 -0.29 [47,] -0.03 -0.83 -0.22 -0.12 -0.19 -0.15 -0.28 [48,] -0.08 -3.34 -1.19 -0.16 -0.40 -0.32 -0.15 [49,] -0.01 -0.40 -0.02 -0.06 -0.04 -0.07 -0.19 [50,] -0.08 -3.46 -1.23 -0.16 -0.49 -0.33 -0.13 [51,] -0.01 -0.25 -0.01 -0.06 -0.03 -0.06 -0.16 [52,] -0.06 -1.95 -0.43 -0.12 -0.37 -0.24 -0.26 [53,] -0.02 -0.46 -0.07 -0.08 -0.10 -0.08 -0.21 [54,] -0.05 -1.48 -0.55 -0.22 -0.33 -0.22 -0.28 [55,] -0.06 -1.21 -0.22 -0.06 -0.28 -0.28 -0.28 [56,] -0.11 -4.23 -1.71 -0.46 -0.34 -0.11 0.21 [57,] -0.02 -0.46 -0.03 -0.11 -0.08 -0.08 -0.24 [58,] -0.04 -1.34 -0.54 -0.15 -0.04 -0.18 -0.29 [59,] -0.02 -0.65 -0.02 -0.10 -0.12 -0.10 -0.25 [60,] -0.05 -1.56 -0.49 -0.20 -0.29 -0.20 -0.29 [61,] -0.07 -3.44 -1.10 -0.37 -0.51 -0.15 -0.20 [62,] -0.05 -2.00 -0.54 -0.16 -0.32 -0.22 -0.28 [63,] -0.03 -0.73 -0.03 -0.07 -0.17 -0.17 -0.28 [64,] -0.04 -0.99 -0.15 -0.07 -0.15 -0.18 -0.29 [65,] -0.04 -1.23 -0.27 -0.15 -0.23 -0.15 -0.29 [66,] -0.06 -2.36 -0.84 -0.11 -0.17 -0.28 -0.27 [67,] -0.04 -1.59 -0.43 -0.17 -0.26 -0.17 -0.29 [68,] -0.05 -2.13 -0.68 -0.14 -0.27 -0.23 -0.29 [69,] -0.01 -0.31 -0.02 -0.07 -0.07 -0.07 -0.19 [70,] -0.05 -1.24 -0.07 -0.09 -0.28 -0.18 -0.29 [71,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [72,] -0.02 -0.76 -0.24 -0.12 -0.09 -0.12 -0.25 [73,] -0.03 -0.69 0.00 -0.06 -0.16 -0.16 -0.28 [74,] -0.06 -2.86 -0.91 -0.24 -0.24 -0.18 -0.26 [75,] -0.09 -2.88 -1.35 -0.09 -0.45 -0.45 -0.07 [76,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.27 [77,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [78,] -0.03 -0.84 -0.12 -0.09 -0.19 -0.16 -0.28 [79,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [80,] -0.10 -5.84 -1.54 -0.20 -0.72 -0.20 0.06 [81,] -0.03 -0.57 -0.05 -0.10 -0.20 -0.16 -0.28 [82,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [83,] -0.04 -0.94 -0.03 -0.09 -0.13 -0.17 -0.29 [84,] -0.03 -1.14 -0.11 -0.11 -0.08 -0.08 -0.27 [85,] -0.02 -0.34 -0.02 -0.06 -0.02 -0.08 -0.20 [86,] -0.06 -1.31 -0.02 -0.06 -0.36 -0.24 -0.26 [87,] -0.05 -1.59 -0.75 -0.20 -0.25 -0.25 -0.29 [88,] -0.06 -1.73 -0.10 -0.19 -0.32 -0.13 -0.25 [89,] -0.02 -0.47 -0.03 -0.06 -0.02 -0.11 -0.24 [90,] -0.06 -2.31 -0.94 -0.19 -0.06 -0.25 -0.25 [91,] -0.06 -1.97 -0.92 -0.25 -0.18 -0.25 -0.26 [92,] -0.08 -3.07 -0.83 -0.17 -0.42 -0.25 -0.13 [93,] -0.04 -1.52 -0.41 -0.16 -0.21 -0.16 -0.29 [94,] -0.04 -2.26 -0.60 -0.20 -0.20 -0.12 -0.29 [95,] -0.05 -1.26 -0.02 -0.05 -0.14 -0.19 -0.29 [96,] -0.02 -0.99 -0.35 -0.12 -0.02 -0.12 -0.25 [97,] -0.11 -5.99 -1.58 -0.32 -0.63 -0.11 0.09 [98,] -0.08 -2.98 -0.81 -0.08 -0.48 -0.32 -0.14 [99,] -0.06 -2.05 -0.83 -0.17 -0.28 -0.28 -0.28 [100,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [101,] -0.02 -0.63 -0.23 -0.12 -0.02 -0.12 -0.25 [102,] -0.07 -2.47 -0.67 -0.13 -0.40 -0.27 -0.23 [103,] -0.02 -0.36 0.00 -0.06 -0.06 -0.08 -0.20 [104,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [105,] -0.06 -2.30 -0.93 -0.25 -0.37 -0.25 -0.25 [106,] -0.04 -0.86 -0.16 -0.16 -0.24 -0.16 -0.29 [107,] -0.04 -1.13 -0.29 -0.17 -0.21 -0.17 -0.29 [108,] -0.04 -2.07 -0.54 -0.15 -0.18 -0.15 -0.29 [109,] -0.10 -3.34 -1.56 -0.31 -0.63 -0.31 0.08 [110,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [111,] -0.02 -0.63 -0.14 -0.08 -0.02 -0.10 -0.23 [112,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [113,] -0.01 -0.31 -0.01 -0.05 -0.05 -0.05 -0.14 [114,] -0.03 -1.28 -0.30 -0.15 -0.21 -0.12 -0.28 [115,] -0.05 -1.43 -0.37 -0.16 -0.26 -0.21 -0.28 [116,] -0.05 -1.70 -0.69 -0.18 -0.28 -0.23 -0.29 [117,] -0.09 -3.41 -1.38 -0.37 -0.28 -0.18 -0.04 [118,] -0.04 -1.23 -0.38 -0.19 -0.23 -0.15 -0.29 [119,] -0.01 -0.31 -0.01 -0.06 -0.01 -0.07 -0.19 [120,] -0.04 -0.98 -0.25 -0.14 -0.07 -0.14 -0.29 [121,] -0.04 -1.15 -0.30 -0.08 -0.08 -0.21 -0.29 [122,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [123,] -0.07 -2.96 -1.06 -0.28 -0.35 -0.21 -0.21 [124,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [125,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [126,] -0.04 -1.33 -0.54 -0.18 -0.22 -0.18 -0.29 [127,] -0.03 -0.66 0.00 -0.06 -0.12 -0.15 -0.28 [128,] -0.02 -0.45 -0.03 -0.07 -0.08 -0.08 -0.21 [129,] -0.03 -0.90 -0.04 -0.06 -0.17 -0.14 -0.27 [130,] -0.03 -0.88 -0.05 -0.07 -0.20 -0.16 -0.28 [131,] -0.06 -1.61 -0.60 -0.24 -0.06 -0.18 -0.26 [132,] -0.04 -1.70 -0.61 -0.16 -0.24 -0.20 -0.29 [133,] -0.03 -0.88 -0.05 -0.07 -0.20 -0.16 -0.28 [134,] -0.07 -1.97 -0.29 -0.15 -0.44 -0.22 -0.20 [135,] -0.08 -2.44 -0.76 -0.23 -0.38 -0.23 -0.18 [136,] -0.08 -2.57 -1.21 -0.24 -0.40 -0.32 -0.15 [137,] -0.04 -0.77 -0.03 -0.07 -0.21 -0.18 -0.29 [138,] -0.08 -2.81 -1.14 -0.15 -0.08 -0.30 -0.18 [139,] -0.02 -0.59 -0.09 -0.09 -0.11 -0.11 -0.24 [140,] -0.07 -1.77 -0.26 -0.07 -0.33 -0.26 -0.24 [141,] -0.07 -1.84 -0.68 -0.14 -0.07 -0.27 -0.23 [142,] -0.06 -1.80 -0.84 -0.28 -0.34 -0.23 -0.27 [143,] -0.05 -1.23 -0.32 -0.23 -0.23 -0.14 -0.29 [144,] -0.06 -3.37 -0.97 -0.13 -0.32 -0.26 -0.24 [145,] -0.06 -1.61 -0.24 -0.18 -0.36 -0.18 -0.26 [146,] -0.05 -1.93 -0.21 -0.05 -0.26 -0.21 -0.28 [147,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [148,] -0.04 -1.94 -0.56 -0.19 -0.04 -0.11 -0.29 [149,] -0.04 -2.17 -0.57 -0.15 -0.23 -0.15 -0.29 [150,] -0.08 -2.13 -0.55 -0.08 -0.39 -0.32 -0.16 [151,] -0.05 -1.74 -0.33 -0.19 -0.28 -0.14 -0.29 [152,] -0.06 -1.33 -0.05 -0.12 -0.24 -0.18 -0.26 [153,] -0.06 -2.02 -0.25 -0.13 -0.32 -0.19 -0.25 [154,] -0.08 -2.98 -1.21 -0.32 -0.48 -0.24 -0.14 [155,] -0.06 -1.33 -0.05 -0.12 -0.24 -0.18 -0.26 [156,] -0.07 -3.07 -1.10 -0.29 -0.44 -0.22 -0.20 [157,] -0.07 -3.57 -1.03 -0.34 -0.07 -0.07 -0.22 [158,] -0.09 -3.20 -1.30 -0.35 -0.09 -0.17 -0.10 [159,] -0.06 -1.54 -0.40 -0.23 -0.29 -0.17 -0.27 [160,] -0.03 -1.07 -0.13 -0.07 -0.17 -0.17 -0.28 [161,] -0.04 -1.09 -0.16 -0.08 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-0.10 -0.12 -0.12 -0.26 [366,] -0.03 -0.84 -0.12 -0.09 -0.19 -0.16 -0.28 [367,] -0.02 -0.63 -0.14 -0.08 -0.02 -0.10 -0.23 [368,] -0.02 -0.84 -0.23 -0.09 -0.02 -0.11 -0.25 [369,] -0.07 -2.38 -0.74 -0.07 -0.07 -0.30 -0.19 [370,] -0.04 -0.87 -0.16 -0.12 -0.04 -0.16 -0.29 [371,] -0.07 -1.88 -0.49 -0.28 -0.21 -0.14 -0.22 [372,] -0.05 -3.11 -0.82 -0.27 -0.27 -0.11 -0.28 [373,] -0.04 -1.38 -0.30 -0.09 -0.21 -0.21 -0.29 [374,] -0.03 -0.82 -0.05 -0.12 -0.03 -0.09 -0.28 [375,] -0.02 -0.44 -0.03 -0.08 -0.10 -0.10 -0.23 [376,] -0.03 -0.66 -0.04 -0.12 -0.15 -0.12 -0.28 [377,] -0.03 -0.86 -0.19 -0.08 -0.03 -0.13 -0.26 [378,] -0.06 -2.77 -0.88 -0.18 -0.29 -0.24 -0.27 [379,] -0.02 -0.44 -0.01 -0.06 -0.02 -0.10 -0.23 [380,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [381,] -0.05 -1.32 -0.20 -0.05 -0.24 -0.24 -0.29 [382,] -0.03 -1.51 -0.44 -0.12 -0.15 -0.15 -0.27 [383,] -0.03 -1.12 -0.35 -0.14 -0.21 -0.17 -0.29 [384,] -0.05 -2.32 -0.74 -0.20 -0.30 -0.20 -0.29 [385,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [386,] -0.02 -0.41 -0.03 -0.08 -0.08 -0.09 -0.22 [387,] -0.07 -2.28 -0.71 -0.14 -0.36 -0.29 -0.21 [388,] -0.05 -1.03 -0.04 -0.09 -0.23 -0.19 -0.29 [389,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [390,] -0.07 -2.87 -1.02 -0.20 -0.41 -0.27 -0.22 [391,] -0.02 -0.55 -0.14 -0.10 -0.08 -0.10 -0.23 [392,] -0.05 -2.14 -0.76 -0.20 -0.20 -0.20 -0.29 [393,] -0.08 -4.62 -1.22 -0.24 -0.41 -0.16 -0.14 [394,] -0.11 -4.58 -1.64 -0.33 -0.65 -0.22 0.14 [395,] -0.08 -2.72 -0.59 -0.17 -0.08 -0.17 -0.11 [396,] -0.02 -0.39 -0.07 -0.09 -0.07 -0.09 -0.21 [397,] -0.05 -1.05 -0.07 -0.05 -0.29 -0.24 -0.29 [398,] -0.04 -0.90 -0.03 -0.04 -0.16 -0.21 -0.29 [399,] -0.04 -1.33 -0.62 -0.17 -0.04 -0.21 -0.29 [400,] -0.07 -1.46 -0.10 -0.13 -0.33 -0.20 -0.23 [401,] -0.01 -0.35 -0.05 -0.06 -0.03 -0.06 -0.18 [402,] -0.02 -0.46 -0.07 -0.07 -0.02 -0.09 -0.21 [403,] -0.04 -1.77 -0.63 -0.21 -0.21 -0.17 -0.29 [404,] -0.06 -1.84 -0.09 -0.12 -0.40 -0.17 -0.27 [405,] -0.08 -4.60 -1.21 -0.32 -0.24 -0.08 -0.14 [406,] -0.02 -0.79 -0.15 -0.09 -0.11 -0.11 -0.24 [407,] -0.06 -3.37 -0.97 -0.13 -0.32 -0.26 -0.24 [408,] -0.04 -1.66 -0.53 -0.14 -0.21 -0.18 -0.29 [409,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [410,] -0.03 -0.79 -0.21 -0.12 -0.15 -0.15 -0.27 [411,] -0.07 -1.59 -0.29 -0.14 -0.22 -0.22 -0.20 [412,] -0.04 -1.46 -0.28 -0.08 -0.24 -0.20 -0.29 [413,] -0.05 -1.48 -0.38 -0.22 -0.22 -0.16 -0.28 [414,] -0.04 -1.58 -0.38 -0.15 -0.23 -0.15 -0.29 [415,] -0.02 -0.47 -0.03 -0.06 -0.02 -0.11 -0.24 [416,] -0.07 -1.47 -0.27 -0.13 -0.07 -0.20 -0.23 [417,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [418,] -0.08 -2.78 -1.13 -0.30 -0.30 -0.23 -0.18 [419,] -0.04 -1.03 -0.27 -0.11 -0.19 -0.19 -0.29 [420,] -0.05 -0.81 -0.47 -0.19 -0.19 -0.23 -0.29 [421,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [422,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [423,] -0.16 -5.81 -2.36 -0.31 -0.79 -0.16 0.91 [424,] -0.02 -0.38 -0.03 -0.09 -0.02 -0.07 -0.21 [425,] -0.07 -1.76 -0.46 -0.13 -0.33 -0.26 -0.24 [426,] -0.02 -0.59 -0.09 -0.09 -0.11 -0.11 -0.24 [427,] -0.04 -0.82 0.00 -0.04 -0.11 -0.19 -0.29 [428,] -0.03 -0.93 -0.24 -0.14 -0.03 -0.14 -0.29 [429,] -0.07 -2.28 -1.07 -0.36 -0.36 -0.21 -0.21 [430,] -0.05 -1.49 -0.47 -0.19 -0.23 -0.19 -0.29 [431,] -0.09 -2.86 -1.34 -0.18 -0.27 -0.36 -0.07 [432,] -0.03 -0.60 -0.04 -0.08 -0.14 -0.14 -0.27 [433,] -0.03 -0.83 -0.12 -0.12 -0.12 -0.12 -0.28 [434,] -0.02 -0.88 -0.25 -0.08 -0.02 -0.08 -0.21 [435,] -0.09 -2.51 -0.65 -0.19 -0.09 -0.19 -0.04 [436,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [437,] -0.07 -2.88 -1.03 -0.14 -0.07 -0.27 -0.22 [438,] -0.05 -2.23 -0.80 -0.21 -0.27 -0.21 -0.28 [439,] -0.05 -1.42 -0.37 -0.21 -0.16 -0.16 -0.28 [440,] -0.11 -2.93 -0.76 -0.22 -0.65 -0.22 0.13 [441,] -0.08 -3.32 -1.18 -0.24 -0.24 -0.24 -0.16 [442,] -0.03 -0.71 -0.11 -0.08 -0.08 -0.13 -0.26 [443,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [444,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [445,] -0.03 -0.70 -0.10 -0.10 -0.03 -0.10 -0.26 [446,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [447,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [448,] -0.05 -2.22 -0.71 -0.19 -0.24 -0.19 -0.29 [449,] -0.11 -3.97 -1.07 -0.21 -0.64 -0.21 0.12 [450,] -0.07 -2.69 -1.09 -0.22 -0.36 -0.29 -0.20 [451,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [452,] 0.17 4.66 0.26 0.52 0.69 0.69 1.02 [453,] 0.20 5.40 0.80 0.60 0.20 1.00 1.72 [454,] 0.12 4.46 1.81 0.60 0.72 0.24 -0.01 [455,] 0.18 5.66 1.77 0.53 0.88 0.35 1.12 [456,] 0.19 4.28 0.02 0.78 0.97 0.97 1.58 [457,] 0.15 3.37 0.23 0.31 0.15 0.77 0.59 [458,] 0.17 6.44 2.61 0.70 0.87 0.35 1.06 [459,] 0.20 4.36 0.30 0.40 0.59 0.79 1.67 [460,] 0.04 1.47 0.60 0.08 0.24 0.16 -0.89 [461,] 0.04 1.22 0.57 0.15 0.11 0.08 -0.90 [462,] 0.02 0.63 0.26 0.07 0.07 0.03 -0.98 [463,] 0.18 7.36 2.63 0.53 0.18 0.70 1.09 [464,] 0.18 7.69 2.75 0.92 0.73 0.18 1.28 [465,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [466,] 0.16 4.97 2.33 0.47 0.16 0.31 0.64 [467,] 0.13 3.42 0.51 0.13 0.76 0.63 0.09 [468,] 0.12 4.28 1.16 0.23 0.81 0.35 -0.09 [469,] 0.24 6.47 0.96 0.72 1.20 1.20 2.91 [470,] 0.13 5.30 1.89 0.50 0.63 0.63 0.08 [471,] 0.17 7.94 2.53 0.84 0.68 0.84 0.94 [472,] 0.21 5.57 0.82 0.62 1.03 0.82 1.89 [473,] 0.16 4.38 1.14 0.81 0.16 0.65 0.79 [474,] 0.30 8.10 0.45 0.90 1.50 1.20 5.12 [475,] 0.21 5.62 1.46 0.83 1.25 0.42 1.94 [476,] 0.12 5.13 1.83 0.49 0.61 0.49 0.01 [477,] 0.14 3.75 1.39 0.56 0.97 0.42 0.31 [478,] 0.05 1.25 0.07 0.09 0.23 0.09 -0.85 [479,] 0.15 4.83 0.60 0.60 0.91 0.60 0.55 [480,] 0.08 2.13 0.55 0.24 0.08 0.24 -0.58 [481,] 0.16 4.98 1.56 0.62 0.16 0.62 0.65 [482,] 0.05 1.24 0.18 0.09 0.32 0.09 -0.86 [483,] 0.21 3.63 0.16 1.04 0.83 1.04 1.92 [484,] 0.16 5.11 1.60 0.64 0.16 0.80 0.74 [485,] 0.17 5.29 1.16 0.33 0.99 0.66 0.86 [486,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [487,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [488,] 0.25 8.09 2.53 0.51 1.26 1.26 3.34 [489,] 0.15 7.95 2.29 0.31 0.92 0.61 0.59 [490,] 0.09 3.87 1.38 0.09 0.09 0.28 -0.42 [491,] 0.03 1.61 0.47 0.06 0.19 0.09 -0.93 [492,] 0.10 3.63 1.47 0.29 0.59 0.49 -0.34 [493,] 0.28 6.07 1.10 0.83 0.83 1.10 4.18 [494,] 0.13 3.61 0.94 0.13 0.80 0.27 0.21 [495,] 0.15 4.09 0.61 0.45 0.76 0.76 0.56 [496,] 0.30 13.93 4.45 1.19 1.78 1.48 4.97 [497,] 0.17 7.26 2.59 0.69 0.17 0.17 1.03 [498,] 0.22 5.82 0.86 0.65 0.65 0.86 2.16 [499,] 0.26 8.26 1.81 1.03 1.03 1.29 3.53 [500,] 0.17 5.44 0.07 0.51 0.51 0.68 0.96 [501,] 0.16 7.52 2.40 0.80 0.80 0.64 0.74 [502,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [503,] 0.16 3.59 0.65 0.33 0.98 0.65 0.81 [504,] 0.13 3.55 0.53 0.26 0.53 0.66 0.18 [505,] 0.22 11.31 3.26 1.09 0.22 0.65 2.22 [506,] 0.09 2.54 0.38 0.28 0.28 0.28 -0.40 [507,] 0.11 3.05 1.13 0.45 0.11 0.45 -0.13 [508,] 0.10 3.11 0.68 0.29 0.68 0.39 -0.36 [509,] 0.07 2.37 0.52 0.15 0.30 0.07 -0.63 [510,] 0.05 1.04 0.07 0.05 0.14 0.09 -0.85 [511,] 0.19 4.14 0.75 0.56 1.13 0.75 1.41 [512,] 0.18 7.39 2.64 0.70 1.06 0.70 1.11 [513,] 0.07 4.13 1.09 0.07 0.36 0.29 -0.64 [514,] 0.15 4.87 0.61 0.46 0.76 0.30 0.57 [515,] 0.07 1.97 0.29 0.07 0.29 0.29 -0.64 [516,] 0.24 7.64 1.67 0.96 0.24 0.96 2.88 [517,] 0.08 4.41 1.16 0.08 0.31 0.31 -0.59 [518,] 0.11 4.77 1.70 0.45 0.57 0.23 -0.12 [519,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [520,] -0.01 -0.35 -0.13 -0.03 -0.05 -0.01 -1.00 [521,] 0.10 5.15 1.48 0.30 0.40 0.40 -0.33 [522,] 0.07 2.01 0.52 0.22 0.37 0.22 -0.62 [523,] 0.18 5.80 1.27 0.36 0.72 0.36 1.23 [524,] 0.18 3.90 0.71 0.71 0.35 0.89 1.14 [525,] 0.12 3.19 0.83 0.35 0.71 0.47 -0.05 [526,] 0.20 7.41 3.01 0.20 1.00 1.00 1.73 [527,] 0.12 3.69 1.73 0.35 0.12 0.35 -0.10 [528,] 0.19 5.14 1.33 0.38 0.95 0.95 1.47 [529,] 0.07 2.34 0.51 0.22 0.37 0.22 -0.64 [530,] 0.14 4.51 0.21 0.28 0.28 0.56 0.35 [531,] 0.21 8.67 3.10 0.83 0.21 0.41 1.90 [532,] 0.17 5.45 1.70 0.51 0.85 0.68 0.98 [533,] 0.14 5.35 0.58 0.14 0.87 0.43 0.42 [534,] 0.06 1.61 0.24 0.12 0.30 0.18 -0.76 [535,] 0.20 8.43 3.01 0.60 0.80 0.60 1.74 [536,] 0.15 3.98 1.47 0.74 0.88 0.74 0.48 [537,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [538,] 0.18 4.90 1.27 0.18 0.54 0.54 1.24 [539,] 0.10 2.69 0.70 0.40 0.10 0.20 -0.32 [540,] 0.09 2.93 0.92 0.18 0.37 0.37 -0.43 [541,] 0.24 4.23 0.18 0.48 0.24 0.73 2.98 [542,] 0.20 6.50 3.05 0.61 1.02 0.81 1.81 [543,] 0.09 2.00 0.64 0.36 0.36 0.27 -0.44 [544,] 0.16 5.13 1.12 0.64 0.96 0.80 0.75 [545,] 0.11 2.95 0.44 0.22 0.66 0.22 -0.19 [546,] 0.14 3.10 0.21 0.71 0.71 0.42 0.35 [547,] 0.10 3.28 1.54 0.31 0.51 0.10 -0.29 [548,] 0.16 6.58 2.35 0.31 0.16 0.31 0.67 [549,] 0.16 6.55 2.34 0.47 0.78 0.62 0.66 [550,] 0.16 5.02 1.57 0.31 0.63 0.31 0.67 [551,] 0.12 3.89 1.82 0.36 0.12 0.12 0.01 [552,] 0.28 16.16 4.25 1.42 1.13 1.42 4.47 [553,] 0.26 12.35 3.94 1.05 1.58 1.05 3.70 [554,] 0.02 0.81 0.29 0.04 0.12 0.06 -0.97 [555,] 0.18 6.59 2.67 0.53 1.07 0.53 1.15 [556,] 0.20 7.35 2.98 0.99 0.99 0.40 1.69 [557,] 0.13 3.45 1.28 0.26 0.77 0.51 0.11 [558,] 0.04 1.65 0.67 0.09 0.22 0.18 -0.86 [559,] 0.09 2.77 1.30 0.09 0.43 0.17 -0.49 [560,] 0.13 4.23 1.32 0.40 0.79 0.40 0.19 [561,] -0.01 -0.25 -0.10 -0.03 -0.03 -0.01 -1.00 [562,] 0.24 6.35 0.35 0.47 1.18 1.18 2.77 [563,] 0.02 0.87 0.28 0.04 0.09 0.04 -0.98 [564,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [565,] 0.29 7.79 1.15 0.58 1.44 1.44 4.66 [566,] 0.16 4.35 1.61 0.65 0.16 0.81 0.77 [567,] 0.09 1.99 0.36 0.27 0.09 0.27 -0.45 [568,] 0.38 19.73 2.66 1.52 1.90 1.90 8.80 [569,] 0.13 3.41 0.50 0.13 0.38 0.63 0.08 [570,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [571,] -0.01 -0.22 -0.03 -0.01 -0.02 -0.01 -1.00 [572,] 0.30 5.24 0.22 0.60 0.90 1.50 5.10 [573,] 0.18 5.74 2.69 0.90 0.90 0.72 1.19 [574,] 0.19 4.10 0.75 0.19 0.56 0.93 1.37 [575,] 0.17 5.31 0.66 0.66 0.99 0.66 0.87 [576,] 0.06 1.27 0.09 0.17 0.29 0.12 -0.77 [577,] 0.07 3.04 1.09 0.14 0.36 0.29 -0.64 [578,] 0.14 4.36 0.95 0.54 0.54 0.54 0.26 [579,] 0.14 5.34 2.17 0.43 0.87 0.29 0.42 [580,] 0.03 1.24 0.44 0.09 0.18 0.09 -0.94 [581,] 0.07 1.84 0.27 0.07 0.34 0.27 -0.68 [582,] 0.05 1.89 0.76 0.20 0.36 0.15 -0.82 [583,] 0.14 5.11 2.07 0.41 0.83 0.55 0.30 [584,] 0.11 2.33 0.16 0.21 0.32 0.32 -0.24 [585,] 0.09 2.83 0.35 0.27 0.53 0.18 -0.47 [586,] 0.13 4.31 2.02 0.67 0.81 0.67 0.23 [587,] 0.24 12.48 3.60 0.24 1.20 1.20 2.92 [588,] 0.22 10.43 3.33 0.22 1.33 1.11 2.35 [589,] 0.10 3.21 1.51 0.40 0.40 0.40 -0.31 [590,] 0.07 2.31 1.08 0.22 0.22 0.14 -0.65 [591,] 0.16 4.28 1.11 0.63 0.16 0.32 0.71 [592,] 0.16 6.62 2.36 0.47 0.95 0.32 0.69 [593,] 0.07 3.09 1.10 0.15 0.22 0.15 -0.63 [594,] 0.23 6.22 1.61 0.46 1.15 0.92 2.61 [595,] 0.11 3.44 1.08 0.43 0.43 0.32 -0.21 [596,] 0.11 5.02 1.60 0.32 0.43 0.21 -0.22 [597,] 0.12 2.72 0.19 0.12 0.25 0.62 0.04 [598,] 0.15 4.66 1.46 0.29 0.73 0.58 0.44 [599,] 0.10 3.23 1.01 0.20 0.61 0.50 -0.31 [600,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 [601,] 0.09 3.02 1.41 0.28 0.09 0.47 -0.40 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 150 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsAdd ~ age + yearsmarried + religiousness + occupation + rating, left = 5, data = Affairs) $terms affairsAdd ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairsAdd, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairsAdd 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairsAdd, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairsAdd age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 451 150 0 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation 10.608161 -0.050347 0.161852 -0.476324 0.106006 rating logSigma -0.712242 2.244542 $left [1] 5 $right [1] Inf class [1] "censReg" "maxLik" "maxim" "list" print( x, digits = 2 ) Call: censReg(formula = affairsAdd ~ age + yearsmarried + religiousness + occupation + rating, left = 5, data = Affairs) Coefficients: (Intercept) age yearsmarried religiousness occupation 13.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating -0.04 0.13 -0.39 0.08 -0.53 printME( margEff( x ) ) age yearsmarried religiousness occupation rating -0.042 0.130 -0.394 0.076 -0.534 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0.000 0.000 0.000 0.000 yearsmarried 0 0.001 0.000 0.000 0.000 religiousness 0 0.000 0.009 0.000 0.000 occupation 0 0.000 0.000 0.004 0.000 rating 0 0.000 0.000 0.000 0.009 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.042 0.018 -2.3 0.02 * yearsmarried 0.130 0.031 4.2 4e-05 *** religiousness -0.394 0.093 -4.2 3e-05 *** occupation 0.076 0.059 1.3 0.20 rating -0.534 0.095 -5.6 3e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-Likelihood: -705.58 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 13.174 2.741 4.8 2e-06 *** age -0.179 0.079 -2.3 0.02 * yearsmarried 0.554 0.135 4.1 4e-05 *** religiousness -1.686 0.404 -4.2 3e-05 *** occupation 0.326 0.254 1.3 0.20 rating -2.285 0.408 -5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsAdd ~ age + yearsmarried + religiousness + occupation + rating, left = 5, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 150 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 13.174 2.741 4.8 2e-06 *** age -0.179 0.079 -2.3 0.02 * yearsmarried 0.554 0.135 4.1 4e-05 *** religiousness -1.686 0.404 -4.2 3e-05 *** occupation 0.326 0.254 1.3 0.20 rating -2.285 0.408 -5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -705.58 on 7 Df > round( coef( estResultAdd ), 2 ) (Intercept) age yearsmarried religiousness occupation 13.17 -0.18 0.55 -1.69 0.33 rating logSigma -2.28 2.11 > round( coef( estResultAdd, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation 13.17 -0.18 0.55 -1.69 0.33 rating sigma -2.28 8.25 > round( vcov( estResultAdd ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.52 -0.12 0.09 -0.40 -0.18 -0.62 age -0.12 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.09 -0.01 0.02 -0.01 0.00 0.00 religiousness -0.40 0.00 -0.01 0.16 0.01 0.00 occupation -0.18 0.00 0.00 0.01 0.06 -0.01 rating -0.62 0.00 0.00 0.00 -0.01 0.17 logSigma 0.01 0.00 0.00 0.00 0.00 -0.01 logSigma (Intercept) 0.01 age 0.00 yearsmarried 0.00 religiousness 0.00 occupation 0.00 rating -0.01 logSigma 0.00 > round( vcov( estResultAdd, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.52 -0.12 0.09 -0.40 -0.18 -0.62 age -0.12 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.09 -0.01 0.02 -0.01 0.00 0.00 religiousness -0.40 0.00 -0.01 0.16 0.01 0.00 occupation -0.18 0.00 0.00 0.01 0.06 -0.01 rating -0.62 0.00 0.00 0.00 -0.01 0.17 sigma 0.06 0.00 0.01 -0.04 0.01 -0.06 sigma (Intercept) 0.06 age 0.00 yearsmarried 0.01 religiousness -0.04 occupation 0.01 rating -0.06 sigma 0.31 > logLik( estResultAdd ) 'log Lik.' -705.58 (df=7) > nobs( estResultAdd ) [1] 601 > extractAIC( estResultAdd ) [1] 7.0 1425.2 > > ## estimation with right-censoring > Affairs$affairsNeg <- - Affairs$affairs > estResultNeg <- censReg( affairsNeg ~ age + yearsmarried + religiousness + + occupation + rating, data = Affairs, left = -Inf, right = 0 ) > printAll( estResultNeg, meCalcVCov = FALSE, meReturnJacobian = TRUE ) $maximum [1] -705.58 $estimate (Intercept) age yearsmarried religiousness occupation -8.17 0.18 -0.55 1.69 -0.33 rating logSigma 2.28 2.11 $gradient (Intercept) age yearsmarried religiousness occupation 0 0 0 0 0 rating logSigma 0 0 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.0 -165.5 -44.9 -14.8 -21.3 -18.5 age -165.5 -5890.2 -1675.4 -500.7 -717.1 -602.6 yearsmarried -44.9 -1675.4 -550.7 -140.7 -193.4 -159.0 religiousness -14.8 -500.7 -140.7 -50.8 -62.7 -54.8 occupation -21.3 -717.1 -193.4 -62.7 -106.7 -78.9 rating -18.5 -602.6 -159.0 -54.8 -78.9 -74.9 logSigma 37.1 1206.3 301.5 116.1 155.3 148.5 logSigma (Intercept) 37.1 age 1206.3 yearsmarried 301.5 religiousness 116.1 occupation 155.3 rating 148.5 logSigma -530.5 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "Newton-Raphson maximisation" $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] 0.06 2.09 0.56 0.17 0.40 0.23 -0.27 [2,] 0.03 0.92 0.14 0.14 0.20 0.14 -0.29 [3,] 0.10 3.17 1.49 0.10 0.10 0.40 0.02 [4,] 0.02 1.11 0.29 0.10 0.12 0.10 -0.23 [5,] 0.07 1.44 0.05 0.13 0.39 0.20 -0.24 [6,] 0.03 0.85 0.04 0.05 0.13 0.13 -0.26 [7,] 0.05 1.18 0.04 0.11 0.05 0.16 -0.28 [8,] 0.06 3.18 0.84 0.11 0.22 0.22 -0.28 [9,] 0.09 3.02 1.42 0.38 0.09 0.19 -0.02 [10,] 0.02 0.41 0.03 0.08 0.08 0.09 -0.22 [11,] 0.14 5.15 2.09 0.28 0.97 0.28 0.58 [12,] 0.03 0.92 0.14 0.14 0.20 0.14 -0.29 [13,] 0.04 1.82 0.58 0.19 0.23 0.16 -0.29 [14,] 0.05 1.09 0.07 0.10 0.25 0.20 -0.29 [15,] 0.03 0.88 0.13 0.13 0.16 0.13 -0.28 [16,] 0.08 3.03 1.23 0.08 0.41 0.41 -0.13 [17,] 0.11 3.91 1.58 0.21 0.42 0.32 0.10 [18,] 0.04 0.81 0.03 0.11 0.18 0.15 -0.29 [19,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [20,] 0.05 1.38 0.51 0.10 0.05 0.26 -0.29 [21,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [22,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [23,] 0.05 1.42 0.53 0.21 0.26 0.21 -0.28 [24,] 0.04 1.13 0.35 0.11 0.04 0.18 -0.29 [25,] 0.04 1.60 0.17 0.09 0.26 0.17 -0.29 [26,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [27,] 0.02 0.63 0.16 0.09 0.02 0.12 -0.25 [28,] 0.04 1.86 0.66 0.22 0.27 0.18 -0.29 [29,] 0.03 0.79 0.12 0.09 0.15 0.15 -0.27 [30,] 0.04 1.13 0.17 0.13 0.21 0.17 -0.29 [31,] 0.07 3.07 1.10 0.29 0.44 0.22 -0.20 [32,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [33,] 0.02 0.66 0.01 0.10 0.15 0.10 -0.25 [34,] 0.04 1.77 0.63 0.21 0.21 0.17 -0.29 [35,] 0.06 1.95 0.24 0.06 0.37 0.24 -0.26 [36,] 0.04 0.94 0.06 0.17 0.21 0.13 -0.29 [37,] 0.06 2.35 0.84 0.17 0.06 0.22 -0.27 [38,] 0.03 0.57 0.10 0.10 0.13 0.13 -0.26 [39,] 0.04 0.92 0.06 0.04 0.12 0.21 -0.29 [40,] 0.02 0.44 0.01 0.06 0.02 0.10 -0.23 [41,] 0.03 0.85 0.26 0.13 0.16 0.13 -0.26 [42,] 0.05 2.46 0.71 0.24 0.28 0.14 -0.29 [43,] 0.02 0.34 0.01 0.08 0.02 0.06 -0.20 [44,] 0.11 3.10 0.46 0.23 0.69 0.11 0.21 [45,] 0.04 1.28 0.28 0.20 0.20 0.12 -0.29 [46,] 0.03 0.75 0.14 0.10 0.17 0.17 -0.29 [47,] 0.03 0.83 0.22 0.12 0.19 0.15 -0.28 [48,] 0.08 3.34 1.19 0.16 0.40 0.32 -0.15 [49,] 0.01 0.40 0.02 0.06 0.04 0.07 -0.19 [50,] 0.08 3.46 1.23 0.16 0.49 0.33 -0.13 [51,] 0.01 0.25 0.01 0.06 0.03 0.06 -0.16 [52,] 0.06 1.95 0.43 0.12 0.37 0.24 -0.26 [53,] 0.02 0.46 0.07 0.08 0.10 0.08 -0.21 [54,] 0.05 1.48 0.55 0.22 0.33 0.22 -0.28 [55,] 0.06 1.21 0.22 0.06 0.28 0.28 -0.28 [56,] 0.11 4.23 1.71 0.46 0.34 0.11 0.21 [57,] 0.02 0.46 0.03 0.11 0.08 0.08 -0.24 [58,] 0.04 1.34 0.54 0.15 0.04 0.18 -0.29 [59,] 0.02 0.65 0.02 0.10 0.12 0.10 -0.25 [60,] 0.05 1.56 0.49 0.20 0.29 0.20 -0.29 [61,] 0.07 3.44 1.10 0.37 0.51 0.15 -0.20 [62,] 0.05 2.00 0.54 0.16 0.32 0.22 -0.28 [63,] 0.03 0.73 0.03 0.07 0.17 0.17 -0.28 [64,] 0.04 0.99 0.15 0.07 0.15 0.18 -0.29 [65,] 0.04 1.23 0.27 0.15 0.23 0.15 -0.29 [66,] 0.06 2.36 0.84 0.11 0.17 0.28 -0.27 [67,] 0.04 1.59 0.43 0.17 0.26 0.17 -0.29 [68,] 0.05 2.13 0.68 0.14 0.27 0.23 -0.29 [69,] 0.01 0.31 0.02 0.07 0.07 0.07 -0.19 [70,] 0.05 1.24 0.07 0.09 0.28 0.18 -0.29 [71,] 0.03 0.79 0.12 0.09 0.15 0.15 -0.27 [72,] 0.02 0.76 0.24 0.12 0.09 0.12 -0.25 [73,] 0.03 0.69 0.00 0.06 0.16 0.16 -0.28 [74,] 0.06 2.86 0.91 0.24 0.24 0.18 -0.26 [75,] 0.09 2.88 1.35 0.09 0.45 0.45 -0.07 [76,] 0.03 0.79 0.21 0.12 0.15 0.15 -0.27 [77,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [78,] 0.03 0.84 0.12 0.09 0.19 0.16 -0.28 [79,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [80,] 0.10 5.84 1.54 0.20 0.72 0.20 0.06 [81,] 0.03 0.57 0.05 0.10 0.20 0.16 -0.28 [82,] 0.03 1.50 0.40 0.11 0.16 0.13 -0.26 [83,] 0.04 0.94 0.03 0.09 0.13 0.17 -0.29 [84,] 0.03 1.14 0.11 0.11 0.08 0.08 -0.27 [85,] 0.02 0.34 0.02 0.06 0.02 0.08 -0.20 [86,] 0.06 1.31 0.02 0.06 0.36 0.24 -0.26 [87,] 0.05 1.59 0.75 0.20 0.25 0.25 -0.29 [88,] 0.06 1.73 0.10 0.19 0.32 0.13 -0.25 [89,] 0.02 0.47 0.03 0.06 0.02 0.11 -0.24 [90,] 0.06 2.31 0.94 0.19 0.06 0.25 -0.25 [91,] 0.06 1.97 0.92 0.25 0.18 0.25 -0.26 [92,] 0.08 3.07 0.83 0.17 0.42 0.25 -0.13 [93,] 0.04 1.52 0.41 0.16 0.21 0.16 -0.29 [94,] 0.04 2.26 0.60 0.20 0.20 0.12 -0.29 [95,] 0.05 1.26 0.02 0.05 0.14 0.19 -0.29 [96,] 0.02 0.99 0.35 0.12 0.02 0.12 -0.25 [97,] 0.11 5.99 1.58 0.32 0.63 0.11 0.09 [98,] 0.08 2.98 0.81 0.08 0.48 0.32 -0.14 [99,] 0.06 2.05 0.83 0.17 0.28 0.28 -0.28 [100,] 0.05 1.70 0.69 0.18 0.28 0.23 -0.29 [101,] 0.02 0.63 0.23 0.12 0.02 0.12 -0.25 [102,] 0.07 2.47 0.67 0.13 0.40 0.27 -0.23 [103,] 0.02 0.36 0.00 0.06 0.06 0.08 -0.20 [104,] 0.02 1.11 0.29 0.10 0.12 0.10 -0.23 [105,] 0.06 2.30 0.93 0.25 0.37 0.25 -0.25 [106,] 0.04 0.86 0.16 0.16 0.24 0.16 -0.29 [107,] 0.04 1.13 0.29 0.17 0.21 0.17 -0.29 [108,] 0.04 2.07 0.54 0.15 0.18 0.15 -0.29 [109,] 0.10 3.34 1.56 0.31 0.63 0.31 0.08 [110,] 0.05 1.09 0.07 0.10 0.25 0.20 -0.29 [111,] 0.02 0.63 0.14 0.08 0.02 0.10 -0.23 [112,] 0.05 1.70 0.69 0.18 0.28 0.23 -0.29 [113,] 0.01 0.31 0.01 0.05 0.05 0.05 -0.14 [114,] 0.03 1.28 0.30 0.15 0.21 0.12 -0.28 [115,] 0.05 1.43 0.37 0.16 0.26 0.21 -0.28 [116,] 0.05 1.70 0.69 0.18 0.28 0.23 -0.29 [117,] 0.09 3.41 1.38 0.37 0.28 0.18 -0.04 [118,] 0.04 1.23 0.38 0.19 0.23 0.15 -0.29 [119,] 0.01 0.31 0.01 0.06 0.01 0.07 -0.19 [120,] 0.04 0.98 0.25 0.14 0.07 0.14 -0.29 [121,] 0.04 1.15 0.30 0.08 0.08 0.21 -0.29 [122,] 0.04 1.77 0.63 0.21 0.21 0.17 -0.29 [123,] 0.07 2.96 1.06 0.28 0.35 0.21 -0.21 [124,] 0.09 2.51 0.65 0.19 0.09 0.19 -0.04 [125,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [126,] 0.04 1.33 0.54 0.18 0.22 0.18 -0.29 [127,] 0.03 0.66 0.00 0.06 0.12 0.15 -0.28 [128,] 0.02 0.45 0.03 0.07 0.08 0.08 -0.21 [129,] 0.03 0.90 0.04 0.06 0.17 0.14 -0.27 [130,] 0.03 0.88 0.05 0.07 0.20 0.16 -0.28 [131,] 0.06 1.61 0.60 0.24 0.06 0.18 -0.26 [132,] 0.04 1.70 0.61 0.16 0.24 0.20 -0.29 [133,] 0.03 0.88 0.05 0.07 0.20 0.16 -0.28 [134,] 0.07 1.97 0.29 0.15 0.44 0.22 -0.20 [135,] 0.08 2.44 0.76 0.23 0.38 0.23 -0.18 [136,] 0.08 2.57 1.21 0.24 0.40 0.32 -0.15 [137,] 0.04 0.77 0.03 0.07 0.21 0.18 -0.29 [138,] 0.08 2.81 1.14 0.15 0.08 0.30 -0.18 [139,] 0.02 0.59 0.09 0.09 0.11 0.11 -0.24 [140,] 0.07 1.77 0.26 0.07 0.33 0.26 -0.24 [141,] 0.07 1.84 0.68 0.14 0.07 0.27 -0.23 [142,] 0.06 1.80 0.84 0.28 0.34 0.23 -0.27 [143,] 0.05 1.23 0.32 0.23 0.23 0.14 -0.29 [144,] 0.06 3.37 0.97 0.13 0.32 0.26 -0.24 [145,] 0.06 1.61 0.24 0.18 0.36 0.18 -0.26 [146,] 0.05 1.93 0.21 0.05 0.26 0.21 -0.28 [147,] 0.03 0.88 0.13 0.13 0.16 0.13 -0.28 [148,] 0.04 1.94 0.56 0.19 0.04 0.11 -0.29 [149,] 0.04 2.17 0.57 0.15 0.23 0.15 -0.29 [150,] 0.08 2.13 0.55 0.08 0.39 0.32 -0.16 [151,] 0.05 1.74 0.33 0.19 0.28 0.14 -0.29 [152,] 0.06 1.33 0.05 0.12 0.24 0.18 -0.26 [153,] 0.06 2.02 0.25 0.13 0.32 0.19 -0.25 [154,] 0.08 2.98 1.21 0.32 0.48 0.24 -0.14 [155,] 0.06 1.33 0.05 0.12 0.24 0.18 -0.26 [156,] 0.07 3.07 1.10 0.29 0.44 0.22 -0.20 [157,] 0.07 3.57 1.03 0.34 0.07 0.07 -0.22 [158,] 0.09 3.20 1.30 0.35 0.09 0.17 -0.10 [159,] 0.06 1.54 0.40 0.23 0.29 0.17 -0.27 [160,] 0.03 1.07 0.13 0.07 0.17 0.17 -0.28 [161,] 0.04 1.09 0.16 0.08 0.24 0.20 -0.29 [162,] 0.04 1.04 0.15 0.08 0.19 0.19 -0.29 [163,] 0.04 1.33 0.54 0.18 0.22 0.18 -0.29 [164,] 0.04 1.73 0.55 0.18 0.18 0.15 -0.29 [165,] 0.05 1.57 0.49 0.15 0.05 0.20 -0.29 [166,] 0.04 1.16 0.06 0.17 0.04 0.09 -0.29 [167,] 0.10 5.49 1.44 0.19 0.48 0.19 0.00 [168,] 0.03 0.66 0.04 0.12 0.15 0.12 -0.28 [169,] 0.06 2.55 0.91 0.18 0.18 0.24 -0.26 [170,] 0.06 3.40 0.90 0.24 0.12 0.12 -0.26 [171,] 0.03 1.50 0.40 0.11 0.16 0.13 -0.26 [172,] 0.02 0.36 0.00 0.06 0.06 0.08 -0.20 [173,] 0.03 0.86 0.27 0.11 0.03 0.13 -0.26 [174,] 0.07 2.76 0.98 0.20 0.33 0.26 -0.24 [175,] 0.03 0.88 0.05 0.07 0.20 0.16 -0.28 [176,] 0.06 2.05 0.01 0.13 0.32 0.13 -0.24 [177,] 0.04 1.13 0.17 0.13 0.21 0.17 -0.29 [178,] 0.07 1.84 0.68 0.14 0.07 0.27 -0.23 [179,] 0.04 1.35 0.30 0.17 0.04 0.13 -0.29 [180,] 0.06 2.21 0.89 0.24 0.30 0.24 -0.26 [181,] 0.08 3.28 1.17 0.39 0.47 0.16 -0.16 [182,] 0.01 0.48 0.02 0.06 0.09 0.07 -0.19 [183,] 0.05 1.63 0.20 0.15 0.25 0.15 -0.29 [184,] 0.02 0.79 0.15 0.09 0.11 0.11 -0.24 [185,] 0.02 0.48 0.01 0.07 0.07 0.11 -0.24 [186,] 0.02 0.63 0.16 0.09 0.02 0.12 -0.25 [187,] 0.02 0.58 0.02 0.06 0.11 0.11 -0.24 [188,] 0.04 1.04 0.15 0.08 0.19 0.19 -0.29 [189,] 0.03 1.01 0.31 0.13 0.13 0.16 -0.28 [190,] 0.09 2.88 1.35 0.09 0.45 0.45 -0.07 [191,] 0.02 0.52 0.02 0.07 0.10 0.12 -0.25 [192,] 0.03 0.93 0.24 0.14 0.03 0.14 -0.29 [193,] 0.02 0.62 0.01 0.09 0.12 0.09 -0.25 [194,] 0.06 2.21 0.89 0.24 0.30 0.24 -0.26 [195,] 0.10 3.56 1.44 0.19 0.10 0.29 0.00 [196,] 0.07 1.60 0.29 0.07 0.36 0.29 -0.20 [197,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [198,] 0.03 0.75 0.05 0.07 0.14 0.17 -0.29 [199,] 0.08 4.01 1.16 0.31 0.46 0.15 -0.17 [200,] 0.02 0.44 0.03 0.08 0.10 0.10 -0.23 [201,] 0.01 0.37 0.05 0.06 0.03 0.06 -0.16 [202,] 0.03 0.96 0.12 0.06 0.09 0.15 -0.28 [203,] 0.03 0.63 0.04 0.09 0.17 0.14 -0.27 [204,] 0.05 1.40 0.04 0.10 0.16 0.16 -0.28 [205,] 0.11 2.51 0.80 0.23 0.57 0.23 0.21 [206,] 0.06 1.52 0.04 0.11 0.28 0.17 -0.27 [207,] 0.09 3.20 1.30 0.35 0.09 0.17 -0.10 [208,] 0.04 0.83 0.06 0.04 0.04 0.19 -0.29 [209,] 0.06 2.28 0.62 0.12 0.25 0.25 -0.25 [210,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [211,] 0.08 3.32 1.18 0.24 0.24 0.24 -0.16 [212,] 0.04 0.96 0.18 0.09 0.22 0.22 -0.29 [213,] 0.07 3.56 0.48 0.14 0.41 0.14 -0.22 [214,] 0.03 0.78 0.02 0.06 0.14 0.14 -0.27 [215,] 0.04 0.99 0.15 0.07 0.15 0.18 -0.29 [216,] 0.01 0.29 0.01 0.03 0.04 0.03 -0.11 [217,] 0.02 0.47 0.03 0.09 0.13 0.11 -0.24 [218,] 0.05 1.14 0.21 0.21 0.26 0.15 -0.29 [219,] 0.07 1.60 0.29 0.07 0.36 0.29 -0.20 [220,] 0.03 1.20 0.49 0.16 0.13 0.16 -0.28 [221,] 0.07 2.65 0.72 0.21 0.43 0.21 -0.21 [222,] 0.04 1.70 0.61 0.16 0.24 0.20 -0.29 [223,] 0.03 1.58 0.50 0.13 0.17 0.17 -0.29 [224,] 0.03 0.66 0.04 0.12 0.15 0.12 -0.28 [225,] 0.05 1.57 0.49 0.15 0.05 0.20 -0.29 [226,] 0.06 1.37 0.44 0.06 0.19 0.31 -0.25 [227,] 0.05 1.49 0.47 0.19 0.23 0.19 -0.29 [228,] 0.04 1.02 0.06 0.08 0.08 0.15 -0.29 [229,] 0.04 1.63 0.66 0.18 0.22 0.22 -0.29 [230,] 0.02 0.71 0.07 0.05 0.07 0.08 -0.21 [231,] 0.05 1.77 0.72 0.24 0.24 0.19 -0.29 [232,] 0.03 0.80 0.18 0.10 0.13 0.13 -0.26 [233,] 0.04 1.70 0.61 0.16 0.24 0.20 -0.29 [234,] 0.03 0.92 0.14 0.14 0.20 0.14 -0.29 [235,] 0.02 0.43 0.01 0.08 0.12 0.10 -0.23 [236,] 0.05 1.23 0.18 0.18 0.23 0.14 -0.29 [237,] 0.01 0.22 0.01 0.05 0.01 0.05 -0.15 [238,] 0.02 1.12 0.32 0.11 0.11 0.11 -0.24 [239,] 0.04 1.37 0.43 0.13 0.21 0.21 -0.29 [240,] 0.06 2.12 0.86 0.23 0.23 0.23 -0.27 [241,] 0.06 1.87 0.41 0.12 0.29 0.23 -0.27 [242,] 0.06 2.35 0.84 0.17 0.06 0.22 -0.27 [243,] 0.09 2.88 1.35 0.09 0.45 0.45 -0.07 [244,] 0.03 0.79 0.12 0.09 0.15 0.15 -0.27 [245,] 0.06 1.97 0.92 0.25 0.18 0.25 -0.26 [246,] 0.03 0.69 0.02 0.09 0.06 0.13 -0.28 [247,] 0.05 1.18 0.08 0.16 0.27 0.16 -0.28 [248,] 0.03 1.47 0.52 0.14 0.10 0.17 -0.29 [249,] 0.05 2.73 0.79 0.16 0.26 0.21 -0.28 [250,] 0.05 1.85 0.75 0.25 0.30 0.20 -0.29 [251,] 0.06 2.64 0.84 0.22 0.11 0.17 -0.27 [252,] 0.06 3.45 0.91 0.12 0.36 0.24 -0.26 [253,] 0.03 0.80 0.18 0.10 0.13 0.13 -0.26 [254,] 0.03 0.93 0.24 0.14 0.03 0.14 -0.29 [255,] 0.05 1.05 0.07 0.05 0.29 0.24 -0.29 [256,] 0.04 0.87 0.16 0.12 0.04 0.16 -0.29 [257,] 0.03 0.64 0.04 0.06 0.03 0.14 -0.27 [258,] 0.08 3.46 1.23 0.16 0.49 0.33 -0.13 [259,] 0.03 1.77 0.47 0.12 0.06 0.12 -0.28 [260,] 0.04 1.09 0.28 0.08 0.04 0.20 -0.29 [261,] 0.03 0.60 0.11 0.08 0.03 0.14 -0.27 [262,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [263,] 0.07 2.37 0.52 0.07 0.45 0.30 -0.19 [264,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [265,] 0.05 0.99 0.07 0.14 0.05 0.14 -0.29 [266,] 0.05 2.52 0.73 0.10 0.24 0.24 -0.29 [267,] 0.14 5.28 2.14 0.29 0.14 0.14 0.64 [268,] 0.05 1.73 0.54 0.11 0.27 0.27 -0.28 [269,] 0.04 1.54 0.55 0.15 0.15 0.18 -0.29 [270,] 0.03 0.75 0.11 0.08 0.11 0.14 -0.27 [271,] 0.05 1.70 0.69 0.18 0.28 0.23 -0.29 [272,] 0.02 0.62 0.03 0.07 0.12 0.12 -0.25 [273,] 0.06 1.38 0.01 0.13 0.38 0.19 -0.25 [274,] 0.09 3.01 0.94 0.19 0.56 0.28 -0.03 [275,] 0.03 0.88 0.13 0.13 0.16 0.13 -0.28 [276,] 0.13 3.47 0.90 0.26 0.64 0.13 0.41 [277,] 0.03 1.04 0.13 0.16 0.20 0.10 -0.28 [278,] 0.07 2.52 1.02 0.14 0.34 0.34 -0.23 [279,] 0.05 2.32 0.74 0.20 0.30 0.20 -0.29 [280,] 0.04 1.08 0.06 0.04 0.20 0.20 -0.29 [281,] 0.06 2.30 0.93 0.25 0.37 0.25 -0.25 [282,] 0.06 1.81 0.85 0.23 0.06 0.23 -0.27 [283,] 0.04 1.17 0.26 0.15 0.18 0.15 -0.29 [284,] 0.07 3.09 1.10 0.22 0.07 0.22 -0.19 [285,] 0.04 1.19 0.31 0.13 0.04 0.18 -0.29 [286,] 0.06 1.66 0.09 0.18 0.25 0.12 -0.26 [287,] 0.02 0.53 0.04 0.07 0.07 0.12 -0.25 [288,] 0.07 1.95 0.29 0.22 0.29 0.14 -0.20 [289,] 0.08 2.11 0.55 0.23 0.08 0.16 -0.16 [290,] 0.09 3.23 1.31 0.17 0.44 0.35 -0.09 [291,] 0.04 1.45 0.27 0.12 0.16 0.16 -0.29 [292,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [293,] 0.05 1.77 0.72 0.24 0.24 0.19 -0.29 [294,] 0.04 0.94 0.06 0.17 0.21 0.13 -0.29 [295,] 0.03 0.86 0.27 0.11 0.03 0.13 -0.26 [296,] 0.07 1.89 0.28 0.14 0.35 0.21 -0.21 [297,] 0.02 0.39 0.01 0.07 0.09 0.09 -0.22 [298,] 0.04 1.04 0.15 0.08 0.19 0.19 -0.29 [299,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [300,] 0.04 1.30 0.35 0.18 0.25 0.14 -0.29 [301,] 0.10 2.76 0.72 0.20 0.41 0.20 0.06 [302,] 0.03 1.07 0.13 0.07 0.17 0.17 -0.28 [303,] 0.05 1.57 0.20 0.10 0.29 0.20 -0.29 [304,] 0.03 0.56 0.04 0.08 0.10 0.13 -0.26 [305,] 0.03 0.74 0.13 0.13 0.10 0.13 -0.29 [306,] 0.05 0.91 0.04 0.10 0.26 0.21 -0.28 [307,] 0.03 1.01 0.31 0.13 0.13 0.16 -0.28 [308,] 0.02 0.64 0.02 0.10 0.06 0.06 -0.23 [309,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [310,] 0.02 0.76 0.10 0.07 0.10 0.12 -0.25 [311,] 0.07 1.95 0.11 0.14 0.22 0.14 -0.20 [312,] 0.03 0.60 0.19 0.11 0.03 0.14 -0.27 [313,] 0.03 1.26 0.40 0.13 0.16 0.13 -0.26 [314,] 0.06 1.70 0.25 0.06 0.25 0.25 -0.25 [315,] 0.05 2.02 0.82 0.27 0.05 0.16 -0.28 [316,] 0.01 0.54 0.05 0.05 0.06 0.06 -0.18 [317,] 0.03 0.86 0.11 0.05 0.03 0.13 -0.26 [318,] 0.07 3.64 1.05 0.14 0.49 0.28 -0.21 [319,] 0.04 0.91 0.06 0.08 0.04 0.17 -0.29 [320,] 0.04 1.86 0.54 0.14 0.07 0.14 -0.29 [321,] 0.02 0.42 0.01 0.06 0.02 0.10 -0.22 [322,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [323,] 0.03 0.92 0.14 0.14 0.20 0.14 -0.29 [324,] 0.04 1.33 0.62 0.17 0.04 0.21 -0.29 [325,] 0.03 0.75 0.04 0.06 0.08 0.14 -0.27 [326,] 0.05 1.45 0.18 0.05 0.27 0.23 -0.29 [327,] 0.08 2.79 1.13 0.23 0.45 0.30 -0.18 [328,] 0.06 1.80 0.56 0.11 0.34 0.28 -0.27 [329,] 0.03 0.80 0.25 0.13 0.13 0.13 -0.26 [330,] 0.03 1.04 0.04 0.11 0.14 0.08 -0.27 [331,] 0.04 1.17 0.05 0.07 0.15 0.15 -0.29 [332,] 0.04 1.24 0.39 0.15 0.04 0.15 -0.29 [333,] 0.05 2.22 0.71 0.19 0.24 0.19 -0.29 [334,] 0.02 0.63 0.23 0.12 0.02 0.12 -0.25 [335,] 0.03 0.75 0.11 0.08 0.11 0.14 -0.27 [336,] 0.10 3.52 1.43 0.38 0.38 0.19 -0.02 [337,] 0.02 0.42 0.01 0.06 0.08 0.08 -0.20 [338,] 0.04 1.34 0.54 0.15 0.04 0.18 -0.29 [339,] 0.05 1.68 0.79 0.16 0.05 0.26 -0.28 [340,] 0.05 1.38 0.51 0.10 0.05 0.26 -0.29 [341,] 0.05 1.37 0.36 0.10 0.31 0.25 -0.29 [342,] 0.10 3.56 1.44 0.19 0.10 0.29 0.00 [343,] 0.04 1.13 0.06 0.08 0.17 0.17 -0.29 [344,] 0.03 0.59 0.02 0.05 0.03 0.13 -0.26 [345,] 0.03 0.70 0.13 0.13 0.06 0.13 -0.28 [346,] 0.01 0.59 0.00 0.06 0.08 0.06 -0.19 [347,] 0.02 0.48 0.03 0.07 0.11 0.09 -0.22 [348,] 0.07 1.96 0.51 0.22 0.44 0.22 -0.20 [349,] 0.05 2.48 0.72 0.19 0.05 0.14 -0.29 [350,] 0.04 1.10 0.06 0.20 0.20 0.08 -0.29 [351,] 0.03 0.83 0.05 0.06 0.15 0.15 -0.28 [352,] 0.02 0.62 0.03 0.07 0.12 0.12 -0.25 [353,] 0.02 0.40 0.00 0.09 0.07 0.07 -0.22 [354,] 0.02 0.46 0.07 0.07 0.02 0.09 -0.21 [355,] 0.02 0.46 0.07 0.07 0.02 0.09 -0.21 [356,] 0.05 2.56 0.82 0.11 0.27 0.27 -0.28 [357,] 0.10 3.24 1.52 0.30 0.51 0.30 0.05 [358,] 0.03 1.38 0.23 0.07 0.16 0.16 -0.28 [359,] 0.03 0.65 0.02 0.12 0.18 0.12 -0.27 [360,] 0.02 0.58 0.00 0.06 0.13 0.11 -0.24 [361,] 0.04 1.44 0.45 0.13 0.27 0.22 -0.29 [362,] 0.01 0.26 0.00 0.06 0.05 0.06 -0.17 [363,] 0.03 1.41 0.45 0.15 0.03 0.12 -0.28 [364,] 0.04 1.13 0.35 0.11 0.04 0.18 -0.29 [365,] 0.02 1.42 0.37 0.10 0.12 0.12 -0.26 [366,] 0.03 0.84 0.12 0.09 0.19 0.16 -0.28 [367,] 0.02 0.63 0.14 0.08 0.02 0.10 -0.23 [368,] 0.02 0.84 0.23 0.09 0.02 0.11 -0.25 [369,] 0.07 2.38 0.74 0.07 0.07 0.30 -0.19 [370,] 0.04 0.87 0.16 0.12 0.04 0.16 -0.29 [371,] 0.07 1.88 0.49 0.28 0.21 0.14 -0.22 [372,] 0.05 3.11 0.82 0.27 0.27 0.11 -0.28 [373,] 0.04 1.38 0.30 0.09 0.21 0.21 -0.29 [374,] 0.03 0.82 0.05 0.12 0.03 0.09 -0.28 [375,] 0.02 0.44 0.03 0.08 0.10 0.10 -0.23 [376,] 0.03 0.66 0.04 0.12 0.15 0.12 -0.28 [377,] 0.03 0.86 0.19 0.08 0.03 0.13 -0.26 [378,] 0.06 2.77 0.88 0.18 0.29 0.24 -0.27 [379,] 0.02 0.44 0.01 0.06 0.02 0.10 -0.23 [380,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [381,] 0.05 1.32 0.20 0.05 0.24 0.24 -0.29 [382,] 0.03 1.51 0.44 0.12 0.15 0.15 -0.27 [383,] 0.03 1.12 0.35 0.14 0.21 0.17 -0.29 [384,] 0.05 2.32 0.74 0.20 0.30 0.20 -0.29 [385,] 0.09 2.51 0.65 0.19 0.09 0.19 -0.04 [386,] 0.02 0.41 0.03 0.08 0.08 0.09 -0.22 [387,] 0.07 2.28 0.71 0.14 0.36 0.29 -0.21 [388,] 0.05 1.03 0.04 0.09 0.23 0.19 -0.29 [389,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [390,] 0.07 2.87 1.02 0.20 0.41 0.27 -0.22 [391,] 0.02 0.55 0.14 0.10 0.08 0.10 -0.23 [392,] 0.05 2.14 0.76 0.20 0.20 0.20 -0.29 [393,] 0.08 4.62 1.22 0.24 0.41 0.16 -0.14 [394,] 0.11 4.58 1.64 0.33 0.65 0.22 0.14 [395,] 0.08 2.72 0.59 0.17 0.08 0.17 -0.11 [396,] 0.02 0.39 0.07 0.09 0.07 0.09 -0.21 [397,] 0.05 1.05 0.07 0.05 0.29 0.24 -0.29 [398,] 0.04 0.90 0.03 0.04 0.16 0.21 -0.29 [399,] 0.04 1.33 0.62 0.17 0.04 0.21 -0.29 [400,] 0.07 1.46 0.10 0.13 0.33 0.20 -0.23 [401,] 0.01 0.35 0.05 0.06 0.03 0.06 -0.18 [402,] 0.02 0.46 0.07 0.07 0.02 0.09 -0.21 [403,] 0.04 1.77 0.63 0.21 0.21 0.17 -0.29 [404,] 0.06 1.84 0.09 0.12 0.40 0.17 -0.27 [405,] 0.08 4.60 1.21 0.32 0.24 0.08 -0.14 [406,] 0.02 0.79 0.15 0.09 0.11 0.11 -0.24 [407,] 0.06 3.37 0.97 0.13 0.32 0.26 -0.24 [408,] 0.04 1.66 0.53 0.14 0.21 0.18 -0.29 [409,] 0.07 1.76 0.46 0.13 0.33 0.26 -0.24 [410,] 0.03 0.79 0.21 0.12 0.15 0.15 -0.27 [411,] 0.07 1.59 0.29 0.14 0.22 0.22 -0.20 [412,] 0.04 1.46 0.28 0.08 0.24 0.20 -0.29 [413,] 0.05 1.48 0.38 0.22 0.22 0.16 -0.28 [414,] 0.04 1.58 0.38 0.15 0.23 0.15 -0.29 [415,] 0.02 0.47 0.03 0.06 0.02 0.11 -0.24 [416,] 0.07 1.47 0.27 0.13 0.07 0.20 -0.23 [417,] 0.03 1.50 0.40 0.11 0.16 0.13 -0.26 [418,] 0.08 2.78 1.13 0.30 0.30 0.23 -0.18 [419,] 0.04 1.03 0.27 0.11 0.19 0.19 -0.29 [420,] 0.05 0.81 0.47 0.19 0.19 0.23 -0.29 [421,] 0.03 0.57 0.10 0.10 0.13 0.13 -0.26 [422,] 0.04 1.20 0.18 0.09 0.04 0.18 -0.29 [423,] 0.16 5.81 2.36 0.31 0.79 0.16 0.91 [424,] 0.02 0.38 0.03 0.09 0.02 0.07 -0.21 [425,] 0.07 1.76 0.46 0.13 0.33 0.26 -0.24 [426,] 0.02 0.59 0.09 0.09 0.11 0.11 -0.24 [427,] 0.04 0.82 0.00 0.04 0.11 0.19 -0.29 [428,] 0.03 0.93 0.24 0.14 0.03 0.14 -0.29 [429,] 0.07 2.28 1.07 0.36 0.36 0.21 -0.21 [430,] 0.05 1.49 0.47 0.19 0.23 0.19 -0.29 [431,] 0.09 2.86 1.34 0.18 0.27 0.36 -0.07 [432,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [433,] 0.03 0.83 0.12 0.12 0.12 0.12 -0.28 [434,] 0.02 0.88 0.25 0.08 0.02 0.08 -0.21 [435,] 0.09 2.51 0.65 0.19 0.09 0.19 -0.04 [436,] 0.04 1.19 0.31 0.13 0.04 0.18 -0.29 [437,] 0.07 2.88 1.03 0.14 0.07 0.27 -0.22 [438,] 0.05 2.23 0.80 0.21 0.27 0.21 -0.28 [439,] 0.05 1.42 0.37 0.21 0.16 0.16 -0.28 [440,] 0.11 2.93 0.76 0.22 0.65 0.22 0.13 [441,] 0.08 3.32 1.18 0.24 0.24 0.24 -0.16 [442,] 0.03 0.71 0.11 0.08 0.08 0.13 -0.26 [443,] 0.04 1.19 0.31 0.13 0.04 0.18 -0.29 [444,] 0.03 0.75 0.05 0.07 0.14 0.17 -0.29 [445,] 0.03 0.70 0.10 0.10 0.03 0.10 -0.26 [446,] 0.03 0.57 0.10 0.10 0.13 0.13 -0.26 [447,] 0.03 0.75 0.05 0.07 0.14 0.17 -0.29 [448,] 0.05 2.22 0.71 0.19 0.24 0.19 -0.29 [449,] 0.11 3.97 1.07 0.21 0.64 0.21 0.12 [450,] 0.07 2.69 1.09 0.22 0.36 0.29 -0.20 [451,] 0.04 1.20 0.18 0.09 0.04 0.18 -0.29 [452,] -0.17 -4.66 -0.26 -0.52 -0.69 -0.69 1.02 [453,] -0.20 -5.40 -0.80 -0.60 -0.20 -1.00 1.72 [454,] -0.12 -4.46 -1.81 -0.60 -0.72 -0.24 -0.01 [455,] -0.18 -5.66 -1.77 -0.53 -0.88 -0.35 1.12 [456,] -0.19 -4.28 -0.02 -0.78 -0.97 -0.97 1.58 [457,] -0.15 -3.37 -0.23 -0.31 -0.15 -0.77 0.59 [458,] -0.17 -6.44 -2.61 -0.70 -0.87 -0.35 1.06 [459,] -0.20 -4.36 -0.30 -0.40 -0.59 -0.79 1.67 [460,] -0.04 -1.47 -0.60 -0.08 -0.24 -0.16 -0.89 [461,] -0.04 -1.22 -0.57 -0.15 -0.11 -0.08 -0.90 [462,] -0.02 -0.63 -0.26 -0.07 -0.07 -0.03 -0.98 [463,] -0.18 -7.36 -2.63 -0.53 -0.18 -0.70 1.09 [464,] -0.18 -7.69 -2.75 -0.92 -0.73 -0.18 1.28 [465,] -0.16 -5.93 -1.60 -0.32 -0.96 -0.32 0.75 [466,] -0.16 -4.97 -2.33 -0.47 -0.16 -0.31 0.64 [467,] -0.13 -3.42 -0.51 -0.13 -0.76 -0.63 0.09 [468,] -0.12 -4.28 -1.16 -0.23 -0.81 -0.35 -0.09 [469,] -0.24 -6.47 -0.96 -0.72 -1.20 -1.20 2.91 [470,] -0.13 -5.30 -1.89 -0.50 -0.63 -0.63 0.08 [471,] -0.17 -7.94 -2.53 -0.84 -0.68 -0.84 0.94 [472,] -0.21 -5.57 -0.82 -0.62 -1.03 -0.82 1.89 [473,] -0.16 -4.38 -1.14 -0.81 -0.16 -0.65 0.79 [474,] -0.30 -8.10 -0.45 -0.90 -1.50 -1.20 5.12 [475,] -0.21 -5.62 -1.46 -0.83 -1.25 -0.42 1.94 [476,] -0.12 -5.13 -1.83 -0.49 -0.61 -0.49 0.01 [477,] -0.14 -3.75 -1.39 -0.56 -0.97 -0.42 0.31 [478,] -0.05 -1.25 -0.07 -0.09 -0.23 -0.09 -0.85 [479,] -0.15 -4.83 -0.60 -0.60 -0.91 -0.60 0.55 [480,] -0.08 -2.13 -0.55 -0.24 -0.08 -0.24 -0.58 [481,] -0.16 -4.98 -1.56 -0.62 -0.16 -0.62 0.65 [482,] -0.05 -1.24 -0.18 -0.09 -0.32 -0.09 -0.86 [483,] -0.21 -3.63 -0.16 -1.04 -0.83 -1.04 1.92 [484,] -0.16 -5.11 -1.60 -0.64 -0.16 -0.80 0.74 [485,] -0.17 -5.29 -1.16 -0.33 -0.99 -0.66 0.86 [486,] -0.11 -4.19 -1.70 -0.23 -0.68 -0.45 -0.13 [487,] -0.10 -3.72 -1.00 -0.10 -0.50 -0.30 -0.31 [488,] -0.25 -8.09 -2.53 -0.51 -1.26 -1.26 3.34 [489,] -0.15 -7.95 -2.29 -0.31 -0.92 -0.61 0.59 [490,] -0.09 -3.87 -1.38 -0.09 -0.09 -0.28 -0.42 [491,] -0.03 -1.61 -0.47 -0.06 -0.19 -0.09 -0.93 [492,] -0.10 -3.63 -1.47 -0.29 -0.59 -0.49 -0.34 [493,] -0.28 -6.07 -1.10 -0.83 -0.83 -1.10 4.18 [494,] -0.13 -3.61 -0.94 -0.13 -0.80 -0.27 0.21 [495,] -0.15 -4.09 -0.61 -0.45 -0.76 -0.76 0.56 [496,] -0.30 -13.93 -4.45 -1.19 -1.78 -1.48 4.97 [497,] -0.17 -7.26 -2.59 -0.69 -0.17 -0.17 1.03 [498,] -0.22 -5.82 -0.86 -0.65 -0.65 -0.86 2.16 [499,] -0.26 -8.26 -1.81 -1.03 -1.03 -1.29 3.53 [500,] -0.17 -5.44 -0.07 -0.51 -0.51 -0.68 0.96 [501,] -0.16 -7.52 -2.40 -0.80 -0.80 -0.64 0.74 [502,] -0.19 -7.09 -2.87 -0.38 -0.96 -0.77 1.50 [503,] -0.16 -3.59 -0.65 -0.33 -0.98 -0.65 0.81 [504,] -0.13 -3.55 -0.53 -0.26 -0.53 -0.66 0.18 [505,] -0.22 -11.31 -3.26 -1.09 -0.22 -0.65 2.22 [506,] -0.09 -2.54 -0.38 -0.28 -0.28 -0.28 -0.40 [507,] -0.11 -3.05 -1.13 -0.45 -0.11 -0.45 -0.13 [508,] -0.10 -3.11 -0.68 -0.29 -0.68 -0.39 -0.36 [509,] -0.07 -2.37 -0.52 -0.15 -0.30 -0.07 -0.63 [510,] -0.05 -1.04 -0.07 -0.05 -0.14 -0.09 -0.85 [511,] -0.19 -4.14 -0.75 -0.56 -1.13 -0.75 1.41 [512,] -0.18 -7.39 -2.64 -0.70 -1.06 -0.70 1.11 [513,] -0.07 -4.13 -1.09 -0.07 -0.36 -0.29 -0.64 [514,] -0.15 -4.87 -0.61 -0.46 -0.76 -0.30 0.57 [515,] -0.07 -1.97 -0.29 -0.07 -0.29 -0.29 -0.64 [516,] -0.24 -7.64 -1.67 -0.96 -0.24 -0.96 2.88 [517,] -0.08 -4.41 -1.16 -0.08 -0.31 -0.31 -0.59 [518,] -0.11 -4.77 -1.70 -0.45 -0.57 -0.23 -0.12 [519,] -0.10 -3.72 -1.00 -0.10 -0.50 -0.30 -0.31 [520,] 0.01 0.35 0.13 0.03 0.05 0.01 -1.00 [521,] -0.10 -5.15 -1.48 -0.30 -0.40 -0.40 -0.33 [522,] -0.07 -2.01 -0.52 -0.22 -0.37 -0.22 -0.62 [523,] -0.18 -5.80 -1.27 -0.36 -0.72 -0.36 1.23 [524,] -0.18 -3.90 -0.71 -0.71 -0.35 -0.89 1.14 [525,] -0.12 -3.19 -0.83 -0.35 -0.71 -0.47 -0.05 [526,] -0.20 -7.41 -3.01 -0.20 -1.00 -1.00 1.73 [527,] -0.12 -3.69 -1.73 -0.35 -0.12 -0.35 -0.10 [528,] -0.19 -5.14 -1.33 -0.38 -0.95 -0.95 1.47 [529,] -0.07 -2.34 -0.51 -0.22 -0.37 -0.22 -0.64 [530,] -0.14 -4.51 -0.21 -0.28 -0.28 -0.56 0.35 [531,] -0.21 -8.67 -3.10 -0.83 -0.21 -0.41 1.90 [532,] -0.17 -5.45 -1.70 -0.51 -0.85 -0.68 0.98 [533,] -0.14 -5.35 -0.58 -0.14 -0.87 -0.43 0.42 [534,] -0.06 -1.61 -0.24 -0.12 -0.30 -0.18 -0.76 [535,] -0.20 -8.43 -3.01 -0.60 -0.80 -0.60 1.74 [536,] -0.15 -3.98 -1.47 -0.74 -0.88 -0.74 0.48 [537,] -0.16 -5.93 -1.60 -0.32 -0.96 -0.32 0.75 [538,] -0.18 -4.90 -1.27 -0.18 -0.54 -0.54 1.24 [539,] -0.10 -2.69 -0.70 -0.40 -0.10 -0.20 -0.32 [540,] -0.09 -2.93 -0.92 -0.18 -0.37 -0.37 -0.43 [541,] -0.24 -4.23 -0.18 -0.48 -0.24 -0.73 2.98 [542,] -0.20 -6.50 -3.05 -0.61 -1.02 -0.81 1.81 [543,] -0.09 -2.00 -0.64 -0.36 -0.36 -0.27 -0.44 [544,] -0.16 -5.13 -1.12 -0.64 -0.96 -0.80 0.75 [545,] -0.11 -2.95 -0.44 -0.22 -0.66 -0.22 -0.19 [546,] -0.14 -3.10 -0.21 -0.71 -0.71 -0.42 0.35 [547,] -0.10 -3.28 -1.54 -0.31 -0.51 -0.10 -0.29 [548,] -0.16 -6.58 -2.35 -0.31 -0.16 -0.31 0.67 [549,] -0.16 -6.55 -2.34 -0.47 -0.78 -0.62 0.66 [550,] -0.16 -5.02 -1.57 -0.31 -0.63 -0.31 0.67 [551,] -0.12 -3.89 -1.82 -0.36 -0.12 -0.12 0.01 [552,] -0.28 -16.16 -4.25 -1.42 -1.13 -1.42 4.47 [553,] -0.26 -12.35 -3.94 -1.05 -1.58 -1.05 3.70 [554,] -0.02 -0.81 -0.29 -0.04 -0.12 -0.06 -0.97 [555,] -0.18 -6.59 -2.67 -0.53 -1.07 -0.53 1.15 [556,] -0.20 -7.35 -2.98 -0.99 -0.99 -0.40 1.69 [557,] -0.13 -3.45 -1.28 -0.26 -0.77 -0.51 0.11 [558,] -0.04 -1.65 -0.67 -0.09 -0.22 -0.18 -0.86 [559,] -0.09 -2.77 -1.30 -0.09 -0.43 -0.17 -0.49 [560,] -0.13 -4.23 -1.32 -0.40 -0.79 -0.40 0.19 [561,] 0.01 0.25 0.10 0.03 0.03 0.01 -1.00 [562,] -0.24 -6.35 -0.35 -0.47 -1.18 -1.18 2.77 [563,] -0.02 -0.87 -0.28 -0.04 -0.09 -0.04 -0.98 [564,] -0.19 -7.09 -2.87 -0.38 -0.96 -0.77 1.50 [565,] -0.29 -7.79 -1.15 -0.58 -1.44 -1.44 4.66 [566,] -0.16 -4.35 -1.61 -0.65 -0.16 -0.81 0.77 [567,] -0.09 -1.99 -0.36 -0.27 -0.09 -0.27 -0.45 [568,] -0.38 -19.73 -2.66 -1.52 -1.90 -1.90 8.80 [569,] -0.13 -3.41 -0.50 -0.13 -0.38 -0.63 0.08 [570,] -0.11 -4.19 -1.70 -0.23 -0.68 -0.45 -0.13 [571,] 0.01 0.22 0.03 0.01 0.02 0.01 -1.00 [572,] -0.30 -5.24 -0.22 -0.60 -0.90 -1.50 5.10 [573,] -0.18 -5.74 -2.69 -0.90 -0.90 -0.72 1.19 [574,] -0.19 -4.10 -0.75 -0.19 -0.56 -0.93 1.37 [575,] -0.17 -5.31 -0.66 -0.66 -0.99 -0.66 0.87 [576,] -0.06 -1.27 -0.09 -0.17 -0.29 -0.12 -0.77 [577,] -0.07 -3.04 -1.09 -0.14 -0.36 -0.29 -0.64 [578,] -0.14 -4.36 -0.95 -0.54 -0.54 -0.54 0.26 [579,] -0.14 -5.34 -2.17 -0.43 -0.87 -0.29 0.42 [580,] -0.03 -1.24 -0.44 -0.09 -0.18 -0.09 -0.94 [581,] -0.07 -1.84 -0.27 -0.07 -0.34 -0.27 -0.68 [582,] -0.05 -1.89 -0.76 -0.20 -0.36 -0.15 -0.82 [583,] -0.14 -5.11 -2.07 -0.41 -0.83 -0.55 0.30 [584,] -0.11 -2.33 -0.16 -0.21 -0.32 -0.32 -0.24 [585,] -0.09 -2.83 -0.35 -0.27 -0.53 -0.18 -0.47 [586,] -0.13 -4.31 -2.02 -0.67 -0.81 -0.67 0.23 [587,] -0.24 -12.48 -3.60 -0.24 -1.20 -1.20 2.92 [588,] -0.22 -10.43 -3.33 -0.22 -1.33 -1.11 2.35 [589,] -0.10 -3.21 -1.51 -0.40 -0.40 -0.40 -0.31 [590,] -0.07 -2.31 -1.08 -0.22 -0.22 -0.14 -0.65 [591,] -0.16 -4.28 -1.11 -0.63 -0.16 -0.32 0.71 [592,] -0.16 -6.62 -2.36 -0.47 -0.95 -0.32 0.69 [593,] -0.07 -3.09 -1.10 -0.15 -0.22 -0.15 -0.63 [594,] -0.23 -6.22 -1.61 -0.46 -1.15 -0.92 2.61 [595,] -0.11 -3.44 -1.08 -0.43 -0.43 -0.32 -0.21 [596,] -0.11 -5.02 -1.60 -0.32 -0.43 -0.21 -0.22 [597,] -0.12 -2.72 -0.19 -0.12 -0.25 -0.62 0.04 [598,] -0.15 -4.66 -1.46 -0.29 -0.73 -0.58 0.44 [599,] -0.10 -3.23 -1.01 -0.20 -0.61 -0.50 -0.31 [600,] -0.02 -0.51 -0.16 -0.07 -0.14 -0.05 -0.96 [601,] -0.09 -3.02 -1.41 -0.28 -0.09 -0.47 -0.40 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 150 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsNeg ~ age + yearsmarried + religiousness + occupation + rating, left = -Inf, right = 0, data = Affairs) $terms affairsNeg ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairsNeg, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairsNeg 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairsNeg, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairsNeg age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 0 150 451 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation -5.608161 0.050347 -0.161852 0.476324 -0.106006 rating logSigma 0.712242 2.244542 $left [1] -Inf $right [1] 0 class [1] "censReg" "maxLik" "maxim" "list" print( x, digits = 2 ) Call: censReg(formula = affairsNeg ~ age + yearsmarried + religiousness + occupation + rating, left = -Inf, right = 0, data = Affairs) Coefficients: (Intercept) age yearsmarried religiousness occupation -8.17 0.18 -0.55 1.69 -0.33 rating logSigma 2.28 2.11 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating 0.04 -0.13 0.39 -0.08 0.53 printME( margEff( x ) ) age yearsmarried religiousness occupation rating 0.042 -0.130 0.394 -0.076 0.534 attr(,"jacobian") (Intercept) age yearsmarried religiousness occupation rating age -0.007 0.017 -0.054 -0.021 -0.028 -0.026 yearsmarried 0.021 0.669 0.402 0.064 0.086 0.081 religiousness -0.063 -2.035 -0.512 0.039 -0.263 -0.246 occupation 0.012 0.394 0.099 0.038 0.285 0.048 rating -0.085 -2.758 -0.694 -0.265 -0.356 -0.100 sigma age 0.005 yearsmarried -0.015 religiousness 0.046 occupation -0.009 rating 0.062 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age 0.042 0.018 2.3 0.02 * yearsmarried -0.130 0.031 -4.2 4e-05 *** religiousness 0.394 0.093 4.2 3e-05 *** occupation -0.076 0.059 -1.3 0.20 rating 0.534 0.095 5.6 3e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-Likelihood: -705.58 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) -8.174 2.741 -3.0 0.003 ** age 0.179 0.079 2.3 0.023 * yearsmarried -0.554 0.135 -4.1 4e-05 *** religiousness 1.686 0.404 4.2 3e-05 *** occupation -0.326 0.254 -1.3 0.200 rating 2.285 0.408 5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsNeg ~ age + yearsmarried + religiousness + occupation + rating, left = -Inf, right = 0, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 0 150 451 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) -8.174 2.741 -3.0 0.003 ** age 0.179 0.079 2.3 0.023 * yearsmarried -0.554 0.135 -4.1 4e-05 *** religiousness 1.686 0.404 4.2 3e-05 *** occupation -0.326 0.254 -1.3 0.200 rating 2.285 0.408 5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -705.58 on 7 Df > round( coef( estResultNeg ), 2 ) (Intercept) age yearsmarried religiousness occupation -8.17 0.18 -0.55 1.69 -0.33 rating logSigma 2.28 2.11 > round( coef( estResultNeg, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation -8.17 0.18 -0.55 1.69 -0.33 rating sigma 2.28 8.25 > round( vcov( estResultNeg ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.52 -0.12 0.09 -0.40 -0.18 -0.62 age -0.12 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.09 -0.01 0.02 -0.01 0.00 0.00 religiousness -0.40 0.00 -0.01 0.16 0.01 0.00 occupation -0.18 0.00 0.00 0.01 0.06 -0.01 rating -0.62 0.00 0.00 0.00 -0.01 0.17 logSigma -0.01 0.00 0.00 0.00 0.00 0.01 logSigma (Intercept) -0.01 age 0.00 yearsmarried 0.00 religiousness 0.00 occupation 0.00 rating 0.01 logSigma 0.00 > round( vcov( estResultNeg, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.52 -0.12 0.09 -0.40 -0.18 -0.62 age -0.12 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.09 -0.01 0.02 -0.01 0.00 0.00 religiousness -0.40 0.00 -0.01 0.16 0.01 0.00 occupation -0.18 0.00 0.00 0.01 0.06 -0.01 rating -0.62 0.00 0.00 0.00 -0.01 0.17 sigma -0.06 0.00 -0.01 0.04 -0.01 0.06 sigma (Intercept) -0.06 age 0.00 yearsmarried -0.01 religiousness 0.04 occupation -0.01 rating 0.06 sigma 0.31 > logLik( estResultNeg ) 'log Lik.' -705.58 (df=7) > nobs( estResultNeg ) [1] 601 > extractAIC( estResultNeg ) [1] 7.0 1425.2 > model.frame( estResultNeg ) affairsNeg age yearsmarried religiousness occupation rating 4 0 37.0 10.000 3 7 4 5 0 27.0 4.000 4 6 4 11 0 32.0 15.000 1 1 4 16 0 57.0 15.000 5 6 5 23 0 22.0 0.750 2 6 3 29 0 32.0 1.500 2 5 5 44 0 22.0 0.750 2 1 3 45 0 57.0 15.000 2 4 4 47 0 32.0 15.000 4 1 2 49 0 22.0 1.500 4 4 5 50 0 37.0 15.000 2 7 2 55 0 27.0 4.000 4 6 4 64 0 47.0 15.000 5 6 4 80 0 22.0 1.500 2 5 4 86 0 27.0 4.000 4 5 4 93 0 37.0 15.000 1 5 5 108 0 37.0 15.000 2 4 3 114 0 22.0 0.750 3 5 4 115 0 22.0 1.500 2 5 5 116 0 27.0 10.000 2 1 5 123 0 22.0 1.500 2 5 5 127 0 22.0 1.500 2 5 5 129 0 27.0 10.000 4 5 4 134 0 32.0 10.000 3 1 5 137 0 37.0 4.000 2 6 4 139 0 22.0 1.500 2 5 5 147 0 27.0 7.000 4 1 5 151 0 42.0 15.000 5 6 4 153 0 27.0 4.000 3 5 5 155 0 27.0 4.000 3 5 4 162 0 42.0 15.000 4 6 3 163 0 22.0 1.500 3 5 5 165 0 27.0 0.417 4 6 4 168 0 42.0 15.000 5 5 4 170 0 32.0 4.000 1 6 4 172 0 22.0 1.500 4 5 3 184 0 42.0 15.000 3 1 4 187 0 22.0 4.000 4 5 5 192 0 22.0 1.500 1 3 5 194 0 22.0 0.750 3 1 5 210 0 32.0 10.000 5 6 5 217 0 52.0 15.000 5 6 3 220 0 22.0 0.417 5 1 4 224 0 27.0 4.000 2 6 1 227 0 32.0 7.000 5 5 3 228 0 22.0 4.000 3 5 5 239 0 27.0 7.000 4 6 5 241 0 42.0 15.000 2 5 4 245 0 27.0 1.500 4 3 5 249 0 42.0 15.000 2 6 4 262 0 22.0 0.750 5 3 5 265 0 32.0 7.000 2 6 4 267 0 27.0 4.000 5 6 5 269 0 27.0 10.000 4 6 4 271 0 22.0 4.000 1 5 5 277 0 37.0 15.000 4 3 1 290 0 22.0 1.500 5 4 4 292 0 37.0 15.000 4 1 5 293 0 27.0 0.750 4 5 4 295 0 32.0 10.000 4 6 4 299 0 47.0 15.000 5 7 2 320 0 37.0 10.000 3 6 4 321 0 22.0 0.750 2 5 5 324 0 27.0 4.000 2 4 5 334 0 32.0 7.000 4 6 4 351 0 42.0 15.000 2 3 5 355 0 37.0 10.000 4 6 4 361 0 47.0 15.000 3 6 5 362 0 22.0 1.500 5 5 5 366 0 27.0 1.500 2 6 4 370 0 27.0 4.000 3 5 5 374 0 32.0 10.000 5 4 5 378 0 22.0 0.125 2 5 5 381 0 47.0 15.000 4 4 3 382 0 32.0 15.000 1 5 5 383 0 27.0 7.000 4 5 5 384 0 22.0 1.500 3 5 5 400 0 27.0 4.000 3 6 5 403 0 22.0 1.500 3 5 5 409 0 57.0 15.000 2 7 2 412 0 17.5 1.500 3 6 5 413 0 57.0 15.000 4 6 5 416 0 22.0 0.750 2 3 4 418 0 42.0 4.000 4 3 3 422 0 22.0 1.500 4 1 5 435 0 22.0 0.417 1 6 4 439 0 32.0 15.000 4 5 5 445 0 27.0 1.500 3 5 2 447 0 22.0 1.500 3 1 5 448 0 37.0 15.000 3 1 4 449 0 32.0 15.000 4 3 4 478 0 37.0 10.000 2 5 3 482 0 37.0 10.000 4 5 4 486 0 57.0 15.000 5 5 3 489 0 27.0 0.417 1 3 4 490 0 42.0 15.000 5 1 5 491 0 57.0 15.000 3 6 1 492 0 37.0 10.000 1 6 4 503 0 37.0 15.000 3 5 5 508 0 37.0 15.000 4 6 5 509 0 27.0 10.000 5 1 5 512 0 37.0 10.000 2 6 4 515 0 22.0 0.125 4 4 5 517 0 57.0 15.000 5 6 5 532 0 37.0 15.000 4 6 4 533 0 22.0 4.000 4 6 4 535 0 27.0 7.000 4 5 4 537 0 57.0 15.000 4 5 4 538 0 32.0 15.000 3 6 3 543 0 22.0 1.500 2 5 4 547 0 32.0 7.000 4 1 5 550 0 37.0 15.000 4 6 5 558 0 32.0 1.500 5 5 5 571 0 42.0 10.000 5 7 4 578 0 27.0 7.000 3 5 4 583 0 37.0 15.000 4 6 5 586 0 37.0 15.000 4 3 2 594 0 32.0 10.000 5 6 4 597 0 22.0 0.750 4 1 5 602 0 27.0 7.000 4 2 4 603 0 27.0 7.000 2 2 5 604 0 42.0 15.000 5 5 4 612 0 42.0 15.000 4 5 3 613 0 27.0 7.000 2 1 2 621 0 22.0 1.500 3 5 5 627 0 37.0 15.000 5 6 5 630 0 22.0 0.125 2 4 5 631 0 27.0 1.500 4 5 5 632 0 32.0 1.500 2 6 5 639 0 27.0 1.500 2 6 5 645 0 27.0 10.000 4 1 3 647 0 42.0 15.000 4 6 5 648 0 27.0 1.500 2 6 5 651 0 27.0 4.000 2 6 3 655 0 32.0 10.000 3 5 3 667 0 32.0 15.000 3 5 4 670 0 22.0 0.750 2 6 5 671 0 37.0 15.000 2 1 4 673 0 27.0 4.000 4 5 5 701 0 27.0 4.000 1 5 4 705 0 27.0 10.000 2 1 4 706 0 32.0 15.000 5 6 4 709 0 27.0 7.000 5 5 3 717 0 52.0 15.000 2 5 4 719 0 27.0 4.000 3 6 3 723 0 37.0 4.000 1 5 4 724 0 27.0 4.000 4 5 4 726 0 52.0 15.000 5 1 3 734 0 57.0 15.000 4 6 4 735 0 27.0 7.000 1 5 4 736 0 37.0 7.000 4 6 3 737 0 22.0 0.750 2 4 3 739 0 32.0 4.000 2 5 3 743 0 37.0 15.000 4 6 3 745 0 22.0 0.750 2 4 3 747 0 42.0 15.000 4 6 3 751 0 52.0 15.000 5 1 1 752 0 37.0 15.000 4 1 2 754 0 27.0 7.000 4 5 3 760 0 32.0 4.000 2 5 5 763 0 27.0 4.000 2 6 5 774 0 27.0 4.000 2 5 5 776 0 37.0 15.000 5 6 5 779 0 47.0 15.000 5 5 4 784 0 32.0 10.000 3 1 4 788 0 27.0 1.500 4 1 2 794 0 57.0 15.000 2 5 2 795 0 22.0 1.500 4 5 4 798 0 42.0 15.000 3 3 4 800 0 57.0 15.000 4 2 2 803 0 57.0 15.000 4 6 5 807 0 22.0 0.125 4 4 5 812 0 32.0 10.000 4 1 5 820 0 42.0 15.000 3 5 4 823 0 27.0 1.500 2 6 5 830 0 32.0 0.125 2 5 2 843 0 27.0 4.000 3 5 4 848 0 27.0 10.000 2 1 4 851 0 32.0 7.000 4 1 3 854 0 37.0 15.000 4 5 4 856 0 42.0 15.000 5 6 2 857 0 32.0 1.500 4 6 5 859 0 32.0 4.000 3 5 3 863 0 37.0 7.000 4 5 5 865 0 22.0 0.417 3 3 5 867 0 27.0 7.000 4 1 5 870 0 27.0 0.750 3 5 5 873 0 27.0 4.000 2 5 5 875 0 32.0 10.000 4 4 5 876 0 32.0 15.000 1 5 5 877 0 22.0 0.750 3 4 5 880 0 27.0 7.000 4 1 4 903 0 27.0 0.417 4 5 4 904 0 37.0 15.000 4 5 4 905 0 37.0 15.000 2 1 3 908 0 22.0 4.000 1 5 4 909 0 37.0 15.000 4 5 3 910 0 22.0 1.500 2 4 5 912 0 52.0 15.000 4 6 2 914 0 22.0 1.500 4 5 5 915 0 32.0 4.000 5 3 5 916 0 32.0 4.000 2 3 5 920 0 22.0 1.500 3 6 5 921 0 27.0 0.750 2 3 3 925 0 22.0 7.000 2 5 2 926 0 27.0 0.750 2 5 3 929 0 37.0 15.000 4 1 2 931 0 22.0 1.500 1 1 5 945 0 37.0 10.000 2 4 4 947 0 37.0 15.000 4 5 3 949 0 42.0 15.000 3 3 3 950 0 22.0 4.000 2 5 5 961 0 52.0 7.000 2 6 2 965 0 27.0 0.750 2 5 5 966 0 27.0 4.000 2 4 5 967 0 42.0 1.500 5 6 5 987 0 22.0 1.500 4 6 5 990 0 22.0 4.000 4 5 3 992 0 22.0 4.000 1 5 4 995 0 37.0 15.000 5 4 5 1009 0 37.0 10.000 3 6 3 1021 0 42.0 15.000 4 6 5 1026 0 47.0 15.000 4 5 5 1027 0 22.0 1.500 4 5 4 1030 0 32.0 10.000 3 1 4 1031 0 22.0 7.000 1 3 5 1034 0 32.0 10.000 4 5 4 1037 0 27.0 1.500 2 2 4 1038 0 37.0 15.000 4 5 5 1039 0 42.0 4.000 3 4 5 1045 0 37.0 15.000 5 5 4 1046 0 32.0 7.000 4 5 5 1054 0 42.0 15.000 4 6 5 1059 0 27.0 4.000 4 6 4 1063 0 22.0 0.750 4 6 5 1068 0 27.0 4.000 4 5 3 1070 0 22.0 0.750 5 1 5 1072 0 52.0 15.000 5 5 5 1073 0 32.0 10.000 3 5 5 1077 0 37.0 15.000 4 4 4 1081 0 32.0 7.000 2 5 4 1083 0 42.0 15.000 3 1 4 1084 0 32.0 15.000 1 5 5 1086 0 27.0 4.000 3 5 5 1087 0 32.0 15.000 4 3 4 1089 0 22.0 0.750 3 2 4 1096 0 22.0 1.500 3 5 3 1102 0 42.0 15.000 4 3 5 1103 0 52.0 15.000 3 5 4 1107 0 37.0 15.000 5 6 4 1109 0 47.0 15.000 4 2 3 1115 0 57.0 15.000 2 6 4 1119 0 32.0 7.000 4 5 5 1124 0 27.0 7.000 4 1 4 1126 0 22.0 1.500 1 6 5 1128 0 22.0 4.000 3 1 4 1129 0 22.0 1.500 2 1 5 1130 0 42.0 15.000 2 6 4 1133 0 57.0 15.000 4 2 4 1140 0 27.0 7.000 2 1 5 1143 0 22.0 4.000 3 1 5 1146 0 37.0 15.000 4 5 3 1153 0 32.0 7.000 1 6 4 1156 0 22.0 1.500 2 5 5 1157 0 22.0 1.500 3 1 3 1158 0 52.0 15.000 2 5 5 1160 0 37.0 15.000 2 1 1 1161 0 32.0 10.000 2 5 5 1166 0 42.0 15.000 4 4 5 1177 0 27.0 4.000 3 4 5 1178 0 37.0 15.000 4 6 5 1180 0 27.0 1.500 3 5 5 1187 0 22.0 0.125 2 6 3 1191 0 32.0 10.000 2 6 3 1195 0 27.0 4.000 4 5 4 1207 0 27.0 7.000 2 5 1 1208 0 32.0 4.000 5 6 3 1209 0 37.0 15.000 2 5 5 1211 0 47.0 15.000 4 6 4 1215 0 27.0 1.500 1 5 5 1221 0 37.0 15.000 4 6 4 1226 0 32.0 15.000 4 1 4 1229 0 32.0 7.000 4 5 4 1231 0 42.0 15.000 3 1 3 1234 0 27.0 7.000 3 1 4 1235 0 27.0 1.500 3 4 2 1242 0 22.0 1.500 3 3 5 1245 0 27.0 4.000 3 4 2 1260 0 27.0 7.000 3 1 2 1266 0 37.0 15.000 2 5 4 1271 0 37.0 7.000 3 4 4 1273 0 22.0 1.500 2 5 5 1276 0 37.0 15.000 5 5 4 1280 0 22.0 1.500 4 5 3 1282 0 32.0 10.000 4 1 5 1285 0 27.0 4.000 2 5 3 1295 0 22.0 0.417 4 5 5 1298 0 27.0 4.000 2 5 5 1299 0 37.0 15.000 4 5 3 1304 0 37.0 10.000 5 7 4 1305 0 27.0 7.000 2 4 2 1311 0 32.0 4.000 2 5 5 1314 0 32.0 4.000 2 6 4 1319 0 22.0 1.500 3 4 5 1322 0 22.0 4.000 4 3 4 1324 0 17.5 0.750 2 5 4 1327 0 32.0 10.000 4 4 5 1328 0 32.0 0.750 5 3 3 1330 0 37.0 15.000 4 5 3 1332 0 32.0 4.000 3 4 5 1333 0 27.0 1.500 2 3 2 1336 0 22.0 7.000 4 1 5 1341 0 47.0 15.000 5 6 5 1344 0 27.0 4.000 1 4 4 1352 0 37.0 15.000 5 1 3 1358 0 42.0 4.000 4 5 5 1359 0 32.0 4.000 2 1 5 1361 0 52.0 15.000 2 7 4 1364 0 22.0 1.500 2 1 4 1368 0 52.0 15.000 4 2 4 1384 0 22.0 0.417 3 1 5 1390 0 22.0 1.500 2 5 5 1393 0 27.0 4.000 4 6 4 1394 0 32.0 15.000 4 1 5 1402 0 27.0 1.500 2 3 5 1407 0 32.0 4.000 1 6 5 1408 0 37.0 15.000 3 6 4 1412 0 32.0 10.000 2 6 5 1413 0 32.0 10.000 5 5 5 1416 0 37.0 1.500 4 5 3 1417 0 32.0 1.500 2 4 4 1418 0 32.0 10.000 4 1 4 1419 0 47.0 15.000 4 5 4 1420 0 27.0 10.000 5 1 5 1423 0 27.0 4.000 3 4 5 1424 0 37.0 15.000 4 4 2 1432 0 27.0 0.750 4 5 5 1433 0 37.0 15.000 4 1 5 1437 0 32.0 15.000 3 1 5 1438 0 27.0 10.000 2 1 5 1439 0 27.0 7.000 2 6 5 1446 0 37.0 15.000 2 1 3 1450 0 27.0 1.500 2 4 4 1451 0 22.0 0.750 2 1 5 1452 0 22.0 4.000 4 2 4 1453 0 42.0 0.125 4 6 4 1456 0 27.0 1.500 4 6 5 1464 0 27.0 7.000 3 6 3 1469 0 52.0 15.000 4 1 3 1473 0 27.0 1.500 5 5 2 1481 0 27.0 1.500 2 5 5 1482 0 27.0 1.500 3 5 5 1496 0 22.0 0.125 5 4 4 1497 0 27.0 4.000 4 1 5 1504 0 27.0 4.000 4 1 5 1513 0 47.0 15.000 2 5 5 1515 0 32.0 15.000 3 5 3 1534 0 42.0 7.000 2 5 5 1535 0 22.0 0.750 4 6 4 1536 0 27.0 0.125 3 6 5 1540 0 32.0 10.000 3 6 5 1551 0 22.0 0.417 5 4 5 1555 0 47.0 15.000 5 1 4 1557 0 32.0 10.000 3 1 5 1566 0 57.0 15.000 4 5 5 1567 0 27.0 4.000 3 6 5 1576 0 32.0 7.000 4 1 5 1584 0 37.0 10.000 4 1 5 1585 0 32.0 10.000 1 1 4 1590 0 22.0 4.000 3 1 4 1594 0 27.0 7.000 4 3 2 1595 0 57.0 15.000 5 5 2 1603 0 32.0 7.000 2 5 5 1608 0 27.0 1.500 4 1 3 1609 0 22.0 1.500 4 5 5 1615 0 22.0 1.500 4 5 4 1616 0 32.0 7.000 3 1 5 1617 0 47.0 15.000 3 5 4 1620 0 22.0 0.750 3 1 5 1621 0 22.0 1.500 2 5 5 1637 0 27.0 4.000 1 5 5 1638 0 52.0 15.000 4 5 5 1650 0 32.0 10.000 4 6 5 1654 0 47.0 15.000 4 6 4 1665 0 27.0 7.000 2 1 2 1670 0 22.0 1.500 4 4 5 1671 0 32.0 10.000 2 5 4 1675 0 22.0 0.750 2 5 4 1688 0 22.0 1.500 2 5 5 1691 0 42.0 15.000 3 6 4 1695 0 27.0 7.000 5 4 5 1698 0 42.0 15.000 4 4 4 1704 0 57.0 15.000 3 5 2 1705 0 42.0 15.000 3 6 2 1711 0 32.0 7.000 2 1 2 1719 0 22.0 4.000 5 4 5 1723 0 22.0 1.500 1 6 5 1726 0 22.0 0.750 1 4 5 1749 0 32.0 15.000 4 1 5 1752 0 22.0 1.500 2 5 3 1754 0 27.0 4.000 5 2 5 1758 0 27.0 4.000 4 1 5 1761 0 42.0 15.000 5 5 4 1773 0 32.0 1.500 2 7 3 1775 0 57.0 15.000 4 3 1 1786 0 37.0 7.000 4 5 5 1793 0 52.0 15.000 2 5 4 1799 0 47.0 15.000 4 6 5 1803 0 27.0 7.000 2 5 4 1806 0 27.0 7.000 4 5 5 1807 0 22.0 4.000 2 3 3 1808 0 37.0 7.000 2 6 5 1814 0 27.0 7.000 4 4 3 1815 0 42.0 10.000 4 6 4 1818 0 22.0 1.500 3 1 5 1827 0 22.0 4.000 2 1 3 1834 0 57.0 15.000 4 6 5 1835 0 37.0 15.000 4 4 3 1843 0 27.0 7.000 3 5 5 1846 0 17.5 10.000 4 4 5 1850 0 22.0 4.000 4 5 5 1851 0 27.0 4.000 2 1 4 1854 0 37.0 15.000 2 5 1 1859 0 22.0 1.500 5 1 4 1861 0 27.0 7.000 2 5 4 1866 0 27.0 4.000 4 5 5 1873 0 22.0 0.125 1 3 5 1875 0 27.0 7.000 4 1 4 1885 0 32.0 15.000 5 5 3 1892 0 32.0 10.000 4 5 4 1895 0 32.0 15.000 2 3 4 1896 0 22.0 1.500 3 5 5 1897 0 27.0 4.000 4 4 4 1899 0 52.0 15.000 5 1 5 1904 0 27.0 7.000 2 1 2 1905 0 27.0 7.000 3 1 4 1908 0 42.0 15.000 2 1 4 1916 0 42.0 15.000 4 5 4 1918 0 27.0 7.000 4 3 3 1920 0 27.0 7.000 2 6 2 1930 0 42.0 15.000 3 3 3 1940 0 27.0 4.000 3 3 5 1947 0 27.0 7.000 3 1 4 1949 0 22.0 1.500 2 4 5 1951 0 27.0 4.000 4 1 4 1952 0 22.0 4.000 4 5 5 1960 0 22.0 1.500 2 4 5 9001 0 47.0 15.000 4 5 4 9012 0 37.0 10.000 2 6 2 9023 0 37.0 15.000 3 5 4 9029 0 27.0 4.000 2 1 4 6 -3 27.0 1.500 3 4 4 12 -3 27.0 4.000 3 1 5 43 -7 37.0 15.000 5 6 2 53 -12 32.0 10.000 3 5 2 67 -1 22.0 0.125 4 5 5 79 -1 22.0 1.500 2 1 5 122 -12 37.0 15.000 4 5 2 126 -7 22.0 1.500 2 3 4 133 -2 37.0 15.000 2 6 4 138 -3 32.0 15.000 4 3 2 154 -1 37.0 15.000 4 4 2 159 -7 42.0 15.000 3 1 4 174 -12 42.0 15.000 5 4 1 176 -12 37.0 10.000 2 6 2 181 -12 32.0 15.000 3 1 2 182 -3 27.0 4.000 1 6 5 186 -7 37.0 10.000 2 7 3 189 -7 27.0 4.000 3 5 5 204 -1 42.0 15.000 4 5 5 215 -1 47.0 15.000 5 4 5 232 -7 27.0 4.000 3 5 4 233 -1 27.0 7.000 5 1 4 252 -12 27.0 1.500 3 5 4 253 -12 27.0 7.000 4 6 2 274 -3 42.0 15.000 4 5 4 275 -7 27.0 10.000 4 7 3 287 -1 27.0 1.500 2 5 2 288 -1 32.0 4.000 4 6 4 325 -1 27.0 7.000 3 1 3 328 -3 32.0 10.000 4 1 4 344 -3 27.0 4.000 2 7 2 353 -1 17.5 0.750 5 4 5 354 -1 32.0 10.000 4 1 5 367 -7 32.0 7.000 2 6 4 369 -7 37.0 15.000 2 6 4 390 -7 37.0 10.000 1 5 3 392 -12 32.0 10.000 2 5 5 423 -7 52.0 15.000 2 6 4 432 -7 42.0 15.000 1 1 3 436 -1 52.0 15.000 2 6 3 483 -2 37.0 15.000 3 6 5 513 -12 22.0 4.000 3 3 4 516 -12 27.0 7.000 1 6 2 518 -1 27.0 4.000 3 5 5 520 -12 47.0 15.000 4 6 5 526 -12 42.0 15.000 4 1 1 528 -7 27.0 4.000 3 3 4 553 -7 32.0 7.000 4 4 5 576 -1 32.0 0.417 3 3 4 611 -3 47.0 15.000 5 5 4 625 -12 37.0 15.000 2 5 4 635 -7 22.0 4.000 2 6 4 646 -1 27.0 4.000 2 4 5 657 -7 52.0 15.000 5 1 3 659 -1 27.0 4.000 3 3 3 666 -1 27.0 10.000 4 1 4 679 -1 32.0 7.000 3 7 4 729 -7 32.0 7.000 2 4 1 755 -3 22.0 1.500 1 3 2 758 -7 22.0 4.000 3 6 4 770 -7 42.0 15.000 4 6 4 786 -2 57.0 15.000 1 5 4 797 -7 32.0 4.000 3 5 2 811 -1 27.0 4.000 1 4 4 834 -7 32.0 7.000 4 1 4 858 -2 57.0 15.000 1 4 4 885 -7 42.0 15.000 4 5 2 893 -7 37.0 10.000 1 5 3 927 -3 42.0 15.000 3 6 1 928 -1 52.0 15.000 3 4 4 933 -2 27.0 7.000 3 5 3 951 -12 32.0 7.000 2 4 2 968 -1 22.0 4.000 4 2 5 972 -3 27.0 7.000 3 6 4 975 -12 37.0 15.000 1 5 5 977 -7 32.0 15.000 3 1 3 981 -7 27.0 7.000 2 5 5 986 -1 32.0 7.000 3 5 3 1002 -1 32.0 1.500 2 2 4 1007 -12 42.0 15.000 4 1 2 1011 -7 32.0 10.000 3 5 4 1035 -7 37.0 4.000 1 6 3 1050 -1 27.0 4.000 2 5 3 1056 -12 42.0 15.000 3 4 3 1057 -1 27.0 10.000 5 6 5 1075 -12 37.0 10.000 2 6 2 1080 -12 27.0 7.000 1 3 3 1125 -3 27.0 7.000 4 1 2 1131 -3 32.0 10.000 2 4 4 1138 -12 17.5 0.750 2 1 3 1150 -12 32.0 15.000 3 5 4 1163 -2 22.0 7.000 4 4 3 1169 -1 32.0 7.000 4 6 5 1198 -7 27.0 4.000 2 6 2 1204 -1 22.0 1.500 5 5 3 1218 -12 32.0 15.000 3 5 1 1230 -12 42.0 15.000 2 1 2 1236 -7 42.0 15.000 3 5 4 1247 -12 32.0 10.000 2 4 2 1259 -12 32.0 15.000 3 1 1 1294 -7 57.0 15.000 5 4 5 1353 -12 47.0 15.000 4 6 4 1370 -2 42.0 15.000 2 6 3 1427 -12 37.0 15.000 3 6 3 1445 -12 37.0 15.000 5 5 2 1460 -7 27.0 10.000 2 6 4 1480 -2 37.0 15.000 2 5 4 1505 -12 32.0 15.000 1 5 2 1543 -7 32.0 10.000 3 6 3 1548 -2 37.0 15.000 4 5 1 1550 -7 27.0 1.500 2 5 5 1561 -3 47.0 15.000 2 5 2 1564 -12 37.0 15.000 2 5 4 1573 -12 27.0 4.000 2 5 5 1575 -2 27.0 10.000 4 1 5 1599 -1 22.0 4.000 3 1 3 1622 -12 52.0 7.000 4 5 5 1629 -2 27.0 4.000 1 3 5 1664 -7 37.0 15.000 2 6 4 1669 -2 27.0 4.000 1 3 1 1674 -12 17.5 0.750 2 3 5 1682 -7 32.0 15.000 5 5 4 1685 -7 22.0 4.000 1 3 5 1697 -2 32.0 4.000 4 6 4 1716 -1 22.0 1.500 3 5 2 1730 -3 42.0 15.000 2 5 4 1731 -1 32.0 7.000 4 4 4 1732 -12 37.0 15.000 3 6 2 1743 -1 42.0 15.000 3 6 3 1751 -1 27.0 4.000 1 5 4 1757 -2 37.0 15.000 4 7 3 1763 -7 37.0 15.000 3 6 4 1766 -3 22.0 1.500 2 3 3 1772 -3 32.0 4.000 3 6 2 1776 -2 32.0 15.000 5 6 5 1782 -12 52.0 15.000 1 5 5 1784 -12 47.0 15.000 1 6 5 1791 -3 32.0 15.000 4 4 4 1831 -7 32.0 15.000 3 3 2 1840 -7 27.0 7.000 4 1 2 1844 -12 42.0 15.000 3 6 2 1856 -7 42.0 15.000 2 3 2 1876 -12 27.0 7.000 2 5 4 1929 -3 32.0 10.000 4 4 3 1935 -7 47.0 15.000 3 4 2 1938 -1 22.0 1.500 1 2 5 1941 -7 32.0 10.000 2 5 4 1954 -2 32.0 10.000 2 6 5 1959 -2 22.0 7.000 3 6 2 9010 -1 32.0 15.000 3 1 5 > > ## estimation with right-censoring at -5 > Affairs$affairsAddNeg <- - Affairs$affairsAdd > estResultAddNeg <- censReg( affairsAddNeg ~ age + yearsmarried + religiousness + + occupation + rating, data = Affairs, left = -Inf, right = -5 ) > printAll( estResultAddNeg, sumMeCalcVCov = FALSE ) $maximum [1] -705.58 $estimate (Intercept) age yearsmarried religiousness occupation -13.17 0.18 -0.55 1.69 -0.33 rating logSigma 2.28 2.11 $gradient (Intercept) age yearsmarried religiousness occupation 0 0 0 0 0 rating logSigma 0 0 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.0 -165.5 -44.9 -14.8 -21.3 -18.5 age -165.5 -5890.2 -1675.4 -500.7 -717.1 -602.6 yearsmarried -44.9 -1675.4 -550.7 -140.7 -193.4 -159.0 religiousness -14.8 -500.7 -140.7 -50.8 -62.7 -54.8 occupation -21.3 -717.1 -193.4 -62.7 -106.7 -78.9 rating -18.5 -602.6 -159.0 -54.8 -78.9 -74.9 logSigma 37.1 1206.3 301.5 116.1 155.3 148.5 logSigma (Intercept) 37.1 age 1206.3 yearsmarried 301.5 religiousness 116.1 occupation 155.3 rating 148.5 logSigma -530.5 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "Newton-Raphson maximisation" $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] 0.06 2.09 0.56 0.17 0.40 0.23 -0.27 [2,] 0.03 0.92 0.14 0.14 0.20 0.14 -0.29 [3,] 0.10 3.17 1.49 0.10 0.10 0.40 0.02 [4,] 0.02 1.11 0.29 0.10 0.12 0.10 -0.23 [5,] 0.07 1.44 0.05 0.13 0.39 0.20 -0.24 [6,] 0.03 0.85 0.04 0.05 0.13 0.13 -0.26 [7,] 0.05 1.18 0.04 0.11 0.05 0.16 -0.28 [8,] 0.06 3.18 0.84 0.11 0.22 0.22 -0.28 [9,] 0.09 3.02 1.42 0.38 0.09 0.19 -0.02 [10,] 0.02 0.41 0.03 0.08 0.08 0.09 -0.22 [11,] 0.14 5.15 2.09 0.28 0.97 0.28 0.58 [12,] 0.03 0.92 0.14 0.14 0.20 0.14 -0.29 [13,] 0.04 1.82 0.58 0.19 0.23 0.16 -0.29 [14,] 0.05 1.09 0.07 0.10 0.25 0.20 -0.29 [15,] 0.03 0.88 0.13 0.13 0.16 0.13 -0.28 [16,] 0.08 3.03 1.23 0.08 0.41 0.41 -0.13 [17,] 0.11 3.91 1.58 0.21 0.42 0.32 0.10 [18,] 0.04 0.81 0.03 0.11 0.18 0.15 -0.29 [19,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [20,] 0.05 1.38 0.51 0.10 0.05 0.26 -0.29 [21,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [22,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [23,] 0.05 1.42 0.53 0.21 0.26 0.21 -0.28 [24,] 0.04 1.13 0.35 0.11 0.04 0.18 -0.29 [25,] 0.04 1.60 0.17 0.09 0.26 0.17 -0.29 [26,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [27,] 0.02 0.63 0.16 0.09 0.02 0.12 -0.25 [28,] 0.04 1.86 0.66 0.22 0.27 0.18 -0.29 [29,] 0.03 0.79 0.12 0.09 0.15 0.15 -0.27 [30,] 0.04 1.13 0.17 0.13 0.21 0.17 -0.29 [31,] 0.07 3.07 1.10 0.29 0.44 0.22 -0.20 [32,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [33,] 0.02 0.66 0.01 0.10 0.15 0.10 -0.25 [34,] 0.04 1.77 0.63 0.21 0.21 0.17 -0.29 [35,] 0.06 1.95 0.24 0.06 0.37 0.24 -0.26 [36,] 0.04 0.94 0.06 0.17 0.21 0.13 -0.29 [37,] 0.06 2.35 0.84 0.17 0.06 0.22 -0.27 [38,] 0.03 0.57 0.10 0.10 0.13 0.13 -0.26 [39,] 0.04 0.92 0.06 0.04 0.12 0.21 -0.29 [40,] 0.02 0.44 0.01 0.06 0.02 0.10 -0.23 [41,] 0.03 0.85 0.26 0.13 0.16 0.13 -0.26 [42,] 0.05 2.46 0.71 0.24 0.28 0.14 -0.29 [43,] 0.02 0.34 0.01 0.08 0.02 0.06 -0.20 [44,] 0.11 3.10 0.46 0.23 0.69 0.11 0.21 [45,] 0.04 1.28 0.28 0.20 0.20 0.12 -0.29 [46,] 0.03 0.75 0.14 0.10 0.17 0.17 -0.29 [47,] 0.03 0.83 0.22 0.12 0.19 0.15 -0.28 [48,] 0.08 3.34 1.19 0.16 0.40 0.32 -0.15 [49,] 0.01 0.40 0.02 0.06 0.04 0.07 -0.19 [50,] 0.08 3.46 1.23 0.16 0.49 0.33 -0.13 [51,] 0.01 0.25 0.01 0.06 0.03 0.06 -0.16 [52,] 0.06 1.95 0.43 0.12 0.37 0.24 -0.26 [53,] 0.02 0.46 0.07 0.08 0.10 0.08 -0.21 [54,] 0.05 1.48 0.55 0.22 0.33 0.22 -0.28 [55,] 0.06 1.21 0.22 0.06 0.28 0.28 -0.28 [56,] 0.11 4.23 1.71 0.46 0.34 0.11 0.21 [57,] 0.02 0.46 0.03 0.11 0.08 0.08 -0.24 [58,] 0.04 1.34 0.54 0.15 0.04 0.18 -0.29 [59,] 0.02 0.65 0.02 0.10 0.12 0.10 -0.25 [60,] 0.05 1.56 0.49 0.20 0.29 0.20 -0.29 [61,] 0.07 3.44 1.10 0.37 0.51 0.15 -0.20 [62,] 0.05 2.00 0.54 0.16 0.32 0.22 -0.28 [63,] 0.03 0.73 0.03 0.07 0.17 0.17 -0.28 [64,] 0.04 0.99 0.15 0.07 0.15 0.18 -0.29 [65,] 0.04 1.23 0.27 0.15 0.23 0.15 -0.29 [66,] 0.06 2.36 0.84 0.11 0.17 0.28 -0.27 [67,] 0.04 1.59 0.43 0.17 0.26 0.17 -0.29 [68,] 0.05 2.13 0.68 0.14 0.27 0.23 -0.29 [69,] 0.01 0.31 0.02 0.07 0.07 0.07 -0.19 [70,] 0.05 1.24 0.07 0.09 0.28 0.18 -0.29 [71,] 0.03 0.79 0.12 0.09 0.15 0.15 -0.27 [72,] 0.02 0.76 0.24 0.12 0.09 0.12 -0.25 [73,] 0.03 0.69 0.00 0.06 0.16 0.16 -0.28 [74,] 0.06 2.86 0.91 0.24 0.24 0.18 -0.26 [75,] 0.09 2.88 1.35 0.09 0.45 0.45 -0.07 [76,] 0.03 0.79 0.21 0.12 0.15 0.15 -0.27 [77,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [78,] 0.03 0.84 0.12 0.09 0.19 0.16 -0.28 [79,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [80,] 0.10 5.84 1.54 0.20 0.72 0.20 0.06 [81,] 0.03 0.57 0.05 0.10 0.20 0.16 -0.28 [82,] 0.03 1.50 0.40 0.11 0.16 0.13 -0.26 [83,] 0.04 0.94 0.03 0.09 0.13 0.17 -0.29 [84,] 0.03 1.14 0.11 0.11 0.08 0.08 -0.27 [85,] 0.02 0.34 0.02 0.06 0.02 0.08 -0.20 [86,] 0.06 1.31 0.02 0.06 0.36 0.24 -0.26 [87,] 0.05 1.59 0.75 0.20 0.25 0.25 -0.29 [88,] 0.06 1.73 0.10 0.19 0.32 0.13 -0.25 [89,] 0.02 0.47 0.03 0.06 0.02 0.11 -0.24 [90,] 0.06 2.31 0.94 0.19 0.06 0.25 -0.25 [91,] 0.06 1.97 0.92 0.25 0.18 0.25 -0.26 [92,] 0.08 3.07 0.83 0.17 0.42 0.25 -0.13 [93,] 0.04 1.52 0.41 0.16 0.21 0.16 -0.29 [94,] 0.04 2.26 0.60 0.20 0.20 0.12 -0.29 [95,] 0.05 1.26 0.02 0.05 0.14 0.19 -0.29 [96,] 0.02 0.99 0.35 0.12 0.02 0.12 -0.25 [97,] 0.11 5.99 1.58 0.32 0.63 0.11 0.09 [98,] 0.08 2.98 0.81 0.08 0.48 0.32 -0.14 [99,] 0.06 2.05 0.83 0.17 0.28 0.28 -0.28 [100,] 0.05 1.70 0.69 0.18 0.28 0.23 -0.29 [101,] 0.02 0.63 0.23 0.12 0.02 0.12 -0.25 [102,] 0.07 2.47 0.67 0.13 0.40 0.27 -0.23 [103,] 0.02 0.36 0.00 0.06 0.06 0.08 -0.20 [104,] 0.02 1.11 0.29 0.10 0.12 0.10 -0.23 [105,] 0.06 2.30 0.93 0.25 0.37 0.25 -0.25 [106,] 0.04 0.86 0.16 0.16 0.24 0.16 -0.29 [107,] 0.04 1.13 0.29 0.17 0.21 0.17 -0.29 [108,] 0.04 2.07 0.54 0.15 0.18 0.15 -0.29 [109,] 0.10 3.34 1.56 0.31 0.63 0.31 0.08 [110,] 0.05 1.09 0.07 0.10 0.25 0.20 -0.29 [111,] 0.02 0.63 0.14 0.08 0.02 0.10 -0.23 [112,] 0.05 1.70 0.69 0.18 0.28 0.23 -0.29 [113,] 0.01 0.31 0.01 0.05 0.05 0.05 -0.14 [114,] 0.03 1.28 0.30 0.15 0.21 0.12 -0.28 [115,] 0.05 1.43 0.37 0.16 0.26 0.21 -0.28 [116,] 0.05 1.70 0.69 0.18 0.28 0.23 -0.29 [117,] 0.09 3.41 1.38 0.37 0.28 0.18 -0.04 [118,] 0.04 1.23 0.38 0.19 0.23 0.15 -0.29 [119,] 0.01 0.31 0.01 0.06 0.01 0.07 -0.19 [120,] 0.04 0.98 0.25 0.14 0.07 0.14 -0.29 [121,] 0.04 1.15 0.30 0.08 0.08 0.21 -0.29 [122,] 0.04 1.77 0.63 0.21 0.21 0.17 -0.29 [123,] 0.07 2.96 1.06 0.28 0.35 0.21 -0.21 [124,] 0.09 2.51 0.65 0.19 0.09 0.19 -0.04 [125,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [126,] 0.04 1.33 0.54 0.18 0.22 0.18 -0.29 [127,] 0.03 0.66 0.00 0.06 0.12 0.15 -0.28 [128,] 0.02 0.45 0.03 0.07 0.08 0.08 -0.21 [129,] 0.03 0.90 0.04 0.06 0.17 0.14 -0.27 [130,] 0.03 0.88 0.05 0.07 0.20 0.16 -0.28 [131,] 0.06 1.61 0.60 0.24 0.06 0.18 -0.26 [132,] 0.04 1.70 0.61 0.16 0.24 0.20 -0.29 [133,] 0.03 0.88 0.05 0.07 0.20 0.16 -0.28 [134,] 0.07 1.97 0.29 0.15 0.44 0.22 -0.20 [135,] 0.08 2.44 0.76 0.23 0.38 0.23 -0.18 [136,] 0.08 2.57 1.21 0.24 0.40 0.32 -0.15 [137,] 0.04 0.77 0.03 0.07 0.21 0.18 -0.29 [138,] 0.08 2.81 1.14 0.15 0.08 0.30 -0.18 [139,] 0.02 0.59 0.09 0.09 0.11 0.11 -0.24 [140,] 0.07 1.77 0.26 0.07 0.33 0.26 -0.24 [141,] 0.07 1.84 0.68 0.14 0.07 0.27 -0.23 [142,] 0.06 1.80 0.84 0.28 0.34 0.23 -0.27 [143,] 0.05 1.23 0.32 0.23 0.23 0.14 -0.29 [144,] 0.06 3.37 0.97 0.13 0.32 0.26 -0.24 [145,] 0.06 1.61 0.24 0.18 0.36 0.18 -0.26 [146,] 0.05 1.93 0.21 0.05 0.26 0.21 -0.28 [147,] 0.03 0.88 0.13 0.13 0.16 0.13 -0.28 [148,] 0.04 1.94 0.56 0.19 0.04 0.11 -0.29 [149,] 0.04 2.17 0.57 0.15 0.23 0.15 -0.29 [150,] 0.08 2.13 0.55 0.08 0.39 0.32 -0.16 [151,] 0.05 1.74 0.33 0.19 0.28 0.14 -0.29 [152,] 0.06 1.33 0.05 0.12 0.24 0.18 -0.26 [153,] 0.06 2.02 0.25 0.13 0.32 0.19 -0.25 [154,] 0.08 2.98 1.21 0.32 0.48 0.24 -0.14 [155,] 0.06 1.33 0.05 0.12 0.24 0.18 -0.26 [156,] 0.07 3.07 1.10 0.29 0.44 0.22 -0.20 [157,] 0.07 3.57 1.03 0.34 0.07 0.07 -0.22 [158,] 0.09 3.20 1.30 0.35 0.09 0.17 -0.10 [159,] 0.06 1.54 0.40 0.23 0.29 0.17 -0.27 [160,] 0.03 1.07 0.13 0.07 0.17 0.17 -0.28 [161,] 0.04 1.09 0.16 0.08 0.24 0.20 -0.29 [162,] 0.04 1.04 0.15 0.08 0.19 0.19 -0.29 [163,] 0.04 1.33 0.54 0.18 0.22 0.18 -0.29 [164,] 0.04 1.73 0.55 0.18 0.18 0.15 -0.29 [165,] 0.05 1.57 0.49 0.15 0.05 0.20 -0.29 [166,] 0.04 1.16 0.06 0.17 0.04 0.09 -0.29 [167,] 0.10 5.49 1.44 0.19 0.48 0.19 0.00 [168,] 0.03 0.66 0.04 0.12 0.15 0.12 -0.28 [169,] 0.06 2.55 0.91 0.18 0.18 0.24 -0.26 [170,] 0.06 3.40 0.90 0.24 0.12 0.12 -0.26 [171,] 0.03 1.50 0.40 0.11 0.16 0.13 -0.26 [172,] 0.02 0.36 0.00 0.06 0.06 0.08 -0.20 [173,] 0.03 0.86 0.27 0.11 0.03 0.13 -0.26 [174,] 0.07 2.76 0.98 0.20 0.33 0.26 -0.24 [175,] 0.03 0.88 0.05 0.07 0.20 0.16 -0.28 [176,] 0.06 2.05 0.01 0.13 0.32 0.13 -0.24 [177,] 0.04 1.13 0.17 0.13 0.21 0.17 -0.29 [178,] 0.07 1.84 0.68 0.14 0.07 0.27 -0.23 [179,] 0.04 1.35 0.30 0.17 0.04 0.13 -0.29 [180,] 0.06 2.21 0.89 0.24 0.30 0.24 -0.26 [181,] 0.08 3.28 1.17 0.39 0.47 0.16 -0.16 [182,] 0.01 0.48 0.02 0.06 0.09 0.07 -0.19 [183,] 0.05 1.63 0.20 0.15 0.25 0.15 -0.29 [184,] 0.02 0.79 0.15 0.09 0.11 0.11 -0.24 [185,] 0.02 0.48 0.01 0.07 0.07 0.11 -0.24 [186,] 0.02 0.63 0.16 0.09 0.02 0.12 -0.25 [187,] 0.02 0.58 0.02 0.06 0.11 0.11 -0.24 [188,] 0.04 1.04 0.15 0.08 0.19 0.19 -0.29 [189,] 0.03 1.01 0.31 0.13 0.13 0.16 -0.28 [190,] 0.09 2.88 1.35 0.09 0.45 0.45 -0.07 [191,] 0.02 0.52 0.02 0.07 0.10 0.12 -0.25 [192,] 0.03 0.93 0.24 0.14 0.03 0.14 -0.29 [193,] 0.02 0.62 0.01 0.09 0.12 0.09 -0.25 [194,] 0.06 2.21 0.89 0.24 0.30 0.24 -0.26 [195,] 0.10 3.56 1.44 0.19 0.10 0.29 0.00 [196,] 0.07 1.60 0.29 0.07 0.36 0.29 -0.20 [197,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [198,] 0.03 0.75 0.05 0.07 0.14 0.17 -0.29 [199,] 0.08 4.01 1.16 0.31 0.46 0.15 -0.17 [200,] 0.02 0.44 0.03 0.08 0.10 0.10 -0.23 [201,] 0.01 0.37 0.05 0.06 0.03 0.06 -0.16 [202,] 0.03 0.96 0.12 0.06 0.09 0.15 -0.28 [203,] 0.03 0.63 0.04 0.09 0.17 0.14 -0.27 [204,] 0.05 1.40 0.04 0.10 0.16 0.16 -0.28 [205,] 0.11 2.51 0.80 0.23 0.57 0.23 0.21 [206,] 0.06 1.52 0.04 0.11 0.28 0.17 -0.27 [207,] 0.09 3.20 1.30 0.35 0.09 0.17 -0.10 [208,] 0.04 0.83 0.06 0.04 0.04 0.19 -0.29 [209,] 0.06 2.28 0.62 0.12 0.25 0.25 -0.25 [210,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [211,] 0.08 3.32 1.18 0.24 0.24 0.24 -0.16 [212,] 0.04 0.96 0.18 0.09 0.22 0.22 -0.29 [213,] 0.07 3.56 0.48 0.14 0.41 0.14 -0.22 [214,] 0.03 0.78 0.02 0.06 0.14 0.14 -0.27 [215,] 0.04 0.99 0.15 0.07 0.15 0.18 -0.29 [216,] 0.01 0.29 0.01 0.03 0.04 0.03 -0.11 [217,] 0.02 0.47 0.03 0.09 0.13 0.11 -0.24 [218,] 0.05 1.14 0.21 0.21 0.26 0.15 -0.29 [219,] 0.07 1.60 0.29 0.07 0.36 0.29 -0.20 [220,] 0.03 1.20 0.49 0.16 0.13 0.16 -0.28 [221,] 0.07 2.65 0.72 0.21 0.43 0.21 -0.21 [222,] 0.04 1.70 0.61 0.16 0.24 0.20 -0.29 [223,] 0.03 1.58 0.50 0.13 0.17 0.17 -0.29 [224,] 0.03 0.66 0.04 0.12 0.15 0.12 -0.28 [225,] 0.05 1.57 0.49 0.15 0.05 0.20 -0.29 [226,] 0.06 1.37 0.44 0.06 0.19 0.31 -0.25 [227,] 0.05 1.49 0.47 0.19 0.23 0.19 -0.29 [228,] 0.04 1.02 0.06 0.08 0.08 0.15 -0.29 [229,] 0.04 1.63 0.66 0.18 0.22 0.22 -0.29 [230,] 0.02 0.71 0.07 0.05 0.07 0.08 -0.21 [231,] 0.05 1.77 0.72 0.24 0.24 0.19 -0.29 [232,] 0.03 0.80 0.18 0.10 0.13 0.13 -0.26 [233,] 0.04 1.70 0.61 0.16 0.24 0.20 -0.29 [234,] 0.03 0.92 0.14 0.14 0.20 0.14 -0.29 [235,] 0.02 0.43 0.01 0.08 0.12 0.10 -0.23 [236,] 0.05 1.23 0.18 0.18 0.23 0.14 -0.29 [237,] 0.01 0.22 0.01 0.05 0.01 0.05 -0.15 [238,] 0.02 1.12 0.32 0.11 0.11 0.11 -0.24 [239,] 0.04 1.37 0.43 0.13 0.21 0.21 -0.29 [240,] 0.06 2.12 0.86 0.23 0.23 0.23 -0.27 [241,] 0.06 1.87 0.41 0.12 0.29 0.23 -0.27 [242,] 0.06 2.35 0.84 0.17 0.06 0.22 -0.27 [243,] 0.09 2.88 1.35 0.09 0.45 0.45 -0.07 [244,] 0.03 0.79 0.12 0.09 0.15 0.15 -0.27 [245,] 0.06 1.97 0.92 0.25 0.18 0.25 -0.26 [246,] 0.03 0.69 0.02 0.09 0.06 0.13 -0.28 [247,] 0.05 1.18 0.08 0.16 0.27 0.16 -0.28 [248,] 0.03 1.47 0.52 0.14 0.10 0.17 -0.29 [249,] 0.05 2.73 0.79 0.16 0.26 0.21 -0.28 [250,] 0.05 1.85 0.75 0.25 0.30 0.20 -0.29 [251,] 0.06 2.64 0.84 0.22 0.11 0.17 -0.27 [252,] 0.06 3.45 0.91 0.12 0.36 0.24 -0.26 [253,] 0.03 0.80 0.18 0.10 0.13 0.13 -0.26 [254,] 0.03 0.93 0.24 0.14 0.03 0.14 -0.29 [255,] 0.05 1.05 0.07 0.05 0.29 0.24 -0.29 [256,] 0.04 0.87 0.16 0.12 0.04 0.16 -0.29 [257,] 0.03 0.64 0.04 0.06 0.03 0.14 -0.27 [258,] 0.08 3.46 1.23 0.16 0.49 0.33 -0.13 [259,] 0.03 1.77 0.47 0.12 0.06 0.12 -0.28 [260,] 0.04 1.09 0.28 0.08 0.04 0.20 -0.29 [261,] 0.03 0.60 0.11 0.08 0.03 0.14 -0.27 [262,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [263,] 0.07 2.37 0.52 0.07 0.45 0.30 -0.19 [264,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [265,] 0.05 0.99 0.07 0.14 0.05 0.14 -0.29 [266,] 0.05 2.52 0.73 0.10 0.24 0.24 -0.29 [267,] 0.14 5.28 2.14 0.29 0.14 0.14 0.64 [268,] 0.05 1.73 0.54 0.11 0.27 0.27 -0.28 [269,] 0.04 1.54 0.55 0.15 0.15 0.18 -0.29 [270,] 0.03 0.75 0.11 0.08 0.11 0.14 -0.27 [271,] 0.05 1.70 0.69 0.18 0.28 0.23 -0.29 [272,] 0.02 0.62 0.03 0.07 0.12 0.12 -0.25 [273,] 0.06 1.38 0.01 0.13 0.38 0.19 -0.25 [274,] 0.09 3.01 0.94 0.19 0.56 0.28 -0.03 [275,] 0.03 0.88 0.13 0.13 0.16 0.13 -0.28 [276,] 0.13 3.47 0.90 0.26 0.64 0.13 0.41 [277,] 0.03 1.04 0.13 0.16 0.20 0.10 -0.28 [278,] 0.07 2.52 1.02 0.14 0.34 0.34 -0.23 [279,] 0.05 2.32 0.74 0.20 0.30 0.20 -0.29 [280,] 0.04 1.08 0.06 0.04 0.20 0.20 -0.29 [281,] 0.06 2.30 0.93 0.25 0.37 0.25 -0.25 [282,] 0.06 1.81 0.85 0.23 0.06 0.23 -0.27 [283,] 0.04 1.17 0.26 0.15 0.18 0.15 -0.29 [284,] 0.07 3.09 1.10 0.22 0.07 0.22 -0.19 [285,] 0.04 1.19 0.31 0.13 0.04 0.18 -0.29 [286,] 0.06 1.66 0.09 0.18 0.25 0.12 -0.26 [287,] 0.02 0.53 0.04 0.07 0.07 0.12 -0.25 [288,] 0.07 1.95 0.29 0.22 0.29 0.14 -0.20 [289,] 0.08 2.11 0.55 0.23 0.08 0.16 -0.16 [290,] 0.09 3.23 1.31 0.17 0.44 0.35 -0.09 [291,] 0.04 1.45 0.27 0.12 0.16 0.16 -0.29 [292,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [293,] 0.05 1.77 0.72 0.24 0.24 0.19 -0.29 [294,] 0.04 0.94 0.06 0.17 0.21 0.13 -0.29 [295,] 0.03 0.86 0.27 0.11 0.03 0.13 -0.26 [296,] 0.07 1.89 0.28 0.14 0.35 0.21 -0.21 [297,] 0.02 0.39 0.01 0.07 0.09 0.09 -0.22 [298,] 0.04 1.04 0.15 0.08 0.19 0.19 -0.29 [299,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [300,] 0.04 1.30 0.35 0.18 0.25 0.14 -0.29 [301,] 0.10 2.76 0.72 0.20 0.41 0.20 0.06 [302,] 0.03 1.07 0.13 0.07 0.17 0.17 -0.28 [303,] 0.05 1.57 0.20 0.10 0.29 0.20 -0.29 [304,] 0.03 0.56 0.04 0.08 0.10 0.13 -0.26 [305,] 0.03 0.74 0.13 0.13 0.10 0.13 -0.29 [306,] 0.05 0.91 0.04 0.10 0.26 0.21 -0.28 [307,] 0.03 1.01 0.31 0.13 0.13 0.16 -0.28 [308,] 0.02 0.64 0.02 0.10 0.06 0.06 -0.23 [309,] 0.08 2.88 1.17 0.31 0.39 0.23 -0.16 [310,] 0.02 0.76 0.10 0.07 0.10 0.12 -0.25 [311,] 0.07 1.95 0.11 0.14 0.22 0.14 -0.20 [312,] 0.03 0.60 0.19 0.11 0.03 0.14 -0.27 [313,] 0.03 1.26 0.40 0.13 0.16 0.13 -0.26 [314,] 0.06 1.70 0.25 0.06 0.25 0.25 -0.25 [315,] 0.05 2.02 0.82 0.27 0.05 0.16 -0.28 [316,] 0.01 0.54 0.05 0.05 0.06 0.06 -0.18 [317,] 0.03 0.86 0.11 0.05 0.03 0.13 -0.26 [318,] 0.07 3.64 1.05 0.14 0.49 0.28 -0.21 [319,] 0.04 0.91 0.06 0.08 0.04 0.17 -0.29 [320,] 0.04 1.86 0.54 0.14 0.07 0.14 -0.29 [321,] 0.02 0.42 0.01 0.06 0.02 0.10 -0.22 [322,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [323,] 0.03 0.92 0.14 0.14 0.20 0.14 -0.29 [324,] 0.04 1.33 0.62 0.17 0.04 0.21 -0.29 [325,] 0.03 0.75 0.04 0.06 0.08 0.14 -0.27 [326,] 0.05 1.45 0.18 0.05 0.27 0.23 -0.29 [327,] 0.08 2.79 1.13 0.23 0.45 0.30 -0.18 [328,] 0.06 1.80 0.56 0.11 0.34 0.28 -0.27 [329,] 0.03 0.80 0.25 0.13 0.13 0.13 -0.26 [330,] 0.03 1.04 0.04 0.11 0.14 0.08 -0.27 [331,] 0.04 1.17 0.05 0.07 0.15 0.15 -0.29 [332,] 0.04 1.24 0.39 0.15 0.04 0.15 -0.29 [333,] 0.05 2.22 0.71 0.19 0.24 0.19 -0.29 [334,] 0.02 0.63 0.23 0.12 0.02 0.12 -0.25 [335,] 0.03 0.75 0.11 0.08 0.11 0.14 -0.27 [336,] 0.10 3.52 1.43 0.38 0.38 0.19 -0.02 [337,] 0.02 0.42 0.01 0.06 0.08 0.08 -0.20 [338,] 0.04 1.34 0.54 0.15 0.04 0.18 -0.29 [339,] 0.05 1.68 0.79 0.16 0.05 0.26 -0.28 [340,] 0.05 1.38 0.51 0.10 0.05 0.26 -0.29 [341,] 0.05 1.37 0.36 0.10 0.31 0.25 -0.29 [342,] 0.10 3.56 1.44 0.19 0.10 0.29 0.00 [343,] 0.04 1.13 0.06 0.08 0.17 0.17 -0.29 [344,] 0.03 0.59 0.02 0.05 0.03 0.13 -0.26 [345,] 0.03 0.70 0.13 0.13 0.06 0.13 -0.28 [346,] 0.01 0.59 0.00 0.06 0.08 0.06 -0.19 [347,] 0.02 0.48 0.03 0.07 0.11 0.09 -0.22 [348,] 0.07 1.96 0.51 0.22 0.44 0.22 -0.20 [349,] 0.05 2.48 0.72 0.19 0.05 0.14 -0.29 [350,] 0.04 1.10 0.06 0.20 0.20 0.08 -0.29 [351,] 0.03 0.83 0.05 0.06 0.15 0.15 -0.28 [352,] 0.02 0.62 0.03 0.07 0.12 0.12 -0.25 [353,] 0.02 0.40 0.00 0.09 0.07 0.07 -0.22 [354,] 0.02 0.46 0.07 0.07 0.02 0.09 -0.21 [355,] 0.02 0.46 0.07 0.07 0.02 0.09 -0.21 [356,] 0.05 2.56 0.82 0.11 0.27 0.27 -0.28 [357,] 0.10 3.24 1.52 0.30 0.51 0.30 0.05 [358,] 0.03 1.38 0.23 0.07 0.16 0.16 -0.28 [359,] 0.03 0.65 0.02 0.12 0.18 0.12 -0.27 [360,] 0.02 0.58 0.00 0.06 0.13 0.11 -0.24 [361,] 0.04 1.44 0.45 0.13 0.27 0.22 -0.29 [362,] 0.01 0.26 0.00 0.06 0.05 0.06 -0.17 [363,] 0.03 1.41 0.45 0.15 0.03 0.12 -0.28 [364,] 0.04 1.13 0.35 0.11 0.04 0.18 -0.29 [365,] 0.02 1.42 0.37 0.10 0.12 0.12 -0.26 [366,] 0.03 0.84 0.12 0.09 0.19 0.16 -0.28 [367,] 0.02 0.63 0.14 0.08 0.02 0.10 -0.23 [368,] 0.02 0.84 0.23 0.09 0.02 0.11 -0.25 [369,] 0.07 2.38 0.74 0.07 0.07 0.30 -0.19 [370,] 0.04 0.87 0.16 0.12 0.04 0.16 -0.29 [371,] 0.07 1.88 0.49 0.28 0.21 0.14 -0.22 [372,] 0.05 3.11 0.82 0.27 0.27 0.11 -0.28 [373,] 0.04 1.38 0.30 0.09 0.21 0.21 -0.29 [374,] 0.03 0.82 0.05 0.12 0.03 0.09 -0.28 [375,] 0.02 0.44 0.03 0.08 0.10 0.10 -0.23 [376,] 0.03 0.66 0.04 0.12 0.15 0.12 -0.28 [377,] 0.03 0.86 0.19 0.08 0.03 0.13 -0.26 [378,] 0.06 2.77 0.88 0.18 0.29 0.24 -0.27 [379,] 0.02 0.44 0.01 0.06 0.02 0.10 -0.23 [380,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [381,] 0.05 1.32 0.20 0.05 0.24 0.24 -0.29 [382,] 0.03 1.51 0.44 0.12 0.15 0.15 -0.27 [383,] 0.03 1.12 0.35 0.14 0.21 0.17 -0.29 [384,] 0.05 2.32 0.74 0.20 0.30 0.20 -0.29 [385,] 0.09 2.51 0.65 0.19 0.09 0.19 -0.04 [386,] 0.02 0.41 0.03 0.08 0.08 0.09 -0.22 [387,] 0.07 2.28 0.71 0.14 0.36 0.29 -0.21 [388,] 0.05 1.03 0.04 0.09 0.23 0.19 -0.29 [389,] 0.04 0.78 0.05 0.07 0.18 0.18 -0.29 [390,] 0.07 2.87 1.02 0.20 0.41 0.27 -0.22 [391,] 0.02 0.55 0.14 0.10 0.08 0.10 -0.23 [392,] 0.05 2.14 0.76 0.20 0.20 0.20 -0.29 [393,] 0.08 4.62 1.22 0.24 0.41 0.16 -0.14 [394,] 0.11 4.58 1.64 0.33 0.65 0.22 0.14 [395,] 0.08 2.72 0.59 0.17 0.08 0.17 -0.11 [396,] 0.02 0.39 0.07 0.09 0.07 0.09 -0.21 [397,] 0.05 1.05 0.07 0.05 0.29 0.24 -0.29 [398,] 0.04 0.90 0.03 0.04 0.16 0.21 -0.29 [399,] 0.04 1.33 0.62 0.17 0.04 0.21 -0.29 [400,] 0.07 1.46 0.10 0.13 0.33 0.20 -0.23 [401,] 0.01 0.35 0.05 0.06 0.03 0.06 -0.18 [402,] 0.02 0.46 0.07 0.07 0.02 0.09 -0.21 [403,] 0.04 1.77 0.63 0.21 0.21 0.17 -0.29 [404,] 0.06 1.84 0.09 0.12 0.40 0.17 -0.27 [405,] 0.08 4.60 1.21 0.32 0.24 0.08 -0.14 [406,] 0.02 0.79 0.15 0.09 0.11 0.11 -0.24 [407,] 0.06 3.37 0.97 0.13 0.32 0.26 -0.24 [408,] 0.04 1.66 0.53 0.14 0.21 0.18 -0.29 [409,] 0.07 1.76 0.46 0.13 0.33 0.26 -0.24 [410,] 0.03 0.79 0.21 0.12 0.15 0.15 -0.27 [411,] 0.07 1.59 0.29 0.14 0.22 0.22 -0.20 [412,] 0.04 1.46 0.28 0.08 0.24 0.20 -0.29 [413,] 0.05 1.48 0.38 0.22 0.22 0.16 -0.28 [414,] 0.04 1.58 0.38 0.15 0.23 0.15 -0.29 [415,] 0.02 0.47 0.03 0.06 0.02 0.11 -0.24 [416,] 0.07 1.47 0.27 0.13 0.07 0.20 -0.23 [417,] 0.03 1.50 0.40 0.11 0.16 0.13 -0.26 [418,] 0.08 2.78 1.13 0.30 0.30 0.23 -0.18 [419,] 0.04 1.03 0.27 0.11 0.19 0.19 -0.29 [420,] 0.05 0.81 0.47 0.19 0.19 0.23 -0.29 [421,] 0.03 0.57 0.10 0.10 0.13 0.13 -0.26 [422,] 0.04 1.20 0.18 0.09 0.04 0.18 -0.29 [423,] 0.16 5.81 2.36 0.31 0.79 0.16 0.91 [424,] 0.02 0.38 0.03 0.09 0.02 0.07 -0.21 [425,] 0.07 1.76 0.46 0.13 0.33 0.26 -0.24 [426,] 0.02 0.59 0.09 0.09 0.11 0.11 -0.24 [427,] 0.04 0.82 0.00 0.04 0.11 0.19 -0.29 [428,] 0.03 0.93 0.24 0.14 0.03 0.14 -0.29 [429,] 0.07 2.28 1.07 0.36 0.36 0.21 -0.21 [430,] 0.05 1.49 0.47 0.19 0.23 0.19 -0.29 [431,] 0.09 2.86 1.34 0.18 0.27 0.36 -0.07 [432,] 0.03 0.60 0.04 0.08 0.14 0.14 -0.27 [433,] 0.03 0.83 0.12 0.12 0.12 0.12 -0.28 [434,] 0.02 0.88 0.25 0.08 0.02 0.08 -0.21 [435,] 0.09 2.51 0.65 0.19 0.09 0.19 -0.04 [436,] 0.04 1.19 0.31 0.13 0.04 0.18 -0.29 [437,] 0.07 2.88 1.03 0.14 0.07 0.27 -0.22 [438,] 0.05 2.23 0.80 0.21 0.27 0.21 -0.28 [439,] 0.05 1.42 0.37 0.21 0.16 0.16 -0.28 [440,] 0.11 2.93 0.76 0.22 0.65 0.22 0.13 [441,] 0.08 3.32 1.18 0.24 0.24 0.24 -0.16 [442,] 0.03 0.71 0.11 0.08 0.08 0.13 -0.26 [443,] 0.04 1.19 0.31 0.13 0.04 0.18 -0.29 [444,] 0.03 0.75 0.05 0.07 0.14 0.17 -0.29 [445,] 0.03 0.70 0.10 0.10 0.03 0.10 -0.26 [446,] 0.03 0.57 0.10 0.10 0.13 0.13 -0.26 [447,] 0.03 0.75 0.05 0.07 0.14 0.17 -0.29 [448,] 0.05 2.22 0.71 0.19 0.24 0.19 -0.29 [449,] 0.11 3.97 1.07 0.21 0.64 0.21 0.12 [450,] 0.07 2.69 1.09 0.22 0.36 0.29 -0.20 [451,] 0.04 1.20 0.18 0.09 0.04 0.18 -0.29 [452,] -0.17 -4.66 -0.26 -0.52 -0.69 -0.69 1.02 [453,] -0.20 -5.40 -0.80 -0.60 -0.20 -1.00 1.72 [454,] -0.12 -4.46 -1.81 -0.60 -0.72 -0.24 -0.01 [455,] -0.18 -5.66 -1.77 -0.53 -0.88 -0.35 1.12 [456,] -0.19 -4.28 -0.02 -0.78 -0.97 -0.97 1.58 [457,] -0.15 -3.37 -0.23 -0.31 -0.15 -0.77 0.59 [458,] -0.17 -6.44 -2.61 -0.70 -0.87 -0.35 1.06 [459,] -0.20 -4.36 -0.30 -0.40 -0.59 -0.79 1.67 [460,] -0.04 -1.47 -0.60 -0.08 -0.24 -0.16 -0.89 [461,] -0.04 -1.22 -0.57 -0.15 -0.11 -0.08 -0.90 [462,] -0.02 -0.63 -0.26 -0.07 -0.07 -0.03 -0.98 [463,] -0.18 -7.36 -2.63 -0.53 -0.18 -0.70 1.09 [464,] -0.18 -7.69 -2.75 -0.92 -0.73 -0.18 1.28 [465,] -0.16 -5.93 -1.60 -0.32 -0.96 -0.32 0.75 [466,] -0.16 -4.97 -2.33 -0.47 -0.16 -0.31 0.64 [467,] -0.13 -3.42 -0.51 -0.13 -0.76 -0.63 0.09 [468,] -0.12 -4.28 -1.16 -0.23 -0.81 -0.35 -0.09 [469,] -0.24 -6.47 -0.96 -0.72 -1.20 -1.20 2.91 [470,] -0.13 -5.30 -1.89 -0.50 -0.63 -0.63 0.08 [471,] -0.17 -7.94 -2.53 -0.84 -0.68 -0.84 0.94 [472,] -0.21 -5.57 -0.82 -0.62 -1.03 -0.82 1.89 [473,] -0.16 -4.38 -1.14 -0.81 -0.16 -0.65 0.79 [474,] -0.30 -8.10 -0.45 -0.90 -1.50 -1.20 5.12 [475,] -0.21 -5.62 -1.46 -0.83 -1.25 -0.42 1.94 [476,] -0.12 -5.13 -1.83 -0.49 -0.61 -0.49 0.01 [477,] -0.14 -3.75 -1.39 -0.56 -0.97 -0.42 0.31 [478,] -0.05 -1.25 -0.07 -0.09 -0.23 -0.09 -0.85 [479,] -0.15 -4.83 -0.60 -0.60 -0.91 -0.60 0.55 [480,] -0.08 -2.13 -0.55 -0.24 -0.08 -0.24 -0.58 [481,] -0.16 -4.98 -1.56 -0.62 -0.16 -0.62 0.65 [482,] -0.05 -1.24 -0.18 -0.09 -0.32 -0.09 -0.86 [483,] -0.21 -3.63 -0.16 -1.04 -0.83 -1.04 1.92 [484,] -0.16 -5.11 -1.60 -0.64 -0.16 -0.80 0.74 [485,] -0.17 -5.29 -1.16 -0.33 -0.99 -0.66 0.86 [486,] -0.11 -4.19 -1.70 -0.23 -0.68 -0.45 -0.13 [487,] -0.10 -3.72 -1.00 -0.10 -0.50 -0.30 -0.31 [488,] -0.25 -8.09 -2.53 -0.51 -1.26 -1.26 3.34 [489,] -0.15 -7.95 -2.29 -0.31 -0.92 -0.61 0.59 [490,] -0.09 -3.87 -1.38 -0.09 -0.09 -0.28 -0.42 [491,] -0.03 -1.61 -0.47 -0.06 -0.19 -0.09 -0.93 [492,] -0.10 -3.63 -1.47 -0.29 -0.59 -0.49 -0.34 [493,] -0.28 -6.07 -1.10 -0.83 -0.83 -1.10 4.18 [494,] -0.13 -3.61 -0.94 -0.13 -0.80 -0.27 0.21 [495,] -0.15 -4.09 -0.61 -0.45 -0.76 -0.76 0.56 [496,] -0.30 -13.93 -4.45 -1.19 -1.78 -1.48 4.97 [497,] -0.17 -7.26 -2.59 -0.69 -0.17 -0.17 1.03 [498,] -0.22 -5.82 -0.86 -0.65 -0.65 -0.86 2.16 [499,] -0.26 -8.26 -1.81 -1.03 -1.03 -1.29 3.53 [500,] -0.17 -5.44 -0.07 -0.51 -0.51 -0.68 0.96 [501,] -0.16 -7.52 -2.40 -0.80 -0.80 -0.64 0.74 [502,] -0.19 -7.09 -2.87 -0.38 -0.96 -0.77 1.50 [503,] -0.16 -3.59 -0.65 -0.33 -0.98 -0.65 0.81 [504,] -0.13 -3.55 -0.53 -0.26 -0.53 -0.66 0.18 [505,] -0.22 -11.31 -3.26 -1.09 -0.22 -0.65 2.22 [506,] -0.09 -2.54 -0.38 -0.28 -0.28 -0.28 -0.40 [507,] -0.11 -3.05 -1.13 -0.45 -0.11 -0.45 -0.13 [508,] -0.10 -3.11 -0.68 -0.29 -0.68 -0.39 -0.36 [509,] -0.07 -2.37 -0.52 -0.15 -0.30 -0.07 -0.63 [510,] -0.05 -1.04 -0.07 -0.05 -0.14 -0.09 -0.85 [511,] -0.19 -4.14 -0.75 -0.56 -1.13 -0.75 1.41 [512,] -0.18 -7.39 -2.64 -0.70 -1.06 -0.70 1.11 [513,] -0.07 -4.13 -1.09 -0.07 -0.36 -0.29 -0.64 [514,] -0.15 -4.87 -0.61 -0.46 -0.76 -0.30 0.57 [515,] -0.07 -1.97 -0.29 -0.07 -0.29 -0.29 -0.64 [516,] -0.24 -7.64 -1.67 -0.96 -0.24 -0.96 2.88 [517,] -0.08 -4.41 -1.16 -0.08 -0.31 -0.31 -0.59 [518,] -0.11 -4.77 -1.70 -0.45 -0.57 -0.23 -0.12 [519,] -0.10 -3.72 -1.00 -0.10 -0.50 -0.30 -0.31 [520,] 0.01 0.35 0.13 0.03 0.05 0.01 -1.00 [521,] -0.10 -5.15 -1.48 -0.30 -0.40 -0.40 -0.33 [522,] -0.07 -2.01 -0.52 -0.22 -0.37 -0.22 -0.62 [523,] -0.18 -5.80 -1.27 -0.36 -0.72 -0.36 1.23 [524,] -0.18 -3.90 -0.71 -0.71 -0.35 -0.89 1.14 [525,] -0.12 -3.19 -0.83 -0.35 -0.71 -0.47 -0.05 [526,] -0.20 -7.41 -3.01 -0.20 -1.00 -1.00 1.73 [527,] -0.12 -3.69 -1.73 -0.35 -0.12 -0.35 -0.10 [528,] -0.19 -5.14 -1.33 -0.38 -0.95 -0.95 1.47 [529,] -0.07 -2.34 -0.51 -0.22 -0.37 -0.22 -0.64 [530,] -0.14 -4.51 -0.21 -0.28 -0.28 -0.56 0.35 [531,] -0.21 -8.67 -3.10 -0.83 -0.21 -0.41 1.90 [532,] -0.17 -5.45 -1.70 -0.51 -0.85 -0.68 0.98 [533,] -0.14 -5.35 -0.58 -0.14 -0.87 -0.43 0.42 [534,] -0.06 -1.61 -0.24 -0.12 -0.30 -0.18 -0.76 [535,] -0.20 -8.43 -3.01 -0.60 -0.80 -0.60 1.74 [536,] -0.15 -3.98 -1.47 -0.74 -0.88 -0.74 0.48 [537,] -0.16 -5.93 -1.60 -0.32 -0.96 -0.32 0.75 [538,] -0.18 -4.90 -1.27 -0.18 -0.54 -0.54 1.24 [539,] -0.10 -2.69 -0.70 -0.40 -0.10 -0.20 -0.32 [540,] -0.09 -2.93 -0.92 -0.18 -0.37 -0.37 -0.43 [541,] -0.24 -4.23 -0.18 -0.48 -0.24 -0.73 2.98 [542,] -0.20 -6.50 -3.05 -0.61 -1.02 -0.81 1.81 [543,] -0.09 -2.00 -0.64 -0.36 -0.36 -0.27 -0.44 [544,] -0.16 -5.13 -1.12 -0.64 -0.96 -0.80 0.75 [545,] -0.11 -2.95 -0.44 -0.22 -0.66 -0.22 -0.19 [546,] -0.14 -3.10 -0.21 -0.71 -0.71 -0.42 0.35 [547,] -0.10 -3.28 -1.54 -0.31 -0.51 -0.10 -0.29 [548,] -0.16 -6.58 -2.35 -0.31 -0.16 -0.31 0.67 [549,] -0.16 -6.55 -2.34 -0.47 -0.78 -0.62 0.66 [550,] -0.16 -5.02 -1.57 -0.31 -0.63 -0.31 0.67 [551,] -0.12 -3.89 -1.82 -0.36 -0.12 -0.12 0.01 [552,] -0.28 -16.16 -4.25 -1.42 -1.13 -1.42 4.47 [553,] -0.26 -12.35 -3.94 -1.05 -1.58 -1.05 3.70 [554,] -0.02 -0.81 -0.29 -0.04 -0.12 -0.06 -0.97 [555,] -0.18 -6.59 -2.67 -0.53 -1.07 -0.53 1.15 [556,] -0.20 -7.35 -2.98 -0.99 -0.99 -0.40 1.69 [557,] -0.13 -3.45 -1.28 -0.26 -0.77 -0.51 0.11 [558,] -0.04 -1.65 -0.67 -0.09 -0.22 -0.18 -0.86 [559,] -0.09 -2.77 -1.30 -0.09 -0.43 -0.17 -0.49 [560,] -0.13 -4.23 -1.32 -0.40 -0.79 -0.40 0.19 [561,] 0.01 0.25 0.10 0.03 0.03 0.01 -1.00 [562,] -0.24 -6.35 -0.35 -0.47 -1.18 -1.18 2.77 [563,] -0.02 -0.87 -0.28 -0.04 -0.09 -0.04 -0.98 [564,] -0.19 -7.09 -2.87 -0.38 -0.96 -0.77 1.50 [565,] -0.29 -7.79 -1.15 -0.58 -1.44 -1.44 4.66 [566,] -0.16 -4.35 -1.61 -0.65 -0.16 -0.81 0.77 [567,] -0.09 -1.99 -0.36 -0.27 -0.09 -0.27 -0.45 [568,] -0.38 -19.73 -2.66 -1.52 -1.90 -1.90 8.80 [569,] -0.13 -3.41 -0.50 -0.13 -0.38 -0.63 0.08 [570,] -0.11 -4.19 -1.70 -0.23 -0.68 -0.45 -0.13 [571,] 0.01 0.22 0.03 0.01 0.02 0.01 -1.00 [572,] -0.30 -5.24 -0.22 -0.60 -0.90 -1.50 5.10 [573,] -0.18 -5.74 -2.69 -0.90 -0.90 -0.72 1.19 [574,] -0.19 -4.10 -0.75 -0.19 -0.56 -0.93 1.37 [575,] -0.17 -5.31 -0.66 -0.66 -0.99 -0.66 0.87 [576,] -0.06 -1.27 -0.09 -0.17 -0.29 -0.12 -0.77 [577,] -0.07 -3.04 -1.09 -0.14 -0.36 -0.29 -0.64 [578,] -0.14 -4.36 -0.95 -0.54 -0.54 -0.54 0.26 [579,] -0.14 -5.34 -2.17 -0.43 -0.87 -0.29 0.42 [580,] -0.03 -1.24 -0.44 -0.09 -0.18 -0.09 -0.94 [581,] -0.07 -1.84 -0.27 -0.07 -0.34 -0.27 -0.68 [582,] -0.05 -1.89 -0.76 -0.20 -0.36 -0.15 -0.82 [583,] -0.14 -5.11 -2.07 -0.41 -0.83 -0.55 0.30 [584,] -0.11 -2.33 -0.16 -0.21 -0.32 -0.32 -0.24 [585,] -0.09 -2.83 -0.35 -0.27 -0.53 -0.18 -0.47 [586,] -0.13 -4.31 -2.02 -0.67 -0.81 -0.67 0.23 [587,] -0.24 -12.48 -3.60 -0.24 -1.20 -1.20 2.92 [588,] -0.22 -10.43 -3.33 -0.22 -1.33 -1.11 2.35 [589,] -0.10 -3.21 -1.51 -0.40 -0.40 -0.40 -0.31 [590,] -0.07 -2.31 -1.08 -0.22 -0.22 -0.14 -0.65 [591,] -0.16 -4.28 -1.11 -0.63 -0.16 -0.32 0.71 [592,] -0.16 -6.62 -2.36 -0.47 -0.95 -0.32 0.69 [593,] -0.07 -3.09 -1.10 -0.15 -0.22 -0.15 -0.63 [594,] -0.23 -6.22 -1.61 -0.46 -1.15 -0.92 2.61 [595,] -0.11 -3.44 -1.08 -0.43 -0.43 -0.32 -0.21 [596,] -0.11 -5.02 -1.60 -0.32 -0.43 -0.21 -0.22 [597,] -0.12 -2.72 -0.19 -0.12 -0.25 -0.62 0.04 [598,] -0.15 -4.66 -1.46 -0.29 -0.73 -0.58 0.44 [599,] -0.10 -3.23 -1.01 -0.20 -0.61 -0.50 -0.31 [600,] -0.02 -0.51 -0.16 -0.07 -0.14 -0.05 -0.96 [601,] -0.09 -3.02 -1.41 -0.28 -0.09 -0.47 -0.40 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 150 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsAddNeg ~ age + yearsmarried + religiousness + occupation + rating, left = -Inf, right = -5, data = Affairs) $terms affairsAddNeg ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairsAddNeg, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairsAddNeg 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairsAddNeg, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairsAddNeg age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 0 150 451 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation -10.608161 0.050347 -0.161852 0.476324 -0.106006 rating logSigma 0.712242 2.244542 $left [1] -Inf $right [1] -5 class [1] "censReg" "maxLik" "maxim" "list" print( x, digits = 2 ) Call: censReg(formula = affairsAddNeg ~ age + yearsmarried + religiousness + occupation + rating, left = -Inf, right = -5, data = Affairs) Coefficients: (Intercept) age yearsmarried religiousness occupation -13.17 0.18 -0.55 1.69 -0.33 rating logSigma 2.28 2.11 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating 0.04 -0.13 0.39 -0.08 0.53 printME( margEff( x ) ) age yearsmarried religiousness occupation rating 0.042 -0.130 0.394 -0.076 0.534 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0.000 0.000 0.000 0.000 yearsmarried 0 0.001 0.000 0.000 0.000 religiousness 0 0.000 0.009 0.000 0.000 occupation 0 0.000 0.000 0.004 0.000 rating 0 0.000 0.000 0.000 0.009 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age 0.042 NA NA NA yearsmarried -0.130 NA NA NA religiousness 0.394 NA NA NA occupation -0.076 NA NA NA rating 0.534 NA NA NA print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-Likelihood: -705.58 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) -13.174 2.741 -4.8 2e-06 *** age 0.179 0.079 2.3 0.02 * yearsmarried -0.554 0.135 -4.1 4e-05 *** religiousness 1.686 0.404 4.2 3e-05 *** occupation -0.326 0.254 -1.3 0.20 rating 2.285 0.408 5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsAddNeg ~ age + yearsmarried + religiousness + occupation + rating, left = -Inf, right = -5, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 0 150 451 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) -13.174 2.741 -4.8 2e-06 *** age 0.179 0.079 2.3 0.02 * yearsmarried -0.554 0.135 -4.1 4e-05 *** religiousness 1.686 0.404 4.2 3e-05 *** occupation -0.326 0.254 -1.3 0.20 rating 2.285 0.408 5.6 2e-08 *** logSigma 2.110 0.067 31.4 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -705.58 on 7 Df Warning message: In summary.margEff.censReg(margEff(x, calcVCov = sumMeCalcVCov, : cannot calculate standard errors, t-values, and P-values, because the marginal effects do not have an attribute 'vcov': please set attribute 'calcVCov' of the 'margEff' method to 'TRUE' > round( coef( estResultAddNeg ), 2 ) (Intercept) age yearsmarried religiousness occupation -13.17 0.18 -0.55 1.69 -0.33 rating logSigma 2.28 2.11 > round( coef( estResultAddNeg, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation -13.17 0.18 -0.55 1.69 -0.33 rating sigma 2.28 8.25 > round( vcov( estResultAddNeg ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.52 -0.12 0.09 -0.40 -0.18 -0.62 age -0.12 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.09 -0.01 0.02 -0.01 0.00 0.00 religiousness -0.40 0.00 -0.01 0.16 0.01 0.00 occupation -0.18 0.00 0.00 0.01 0.06 -0.01 rating -0.62 0.00 0.00 0.00 -0.01 0.17 logSigma -0.01 0.00 0.00 0.00 0.00 0.01 logSigma (Intercept) -0.01 age 0.00 yearsmarried 0.00 religiousness 0.00 occupation 0.00 rating 0.01 logSigma 0.00 > round( vcov( estResultAddNeg, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.52 -0.12 0.09 -0.40 -0.18 -0.62 age -0.12 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.09 -0.01 0.02 -0.01 0.00 0.00 religiousness -0.40 0.00 -0.01 0.16 0.01 0.00 occupation -0.18 0.00 0.00 0.01 0.06 -0.01 rating -0.62 0.00 0.00 0.00 -0.01 0.17 sigma -0.06 0.00 -0.01 0.04 -0.01 0.06 sigma (Intercept) -0.06 age 0.00 yearsmarried -0.01 religiousness 0.04 occupation -0.01 rating 0.06 sigma 0.31 > logLik( estResultAddNeg ) 'log Lik.' -705.58 (df=7) > nobs( estResultAddNeg ) [1] 601 > extractAIC( estResultAddNeg ) [1] 7.0 1425.2 > > ## estimation with left and right censoring > estResultBoth <- censReg( affairsFormula, data = Affairs, right = 4 ) Warning message: In censReg(affairsFormula, data = Affairs, right = 4) : at least one value of the endogenous variable is larger than the right limit > printAll( estResultBoth, logSigmaFalse = TRUE ) $maximum [1] -500.04 $estimate (Intercept) age yearsmarried religiousness occupation 7.90 -0.18 0.53 -1.62 0.32 rating logSigma -2.21 2.07 $gradient (Intercept) age yearsmarried religiousness occupation 0 0 0 0 0 rating logSigma 0 0 $hessian (Intercept) age yearsmarried religiousness occupation rating (Intercept) -5.1 -167.5 -44.9 -15.2 -21.5 -18.9 age -167.5 -5950.1 -1677.3 -510.0 -724.9 -616.1 yearsmarried -44.9 -1677.3 -548.7 -142.0 -193.4 -161.2 religiousness -15.2 -510.0 -142.0 -52.2 -63.9 -56.5 occupation -21.5 -724.9 -193.4 -63.9 -107.9 -80.6 rating -18.9 -616.1 -161.2 -56.5 -80.6 -77.3 logSigma -31.0 -989.9 -232.1 -100.1 -127.8 -129.9 logSigma (Intercept) -31.0 age -989.9 yearsmarried -232.1 religiousness -100.1 occupation -127.8 rating -129.9 logSigma -325.1 $last.step NULL $fixed (Intercept) age yearsmarried religiousness occupation FALSE FALSE FALSE FALSE FALSE rating logSigma FALSE FALSE $type [1] "Newton-Raphson maximisation" $gradientObs (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.06 -2.14 -0.58 -0.17 -0.41 -0.23 -0.28 [2,] -0.04 -0.95 -0.14 -0.14 -0.21 -0.14 -0.29 [3,] -0.10 -3.24 -1.52 -0.10 -0.10 -0.41 0.01 [4,] -0.02 -1.10 -0.29 -0.10 -0.12 -0.10 -0.22 [5,] -0.07 -1.49 -0.05 -0.14 -0.41 -0.20 -0.24 [6,] -0.03 -0.87 -0.04 -0.05 -0.14 -0.14 -0.26 [7,] -0.05 -1.21 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.06 -3.19 -0.84 -0.11 -0.22 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-0.04 -0.93 -0.03 -0.04 -0.17 -0.21 -0.29 [399,] -0.04 -1.35 -0.63 -0.17 -0.04 -0.21 -0.29 [400,] -0.07 -1.51 -0.10 -0.14 -0.34 -0.21 -0.24 [401,] -0.01 -0.36 -0.05 -0.07 -0.03 -0.07 -0.17 [402,] -0.02 -0.47 -0.07 -0.07 -0.02 -0.09 -0.21 [403,] -0.04 -1.80 -0.64 -0.21 -0.21 -0.17 -0.29 [404,] -0.06 -1.90 -0.09 -0.12 -0.42 -0.18 -0.27 [405,] -0.08 -4.66 -1.23 -0.33 -0.25 -0.08 -0.16 [406,] -0.02 -0.80 -0.15 -0.09 -0.11 -0.11 -0.24 [407,] -0.07 -3.41 -0.98 -0.13 -0.33 -0.26 -0.25 [408,] -0.04 -1.68 -0.54 -0.14 -0.21 -0.18 -0.29 [409,] -0.07 -1.81 -0.47 -0.13 -0.34 -0.27 -0.24 [410,] -0.03 -0.81 -0.21 -0.12 -0.15 -0.15 -0.27 [411,] -0.07 -1.64 -0.30 -0.15 -0.22 -0.22 -0.21 [412,] -0.04 -1.49 -0.28 -0.08 -0.24 -0.20 -0.29 [413,] -0.06 -1.52 -0.39 -0.23 -0.23 -0.17 -0.28 [414,] -0.04 -1.61 -0.38 -0.15 -0.23 -0.15 -0.29 [415,] -0.02 -0.48 -0.03 -0.07 -0.02 -0.11 -0.24 [416,] -0.07 -1.51 -0.28 -0.14 -0.07 -0.21 -0.23 [417,] -0.03 -1.50 -0.40 -0.11 -0.16 -0.13 -0.26 [418,] -0.08 -2.84 -1.15 -0.31 -0.31 -0.23 -0.19 [419,] -0.04 -1.06 -0.28 -0.12 -0.20 -0.20 -0.29 [420,] -0.05 -0.84 -0.48 -0.19 -0.19 -0.24 -0.29 [421,] -0.03 -0.58 -0.11 -0.11 -0.13 -0.13 -0.26 [422,] -0.05 -1.22 -0.18 -0.09 -0.05 -0.18 -0.29 [423,] -0.16 -5.98 -2.43 -0.32 -0.81 -0.16 0.89 [424,] -0.02 -0.39 -0.03 -0.09 -0.02 -0.07 -0.21 [425,] -0.07 -1.81 -0.47 -0.13 -0.34 -0.27 -0.24 [426,] -0.02 -0.61 -0.09 -0.09 -0.11 -0.11 -0.24 [427,] -0.04 -0.84 0.00 -0.04 -0.11 -0.19 -0.29 [428,] -0.04 -0.95 -0.25 -0.14 -0.04 -0.14 -0.29 [429,] -0.07 -2.35 -1.10 -0.37 -0.37 -0.22 -0.21 [430,] -0.05 -1.53 -0.48 -0.19 -0.24 -0.19 -0.29 [431,] -0.09 -2.93 -1.38 -0.18 -0.28 -0.37 -0.08 [432,] -0.03 -0.61 -0.04 -0.08 -0.14 -0.14 -0.26 [433,] -0.03 -0.85 -0.13 -0.13 -0.13 -0.13 -0.28 [434,] -0.02 -0.87 -0.25 -0.08 -0.02 -0.08 -0.20 [435,] -0.10 -2.58 -0.67 -0.19 -0.10 -0.19 -0.05 [436,] -0.05 -1.22 -0.32 -0.14 -0.05 -0.18 -0.29 [437,] -0.07 -2.92 -1.04 -0.14 -0.07 -0.28 -0.23 [438,] -0.05 -2.28 -0.81 -0.22 -0.27 -0.22 -0.28 [439,] -0.05 -1.46 -0.38 -0.22 -0.16 -0.16 -0.28 [440,] -0.11 -3.03 -0.78 -0.22 -0.67 -0.22 0.13 [441,] -0.08 -3.38 -1.21 -0.24 -0.24 -0.24 -0.17 [442,] -0.03 -0.73 -0.11 -0.08 -0.08 -0.13 -0.26 [443,] -0.05 -1.22 -0.32 -0.14 -0.05 -0.18 -0.29 [444,] -0.03 -0.77 -0.05 -0.07 -0.14 -0.17 -0.28 [445,] -0.03 -0.72 -0.11 -0.11 -0.03 -0.11 -0.26 [446,] -0.03 -0.58 -0.11 -0.11 -0.13 -0.13 -0.26 [447,] -0.03 -0.77 -0.05 -0.07 -0.14 -0.17 -0.28 [448,] -0.05 -2.25 -0.72 -0.19 -0.24 -0.19 -0.29 [449,] -0.11 -4.08 -1.10 -0.22 -0.66 -0.22 0.10 [450,] -0.07 -2.76 -1.12 -0.22 -0.37 -0.30 -0.20 [451,] -0.05 -1.22 -0.18 -0.09 -0.05 -0.18 -0.29 [452,] 0.18 4.91 0.27 0.55 0.73 0.73 1.09 [453,] 0.21 5.70 0.84 0.63 0.21 1.06 1.81 [454,] 0.16 5.87 2.38 0.79 0.95 0.32 0.83 [455,] 0.15 4.65 1.45 0.44 0.73 0.29 0.60 [456,] 0.20 4.47 0.03 0.81 1.02 1.02 1.60 [457,] 0.16 3.54 0.24 0.32 0.16 0.80 0.63 [458,] 0.14 5.30 2.15 0.57 0.72 0.29 0.57 [459,] 0.22 4.89 0.33 0.44 0.67 0.89 2.29 [460,] 0.04 1.64 0.67 0.09 0.27 0.18 -0.88 [461,] 0.04 1.37 0.64 0.17 0.13 0.09 -0.88 [462,] 0.02 0.74 0.30 0.08 0.08 0.04 -0.97 [463,] 0.20 8.59 3.07 0.61 0.20 0.82 1.83 [464,] 0.15 6.33 2.26 0.75 0.60 0.15 0.69 [465,] 0.13 4.92 1.33 0.27 0.80 0.27 0.41 [466,] 0.13 4.15 1.94 0.39 0.13 0.26 0.35 [467,] 0.13 3.63 0.54 0.13 0.81 0.67 0.14 [468,] 0.15 5.73 1.55 0.31 1.08 0.46 0.76 [469,] 0.26 7.00 1.04 0.78 1.30 1.30 3.38 [470,] 0.13 5.63 2.01 0.54 0.67 0.67 0.13 [471,] 0.18 8.40 2.68 0.89 0.72 0.89 1.02 [472,] 0.23 6.19 0.92 0.69 1.15 0.92 2.48 [473,] 0.17 4.60 1.19 0.85 0.17 0.68 0.83 [474,] 0.25 6.68 0.37 0.74 1.24 0.99 3.00 [475,] 0.17 4.56 1.18 0.68 1.01 0.34 1.03 [476,] 0.13 5.49 1.96 0.52 0.65 0.52 0.08 [477,] 0.17 4.66 1.73 0.69 1.21 0.52 1.10 [478,] 0.05 1.34 0.07 0.10 0.25 0.10 -0.85 [479,] 0.16 5.07 0.63 0.63 0.95 0.63 0.59 [480,] 0.08 2.27 0.59 0.25 0.08 0.25 -0.55 [481,] 0.17 5.29 1.65 0.66 0.17 0.66 0.72 [482,] 0.05 1.34 0.20 0.10 0.35 0.10 -0.84 [483,] 0.22 3.78 0.16 1.08 0.86 1.08 1.94 [484,] 0.17 5.39 1.69 0.67 0.17 0.84 0.79 [485,] 0.19 6.24 1.36 0.39 1.17 0.78 1.59 [486,] 0.15 5.68 2.30 0.31 0.92 0.61 0.74 [487,] 0.14 5.31 1.44 0.14 0.72 0.43 0.57 [488,] 0.21 6.62 2.07 0.41 1.03 1.03 1.88 [489,] 0.19 9.67 2.79 0.37 1.12 0.74 1.39 [490,] 0.14 5.80 2.07 0.14 0.14 0.41 0.48 [491,] 0.04 1.86 0.54 0.07 0.21 0.11 -0.92 [492,] 0.10 3.88 1.57 0.31 0.63 0.52 -0.30 [493,] 0.23 4.98 0.90 0.68 0.68 0.90 2.39 [494,] 0.11 3.06 0.79 0.11 0.68 0.23 0.14 [495,] 0.16 4.29 0.64 0.48 0.79 0.79 0.59 [496,] 0.25 11.54 3.68 0.98 1.47 1.23 2.95 [497,] 0.14 6.01 2.15 0.57 0.14 0.14 0.56 [498,] 0.24 6.43 0.95 0.71 0.71 0.95 2.73 [499,] 0.28 8.84 1.93 1.10 1.10 1.38 3.94 [500,] 0.18 5.71 0.07 0.54 0.54 0.71 1.01 [501,] 0.17 8.01 2.56 0.85 0.85 0.68 0.83 [502,] 0.16 5.82 2.36 0.31 0.79 0.63 0.81 [503,] 0.19 4.24 0.77 0.39 1.16 0.77 1.54 [504,] 0.14 3.74 0.55 0.28 0.55 0.69 0.21 [505,] 0.24 12.56 3.62 1.21 0.24 0.72 2.83 [506,] 0.10 2.68 0.40 0.30 0.30 0.30 -0.38 [507,] 0.12 3.23 1.20 0.48 0.12 0.48 -0.10 [508,] 0.10 3.28 0.72 0.31 0.72 0.41 -0.34 [509,] 0.12 3.96 0.87 0.25 0.49 0.12 0.27 [510,] 0.05 1.14 0.08 0.05 0.16 0.10 -0.83 [511,] 0.21 4.70 0.85 0.64 1.28 0.85 2.05 [512,] 0.20 8.59 3.07 0.82 1.23 0.82 1.82 [513,] 0.08 4.57 1.20 0.08 0.40 0.32 -0.59 [514,] 0.18 5.88 0.74 0.55 0.92 0.37 1.34 [515,] 0.08 2.10 0.31 0.08 0.31 0.31 -0.62 [516,] 0.26 8.29 1.81 1.04 0.26 1.04 3.38 [517,] 0.09 4.87 1.28 0.09 0.34 0.34 -0.54 [518,] 0.15 6.46 2.31 0.61 0.77 0.31 0.74 [519,] 0.14 5.31 1.44 0.14 0.72 0.43 0.57 [520,] -0.01 -0.21 -0.08 -0.02 -0.03 -0.01 -1.00 [521,] 0.11 5.55 1.60 0.32 0.43 0.43 -0.28 [522,] 0.08 2.15 0.56 0.24 0.40 0.24 -0.60 [523,] 0.15 4.76 1.04 0.30 0.59 0.30 0.66 [524,] 0.19 4.09 0.74 0.74 0.37 0.93 1.18 [525,] 0.13 3.38 0.88 0.38 0.75 0.50 -0.01 [526,] 0.16 6.08 2.47 0.16 0.82 0.82 0.94 [527,] 0.16 4.96 2.33 0.47 0.16 0.47 0.77 [528,] 0.22 5.84 1.51 0.43 1.08 1.08 2.12 [529,] 0.08 2.49 0.54 0.23 0.39 0.23 -0.62 [530,] 0.15 4.77 0.22 0.30 0.30 0.60 0.40 [531,] 0.17 7.11 2.54 0.68 0.17 0.34 1.04 [532,] 0.20 6.38 1.99 0.60 1.00 0.80 1.70 [533,] 0.18 6.59 0.71 0.18 1.07 0.53 1.22 [534,] 0.06 1.71 0.25 0.13 0.32 0.19 -0.75 [535,] 0.16 6.92 2.47 0.49 0.66 0.49 0.95 [536,] 0.15 4.17 1.54 0.77 0.93 0.77 0.50 [537,] 0.13 4.92 1.33 0.27 0.80 0.27 0.41 [538,] 0.15 4.02 1.04 0.15 0.45 0.45 0.66 [539,] 0.11 2.88 0.75 0.43 0.11 0.21 -0.28 [540,] 0.10 3.16 0.99 0.20 0.39 0.39 -0.39 [541,] 0.20 3.45 0.15 0.39 0.20 0.59 1.64 [542,] 0.17 5.31 2.49 0.50 0.83 0.66 0.97 [543,] 0.10 2.12 0.67 0.38 0.38 0.29 -0.42 [544,] 0.17 5.38 1.18 0.67 1.01 0.84 0.78 [545,] 0.15 4.03 0.60 0.30 0.90 0.30 0.67 [546,] 0.15 3.24 0.22 0.74 0.74 0.44 0.37 [547,] 0.09 2.97 1.39 0.28 0.46 0.09 -0.07 [548,] 0.13 5.52 1.97 0.26 0.13 0.26 0.38 [549,] 0.19 7.90 2.82 0.56 0.94 0.75 1.43 [550,] 0.13 4.17 1.30 0.26 0.52 0.26 0.37 [551,] 0.11 3.39 1.59 0.32 0.11 0.11 0.06 [552,] 0.30 17.17 4.52 1.51 1.20 1.51 4.84 [553,] 0.22 10.16 3.24 0.86 1.30 0.86 2.12 [554,] 0.02 0.99 0.35 0.05 0.14 0.07 -0.97 [555,] 0.15 5.42 2.20 0.44 0.88 0.44 0.62 [556,] 0.16 6.01 2.44 0.81 0.81 0.32 0.90 [557,] 0.16 4.43 1.64 0.33 0.98 0.66 0.93 [558,] 0.05 1.83 0.74 0.10 0.25 0.20 -0.85 [559,] 0.08 2.66 1.25 0.08 0.42 0.17 -0.15 [560,] 0.17 5.36 1.68 0.50 1.01 0.50 1.00 [561,] 0.00 -0.15 -0.06 -0.02 -0.02 0.00 -1.00 [562,] 0.26 6.89 0.38 0.51 1.28 1.28 3.25 [563,] 0.02 1.11 0.35 0.05 0.12 0.05 -0.96 [564,] 0.16 5.82 2.36 0.31 0.79 0.63 0.81 [565,] 0.24 6.41 0.95 0.47 1.19 1.19 2.71 [566,] 0.17 4.60 1.70 0.68 0.17 0.85 0.83 [567,] 0.10 2.10 0.38 0.29 0.10 0.29 -0.43 [568,] 0.32 16.71 2.25 1.29 1.61 1.61 5.62 [569,] 0.13 3.61 0.54 0.13 0.40 0.67 0.13 [570,] 0.15 5.68 2.30 0.31 0.92 0.61 0.74 [571,] -0.01 -0.16 -0.02 -0.01 -0.02 -0.01 -1.00 [572,] 0.25 4.32 0.18 0.49 0.74 1.23 2.98 [573,] 0.21 6.61 3.10 1.03 1.03 0.83 1.88 [574,] 0.21 4.68 0.85 0.21 0.64 1.06 2.03 [575,] 0.17 5.58 0.70 0.70 1.05 0.70 0.92 [576,] 0.06 1.34 0.09 0.18 0.31 0.12 -0.77 [577,] 0.08 3.34 1.19 0.16 0.40 0.32 -0.60 [578,] 0.14 4.59 1.00 0.57 0.57 0.57 0.30 [579,] 0.12 4.50 1.82 0.36 0.73 0.24 0.24 [580,] 0.03 1.40 0.50 0.10 0.20 0.10 -0.93 [581,] 0.07 1.96 0.29 0.07 0.36 0.29 -0.67 [582,] 0.06 2.05 0.83 0.22 0.39 0.17 -0.81 [583,] 0.17 6.40 2.59 0.52 1.04 0.69 1.11 [584,] 0.11 2.47 0.17 0.22 0.34 0.34 -0.20 [585,] 0.09 3.03 0.38 0.28 0.57 0.19 -0.43 [586,] 0.14 4.55 2.13 0.71 0.85 0.71 0.28 [587,] 0.20 10.27 2.96 0.20 0.99 0.99 1.66 [588,] 0.18 8.57 2.73 0.18 1.09 0.91 1.31 [589,] 0.11 3.45 1.62 0.43 0.43 0.43 -0.27 [590,] 0.12 3.92 1.84 0.37 0.37 0.24 0.26 [591,] 0.19 5.11 1.32 0.76 0.19 0.38 1.46 [592,] 0.13 5.52 1.97 0.39 0.79 0.26 0.38 [593,] 0.12 5.22 1.86 0.25 0.37 0.25 0.28 [594,] 0.19 5.07 1.31 0.38 0.94 0.75 1.43 [595,] 0.11 3.68 1.15 0.46 0.46 0.34 -0.17 [596,] 0.15 7.00 2.23 0.45 0.60 0.30 0.66 [597,] 0.13 2.86 0.20 0.13 0.26 0.65 0.07 [598,] 0.18 5.72 1.79 0.36 0.89 0.72 1.23 [599,] 0.11 3.44 1.07 0.21 0.64 0.54 -0.27 [600,] 0.03 0.56 0.18 0.08 0.15 0.05 -0.96 [601,] 0.10 3.22 1.51 0.30 0.10 0.50 -0.36 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 150 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness occupation 1.0000 32.4875 8.1777 3.1165 4.1947 rating 3.9318 $call censReg(formula = affairsFormula, right = 4, data = Affairs) $terms affairs ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairs 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairs age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "numeric" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 601 451 70 80 $df.residual [1] 594 $start (Intercept) age yearsmarried religiousness occupation 5.608161 -0.050347 0.161852 -0.476324 0.106006 rating logSigma -0.712242 2.244542 $left [1] 0 $right [1] 4 class [1] "censReg" "maxLik" "maxim" "list" print( x, digits = 2 ) Call: censReg(formula = affairsFormula, right = 4, data = Affairs) Coefficients: (Intercept) age yearsmarried religiousness occupation 7.90 -0.18 0.53 -1.62 0.32 rating logSigma -2.21 2.07 print( x, logSigma = FALSE, digits = 2 ) Call: censReg(formula = affairsFormula, right = 4, data = Affairs) Coefficients: (Intercept) age yearsmarried religiousness occupation 7.90 -0.18 0.53 -1.62 0.32 rating sigma -2.21 7.94 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness occupation rating -0.02 0.07 -0.20 0.04 -0.27 printME( margEff( x ) ) age yearsmarried religiousness occupation rating -0.022 0.065 -0.199 0.040 -0.271 attr(,"vcov") age yearsmarried religiousness occupation rating age 0 0 0.000 0.000 0.000 yearsmarried 0 0 0.000 0.000 0.000 religiousness 0 0 0.002 0.000 0.000 occupation 0 0 0.000 0.001 0.000 rating 0 0 0.000 0.000 0.002 attr(,"df.residual") [1] 594 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.0218 0.0096 -2.3 0.02 * yearsmarried 0.0654 0.0161 4.1 5e-05 *** religiousness -0.1987 0.0483 -4.1 4e-05 *** occupation 0.0399 0.0310 1.3 0.20 rating -0.2713 0.0488 -5.6 4e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-Likelihood: -500.04 7 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 7.90 2.80 2.8 0.005 ** age -0.18 0.08 -2.2 0.026 * yearsmarried 0.53 0.14 3.8 2e-04 *** religiousness -1.62 0.42 -3.8 1e-04 *** occupation 0.32 0.25 1.3 0.202 rating -2.21 0.45 -4.9 9e-07 *** logSigma 2.07 0.11 18.8 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsFormula, right = 4, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 70 80 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 7.90 2.80 2.8 0.005 ** age -0.18 0.08 -2.2 0.026 * yearsmarried 0.53 0.14 3.8 2e-04 *** religiousness -1.62 0.42 -3.8 1e-04 *** occupation 0.32 0.25 1.3 0.202 rating -2.21 0.45 -4.9 9e-07 *** logSigma 2.07 0.11 18.8 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -500.04 on 7 Df print( summary( x ), logSigma = FALSE, digits = sDigits ) Call: censReg(formula = affairsFormula, right = 4, data = Affairs) Observations: Total Left-censored Uncensored Right-censored 601 451 70 80 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 7.90 2.80 2.8 0.005 ** age -0.18 0.08 -2.2 0.026 * yearsmarried 0.53 0.14 3.8 2e-04 *** religiousness -1.62 0.42 -3.8 1e-04 *** occupation 0.32 0.25 1.3 0.202 rating -2.21 0.45 -4.9 9e-07 *** sigma 7.94 0.88 9.1 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -500.04 on 7 Df > round( coef( estResultBoth ), 2 ) (Intercept) age yearsmarried religiousness occupation 7.90 -0.18 0.53 -1.62 0.32 rating logSigma -2.21 2.07 > round( coef( estResultBoth, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation 7.90 -0.18 0.53 -1.62 0.32 rating sigma -2.21 7.94 > round( vcov( estResultBoth ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.86 -0.13 0.12 -0.48 -0.15 -0.75 age -0.13 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.12 -0.01 0.02 -0.02 0.00 0.00 religiousness -0.48 0.00 -0.02 0.18 0.00 0.03 occupation -0.15 0.00 0.00 0.00 0.06 -0.01 rating -0.75 0.00 0.00 0.03 -0.01 0.20 logSigma 0.06 0.00 0.01 -0.02 0.00 -0.02 logSigma (Intercept) 0.06 age 0.00 yearsmarried 0.01 religiousness -0.02 occupation 0.00 rating -0.02 logSigma 0.01 > round( vcov( estResultBoth, logSigma = FALSE ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 7.86 -0.13 0.12 -0.48 -0.15 -0.75 age -0.13 0.01 -0.01 0.00 0.00 0.00 yearsmarried 0.12 -0.01 0.02 -0.02 0.00 0.00 religiousness -0.48 0.00 -0.02 0.18 0.00 0.03 occupation -0.15 0.00 0.00 0.00 0.06 -0.01 rating -0.75 0.00 0.00 0.03 -0.01 0.20 sigma 0.51 -0.02 0.05 -0.14 0.03 -0.19 sigma (Intercept) 0.51 age -0.02 yearsmarried 0.05 religiousness -0.14 occupation 0.03 rating -0.19 sigma 0.77 > round( coef( summary( estResultBoth ) ), 2 ) Estimate Std. error t value Pr(> t) (Intercept) 7.90 2.80 2.82 0.00 age -0.18 0.08 -2.22 0.03 yearsmarried 0.53 0.14 3.77 0.00 religiousness -1.62 0.42 -3.81 0.00 occupation 0.32 0.25 1.28 0.20 rating -2.21 0.45 -4.91 0.00 logSigma 2.07 0.11 18.77 0.00 > round( coef( summary( estResultBoth ), logSigma = FALSE ), 2 ) Estimate Std. error t value Pr(> t) (Intercept) 7.90 2.80 2.82 0.00 age -0.18 0.08 -2.22 0.03 yearsmarried 0.53 0.14 3.77 0.00 religiousness -1.62 0.42 -3.81 0.00 occupation 0.32 0.25 1.28 0.20 rating -2.21 0.45 -4.91 0.00 sigma 7.94 0.88 9.06 0.00 > logLik( estResultBoth ) 'log Lik.' -500.04 (df=7) > nobs( estResultBoth ) [1] 601 > extractAIC( estResultBoth ) [1] 7.0 1014.1 > round( estfun( estResultBoth )[ 20 * c(1:30), ], 2 ) (Intercept) age yearsmarried religiousness occupation rating logSigma [1,] -0.05 -1.41 -0.52 -0.10 -0.05 -0.26 -0.29 [2,] -0.02 -0.45 -0.02 -0.06 -0.02 -0.10 -0.23 [3,] -0.05 -1.60 -0.50 -0.20 -0.30 -0.20 -0.29 [4,] -0.10 -5.95 -1.57 -0.21 -0.73 -0.21 0.04 [5,] -0.05 -1.74 -0.71 -0.19 -0.28 -0.24 -0.29 [6,] -0.04 -1.00 -0.26 -0.15 -0.07 -0.15 -0.29 [7,] -0.07 -1.82 -0.27 -0.07 -0.34 -0.27 -0.24 [8,] -0.03 -1.09 -0.14 -0.07 -0.17 -0.17 -0.28 [9,] -0.06 -2.26 -0.91 -0.24 -0.30 -0.24 -0.27 [10,] -0.02 -0.45 -0.03 -0.08 -0.10 -0.10 -0.23 [11,] -0.03 -1.22 -0.49 -0.16 -0.13 -0.16 -0.28 [12,] -0.06 -2.16 -0.88 -0.23 -0.23 -0.23 -0.27 [13,] -0.04 -1.11 -0.29 -0.08 -0.04 -0.21 -0.29 [14,] -0.04 -1.11 -0.06 -0.04 -0.21 -0.21 -0.29 [15,] -0.04 -1.33 -0.36 -0.18 -0.25 -0.14 -0.29 [16,] -0.04 -1.87 -0.54 -0.14 -0.07 -0.14 -0.29 [17,] -0.05 -1.41 -0.52 -0.10 -0.05 -0.26 -0.29 [18,] -0.02 -0.59 0.00 -0.07 -0.13 -0.11 -0.24 [19,] -0.04 -0.81 -0.06 -0.07 -0.18 -0.18 -0.29 [20,] -0.07 -1.51 -0.10 -0.14 -0.34 -0.21 -0.24 [21,] -0.05 -0.84 -0.48 -0.19 -0.19 -0.24 -0.29 [22,] -0.11 -3.03 -0.78 -0.22 -0.67 -0.22 0.13 [23,] 0.04 1.64 0.67 0.09 0.27 0.18 -0.88 [24,] 0.08 2.27 0.59 0.25 0.08 0.25 -0.55 [25,] 0.18 5.71 0.07 0.54 0.54 0.71 1.01 [26,] -0.01 -0.21 -0.08 -0.02 -0.03 -0.01 -1.00 [27,] 0.10 3.16 0.99 0.20 0.39 0.39 -0.39 [28,] 0.17 5.36 1.68 0.50 1.01 0.50 1.00 [29,] 0.03 1.40 0.50 0.10 0.20 0.10 -0.93 [30,] 0.03 0.56 0.18 0.08 0.15 0.05 -0.96 > round( meat( estResultBoth ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 0.01 0.28 0.08 0.03 0.04 0.03 age 0.28 10.34 2.91 0.87 1.22 1.05 yearsmarried 0.08 2.91 0.94 0.24 0.33 0.28 religiousness 0.03 0.87 0.24 0.09 0.10 0.09 occupation 0.04 1.22 0.33 0.10 0.18 0.13 rating 0.03 1.05 0.28 0.09 0.13 0.13 logSigma 0.05 1.72 0.40 0.16 0.21 0.21 logSigma (Intercept) 0.05 age 1.72 yearsmarried 0.40 religiousness 0.16 occupation 0.21 rating 0.21 logSigma 0.53 > round( bread( estResultBoth ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 4724.82 -77.51 73.42 -289.30 -92.94 -449.21 age -77.51 3.84 -5.20 1.72 -2.64 2.69 yearsmarried 73.42 -5.20 11.98 -10.05 2.94 -2.97 religiousness -289.30 1.72 -10.05 108.25 0.56 15.81 occupation -92.94 -2.64 2.94 0.56 38.74 -6.48 rating -449.21 2.69 -2.97 15.81 -6.48 121.61 logSigma 38.79 -1.16 3.42 -10.38 1.98 -14.20 logSigma (Intercept) 38.79 age -1.16 yearsmarried 3.42 religiousness -10.38 occupation 1.98 rating -14.20 logSigma 7.32 > round( sandwich( estResultBoth ), 2 ) (Intercept) age yearsmarried religiousness occupation rating (Intercept) 9.06 -0.17 0.18 -0.54 -0.11 -0.79 age -0.17 0.01 -0.01 0.01 -0.01 0.01 yearsmarried 0.18 -0.01 0.02 -0.02 0.01 -0.01 religiousness -0.54 0.01 -0.02 0.19 -0.01 0.04 occupation -0.11 -0.01 0.01 -0.01 0.06 -0.01 rating -0.79 0.01 -0.01 0.04 -0.01 0.20 logSigma 0.04 0.00 0.01 -0.02 0.00 -0.02 logSigma (Intercept) 0.04 age 0.00 yearsmarried 0.01 religiousness -0.02 occupation 0.00 rating -0.02 logSigma 0.01 > # all.equal( sandwich( estResultBoth ), vcov( estResultBoth ) ) > waldtest( estResultBoth, . ~ . - age ) Wald test Model 1: affairs ~ age + yearsmarried + religiousness + occupation + rating Model 2: affairs ~ yearsmarried + religiousness + occupation + rating Res.Df Df Chisq Pr(>Chisq) 1 594 2 595 -1 4.94 0.026 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Warning message: In censReg(formula = affairs ~ yearsmarried + religiousness + occupation + : at least one value of the endogenous variable is larger than the right limit > waldtest( estResultBoth, . ~ . - age, vcov = sandwich( estResultBoth ) ) Wald test Model 1: affairs ~ age + yearsmarried + religiousness + occupation + rating Model 2: affairs ~ yearsmarried + religiousness + occupation + rating Res.Df Df Chisq Pr(>Chisq) 1 594 2 595 -1 4.18 0.041 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Warning message: In censReg(formula = affairs ~ yearsmarried + religiousness + occupation + : at least one value of the endogenous variable is larger than the right limit > > ## with empty levels > Affairs2 <- Affairs > Affairs2$religiousness <- as.factor( Affairs2$religiousness ) > Affairs2 <- Affairs2[ Affairs2$religiousness != "5", ] > estResultEmpty <- censReg( affairsFormula, data = Affairs2 ) > printAll( estResultEmpty ) $maximum [1] -638.42 $estimate (Intercept) age yearsmarried religiousness2 religiousness3 7.31 -0.16 0.52 -4.12 -3.02 religiousness4 occupation rating logSigma -7.22 0.40 -2.33 2.11 $gradient (Intercept) age yearsmarried religiousness2 religiousness3 0 0 0 0 0 religiousness4 occupation rating logSigma 0 0 0 0 $hessian (Intercept) age yearsmarried religiousness2 religiousness3 (Intercept) -4.5 -145.6 -38.8 -1.4 -1.2 age -145.6 -5090.7 -1428.2 -44.7 -39.3 yearsmarried -38.8 -1428.2 -467.6 -10.8 -10.7 religiousness2 -1.4 -44.7 -10.8 -1.4 0.0 religiousness3 -1.2 -39.3 -10.7 0.0 -1.2 religiousness4 -1.3 -45.1 -13.4 0.0 0.0 occupation -18.9 -630.3 -166.4 -6.3 -5.1 rating -16.4 -528.3 -136.6 -5.1 -4.4 logSigma -32.4 -1032.3 -252.1 -10.4 -7.5 religiousness4 occupation rating logSigma (Intercept) -1.3 -18.9 -16.4 -32.4 age -45.1 -630.3 -528.3 -1032.3 yearsmarried -13.4 -166.4 -136.6 -252.1 religiousness2 0.0 -6.3 -5.1 -10.4 religiousness3 0.0 -5.1 -4.4 -7.5 religiousness4 -1.3 -5.3 -4.9 -11.9 occupation -5.3 -94.8 -70.0 -134.8 rating -4.9 -70.0 -66.5 -129.8 logSigma -11.9 -134.8 -129.8 -473.3 $last.step NULL $fixed (Intercept) age yearsmarried religiousness2 religiousness3 FALSE FALSE FALSE FALSE FALSE religiousness4 occupation rating logSigma FALSE FALSE FALSE FALSE $type [1] "Newton-Raphson maximisation" $gradientObs (Intercept) age yearsmarried religiousness2 religiousness3 [1,] -0.07 -2.60 -0.70 0.00 -0.07 [2,] -0.03 -0.82 -0.12 0.00 0.00 [3,] -0.11 -3.41 -1.60 0.00 0.00 [4,] -0.06 -1.28 -0.04 -0.06 0.00 [5,] -0.02 -0.71 -0.03 -0.02 0.00 [6,] -0.04 -0.98 -0.03 -0.04 0.00 [7,] -0.05 -2.77 -0.73 -0.05 0.00 [8,] -0.08 -2.65 -1.24 0.00 0.00 [9,] -0.02 -0.34 -0.02 0.00 0.00 [10,] -0.13 -4.74 -1.92 -0.13 0.00 [11,] -0.03 -0.82 -0.12 0.00 0.00 [12,] -0.04 -0.94 -0.06 -0.04 0.00 [13,] -0.03 -0.76 -0.11 0.00 0.00 [14,] -0.09 -3.40 -1.38 0.00 0.00 [15,] -0.09 -3.45 -1.40 -0.09 0.00 [16,] -0.05 -1.03 -0.04 0.00 -0.05 [17,] -0.03 -0.65 -0.04 -0.03 0.00 [18,] -0.04 -1.10 -0.41 -0.04 0.00 [19,] -0.03 -0.65 -0.04 -0.03 0.00 [20,] -0.03 -0.65 -0.04 -0.03 0.00 [21,] -0.05 -1.24 -0.46 0.00 0.00 [22,] -0.04 -1.36 -0.42 0.00 -0.04 [23,] -0.04 -1.41 -0.15 -0.04 0.00 [24,] -0.03 -0.65 -0.04 -0.03 0.00 [25,] -0.02 -0.50 -0.13 0.00 0.00 [26,] -0.04 -1.03 -0.15 0.00 -0.04 [27,] -0.05 -1.43 -0.21 0.00 -0.05 [28,] -0.07 -2.79 -1.00 0.00 0.00 [29,] -0.04 -0.78 -0.05 0.00 -0.04 [30,] -0.02 -0.59 -0.01 0.00 0.00 [31,] -0.07 -2.33 -0.29 0.00 0.00 [32,] -0.04 -0.83 -0.06 0.00 0.00 [33,] -0.07 -2.75 -0.98 0.00 -0.07 [34,] -0.02 -0.48 -0.09 0.00 0.00 [35,] -0.05 -1.07 -0.07 0.00 0.00 [36,] -0.03 -0.56 -0.02 0.00 -0.03 [37,] -0.11 -2.86 -0.42 -0.11 0.00 [38,] -0.04 -0.95 -0.17 0.00 -0.04 [39,] -0.03 -0.72 -0.19 0.00 0.00 [40,] -0.07 -2.92 -1.04 -0.07 0.00 [41,] -0.01 -0.33 -0.02 0.00 0.00 [42,] -0.07 -3.05 -1.09 -0.07 0.00 [43,] -0.05 -1.71 -0.37 -0.05 0.00 [44,] -0.05 -1.31 -0.48 0.00 0.00 [45,] -0.06 -1.41 -0.26 0.00 0.00 [46,] -0.10 -3.85 -1.56 0.00 0.00 [47,] -0.03 -1.08 -0.44 0.00 0.00 [48,] -0.02 -0.57 -0.02 0.00 0.00 [49,] -0.04 -1.38 -0.43 0.00 0.00 [50,] -0.07 -2.48 -0.67 0.00 -0.07 [51,] -0.03 -0.61 -0.02 -0.03 0.00 [52,] -0.03 -0.81 -0.12 -0.03 0.00 [53,] -0.03 -1.09 -0.24 0.00 0.00 [54,] -0.05 -1.96 -0.70 -0.05 0.00 [55,] -0.04 -1.42 -0.38 0.00 0.00 [56,] -0.06 -2.68 -0.85 0.00 -0.06 [57,] -0.04 -1.08 -0.06 -0.04 0.00 [58,] -0.04 -1.03 -0.15 0.00 -0.04 [59,] -0.03 -0.58 0.00 -0.03 0.00 [60,] -0.05 -2.56 -0.82 0.00 0.00 [61,] -0.10 -3.19 -1.49 0.00 0.00 [62,] -0.02 -0.67 -0.17 0.00 0.00 [63,] -0.04 -0.78 -0.05 0.00 -0.04 [64,] -0.04 -1.09 -0.16 0.00 -0.04 [65,] -0.04 -0.78 -0.05 0.00 -0.04 [66,] -0.10 -5.45 -1.43 -0.10 0.00 [67,] -0.04 -0.73 -0.06 0.00 -0.04 [68,] -0.02 -1.34 -0.35 0.00 0.00 [69,] -0.04 -0.78 -0.03 -0.04 0.00 [70,] -0.02 -1.01 -0.10 0.00 0.00 [71,] -0.01 -0.27 -0.02 0.00 0.00 [72,] -0.07 -1.56 -0.03 0.00 0.00 [73,] -0.04 -1.36 -0.64 0.00 0.00 [74,] -0.08 -2.13 -0.12 0.00 -0.08 [75,] -0.03 -0.60 -0.04 0.00 -0.03 [76,] -0.07 -2.66 -1.08 0.00 -0.07 [77,] -0.05 -1.68 -0.79 0.00 0.00 [78,] -0.07 -2.73 -0.74 -0.07 0.00 [79,] -0.04 -1.33 -0.36 0.00 0.00 [80,] -0.06 -1.49 -0.02 0.00 0.00 [81,] -0.13 -7.15 -1.88 0.00 -0.13 [82,] -0.09 -3.44 -0.93 0.00 0.00 [83,] -0.07 -2.45 -0.99 0.00 -0.07 [84,] -0.04 -1.48 -0.60 0.00 0.00 [85,] -0.06 -2.17 -0.59 -0.06 0.00 [86,] -0.01 -0.30 0.00 0.00 0.00 [87,] -0.06 -2.04 -0.83 0.00 0.00 [88,] -0.03 -0.76 -0.14 0.00 0.00 [89,] -0.04 -0.98 -0.26 0.00 0.00 [90,] -0.03 -1.86 -0.49 0.00 0.00 [91,] -0.12 -3.83 -1.79 0.00 -0.12 [92,] -0.04 -0.94 -0.06 -0.04 0.00 [93,] -0.02 -0.50 -0.11 0.00 0.00 [94,] -0.04 -1.48 -0.60 0.00 0.00 [95,] -0.06 -1.74 -0.45 0.00 -0.06 [96,] -0.04 -1.48 -0.60 0.00 0.00 [97,] -0.08 -3.06 -1.24 0.00 0.00 [98,] -0.01 -0.24 -0.01 0.00 0.00 [99,] -0.03 -0.81 -0.21 0.00 0.00 [100,] -0.03 -0.92 -0.24 -0.03 0.00 [101,] -0.06 -2.66 -0.95 0.00 0.00 [102,] -0.08 -2.17 -0.56 -0.08 0.00 [103,] -0.04 -0.78 -0.05 0.00 -0.04 [104,] -0.02 -0.54 0.00 -0.02 0.00 [105,] -0.01 -0.39 -0.02 0.00 0.00 [106,] -0.02 -0.77 -0.04 -0.02 0.00 [107,] -0.03 -0.75 -0.04 -0.03 0.00 [108,] -0.05 -1.37 -0.51 0.00 0.00 [109,] -0.04 -1.49 -0.53 0.00 0.00 [110,] -0.03 -0.75 -0.04 -0.03 0.00 [111,] -0.06 -1.75 -0.26 -0.06 0.00 [112,] -0.09 -2.89 -0.90 0.00 -0.09 [113,] -0.09 -2.98 -1.40 0.00 -0.09 [114,] -0.03 -0.65 -0.02 -0.03 0.00 [115,] -0.06 -2.34 -0.95 -0.06 0.00 [116,] -0.02 -0.50 -0.07 0.00 0.00 [117,] -0.08 -2.06 -0.30 0.00 0.00 [118,] -0.06 -1.51 -0.56 -0.06 0.00 [119,] -0.06 -2.96 -0.85 -0.06 0.00 [120,] -0.07 -1.99 -0.30 0.00 -0.07 [121,] -0.06 -2.33 -0.25 0.00 0.00 [122,] -0.03 -0.76 -0.11 0.00 0.00 [123,] -0.03 -1.98 -0.52 0.00 0.00 [124,] -0.09 -2.43 -0.63 0.00 0.00 [125,] -0.04 -1.59 -0.30 0.00 0.00 [126,] -0.05 -1.16 -0.04 -0.05 0.00 [127,] -0.06 -1.79 -0.22 -0.06 0.00 [128,] -0.07 -2.71 -1.10 0.00 0.00 [129,] -0.05 -1.16 -0.04 -0.05 0.00 [130,] -0.07 -2.79 -1.00 0.00 0.00 [131,] -0.08 -2.80 -1.14 0.00 0.00 [132,] -0.05 -1.37 -0.36 0.00 0.00 [133,] -0.03 -0.90 -0.11 -0.03 0.00 [134,] -0.03 -0.92 -0.14 -0.03 0.00 [135,] -0.03 -0.87 -0.13 -0.03 0.00 [136,] -0.06 -1.86 -0.58 0.00 -0.06 [137,] -0.04 -1.01 -0.06 0.00 0.00 [138,] -0.09 -5.03 -1.32 -0.09 0.00 [139,] -0.03 -0.57 -0.04 0.00 0.00 [140,] -0.07 -3.02 -1.08 0.00 -0.07 [141,] -0.05 -3.06 -0.80 0.00 0.00 [142,] -0.02 -1.34 -0.35 0.00 0.00 [143,] -0.01 -0.30 0.00 0.00 0.00 [144,] -0.02 -0.68 -0.21 0.00 0.00 [145,] -0.08 -3.30 -1.18 0.00 -0.08 [146,] -0.03 -0.75 -0.04 -0.03 0.00 [147,] -0.06 -1.86 -0.01 -0.06 0.00 [148,] -0.05 -1.43 -0.21 0.00 -0.05 [149,] -0.06 -1.51 -0.56 -0.06 0.00 [150,] -0.04 -1.15 -0.25 0.00 0.00 [151,] -0.05 -1.94 -0.79 0.00 0.00 [152,] -0.01 -0.42 -0.02 0.00 0.00 [153,] -0.06 -2.05 -0.26 0.00 -0.06 [154,] -0.02 -0.68 -0.13 0.00 0.00 [155,] -0.03 -0.62 -0.01 0.00 -0.03 [156,] -0.02 -0.50 -0.13 0.00 0.00 [157,] -0.03 -0.79 -0.02 0.00 -0.03 [158,] -0.03 -0.87 -0.13 -0.03 0.00 [159,] -0.03 -0.85 -0.26 0.00 0.00 [160,] -0.10 -3.19 -1.49 0.00 0.00 [161,] -0.03 -0.69 -0.02 0.00 -0.03 [162,] -0.03 -0.76 -0.20 0.00 0.00 [163,] -0.02 -0.55 -0.01 0.00 0.00 [164,] -0.05 -1.94 -0.79 0.00 0.00 [165,] -0.08 -3.05 -1.24 -0.08 0.00 [166,] -0.08 -1.83 -0.33 0.00 0.00 [167,] -0.07 -2.58 -1.05 0.00 0.00 [168,] -0.03 -0.61 -0.04 -0.03 0.00 [169,] -0.07 -3.75 -1.08 0.00 0.00 [170,] 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0.15 3.99 0.22 0.00 0.15 [396,] 0.18 4.87 0.72 0.00 0.18 [397,] 0.15 4.87 1.52 0.00 0.15 [398,] 0.21 4.55 0.03 0.00 0.00 [399,] 0.17 3.84 0.26 0.17 0.00 [400,] 0.19 6.94 2.81 0.00 0.00 [401,] 0.22 4.76 0.32 0.22 0.00 [402,] 0.06 2.13 0.86 0.06 0.00 [403,] 0.06 1.76 0.83 0.00 0.00 [404,] 0.03 1.17 0.48 0.00 0.00 [405,] 0.16 6.57 2.35 0.00 0.16 [406,] 0.17 6.46 1.75 0.17 0.00 [407,] 0.14 4.40 2.06 0.00 0.14 [408,] 0.11 2.87 0.43 0.00 0.00 [409,] 0.13 4.79 1.29 0.13 0.00 [410,] 0.22 5.82 0.86 0.00 0.22 [411,] 0.14 5.90 2.11 0.00 0.00 [412,] 0.18 4.90 0.73 0.00 0.18 [413,] 0.27 7.40 0.41 0.00 0.27 [414,] 0.22 5.92 1.53 0.00 0.00 [415,] 0.14 5.70 2.04 0.00 0.00 [416,] 0.15 4.07 1.51 0.00 0.00 [417,] 0.06 1.62 0.09 0.06 0.00 [418,] 0.16 5.15 0.64 0.00 0.00 [419,] 0.06 1.60 0.42 0.00 0.06 [420,] 0.17 5.56 1.74 0.00 0.00 [421,] 0.06 1.58 0.23 0.06 0.00 [422,] 0.18 5.71 1.79 0.00 0.00 [423,] 0.18 5.78 1.26 0.18 0.00 [424,] 0.13 4.85 1.97 0.13 0.00 [425,] 0.08 2.98 0.81 0.00 0.00 [426,] 0.27 8.68 2.71 0.27 0.00 [427,] 0.17 8.69 2.51 0.17 0.00 [428,] 0.08 3.27 1.17 0.00 0.00 [429,] 0.04 2.32 0.67 0.04 0.00 [430,] 0.08 2.80 1.13 0.00 0.08 [431,] 0.25 5.60 1.02 0.00 0.25 [432,] 0.11 3.05 0.79 0.00 0.00 [433,] 0.13 3.44 0.51 0.00 0.13 [434,] 0.31 14.49 4.63 0.00 0.00 [435,] 0.19 7.94 2.83 0.00 0.00 [436,] 0.19 5.21 0.77 0.00 0.19 [437,] 0.27 8.71 1.91 0.00 0.00 [438,] 0.14 4.62 0.06 0.00 0.14 [439,] 0.21 7.79 3.16 0.21 0.00 [440,] 0.18 3.95 0.72 0.18 0.00 [441,] 0.15 4.04 0.60 0.15 0.00 [442,] 0.07 1.91 0.28 0.00 0.07 [443,] 0.13 3.57 1.32 0.00 0.00 [444,] 0.07 2.25 0.49 0.00 0.07 [445,] 0.09 2.87 0.63 0.09 0.00 [446,] 0.03 0.63 0.04 0.00 0.00 [447,] 0.16 3.59 0.65 0.00 0.16 [448,] 0.19 7.92 2.83 0.00 0.00 [449,] 0.05 2.90 0.76 0.00 0.00 [450,] 0.12 4.00 0.50 0.00 0.12 [451,] 0.05 1.48 0.22 0.00 0.00 [452,] 0.26 8.18 1.79 0.00 0.00 [453,] 0.06 3.24 0.85 0.00 0.00 [454,] 0.13 5.30 1.89 0.00 0.00 [455,] 0.08 2.98 0.81 0.00 0.00 [456,] -0.03 -1.45 -0.52 0.00 -0.03 [457,] 0.07 3.87 1.12 0.00 0.07 [458,] 0.05 1.36 0.35 0.00 0.05 [459,] 0.20 6.32 1.38 0.20 0.00 [460,] 0.19 4.29 0.78 0.00 0.00 [461,] 0.09 2.53 0.65 0.00 0.09 [462,] 0.18 6.81 2.76 0.00 0.00 [463,] 0.10 3.14 1.47 0.00 0.10 [464,] 0.21 5.63 1.46 0.21 0.00 [465,] 0.05 1.53 0.33 0.00 0.05 [466,] 0.16 5.07 0.24 0.16 0.00 [467,] 0.22 9.37 3.35 0.00 0.00 [468,] 0.15 4.71 1.47 0.00 0.15 [469,] 0.12 4.47 0.48 0.00 0.00 [470,] 0.08 2.03 0.30 0.08 0.00 [471,] 0.18 7.47 2.67 0.00 0.18 [472,] 0.17 6.46 1.75 0.17 0.00 [473,] 0.16 4.45 1.15 0.00 0.00 [474,] 0.12 3.14 0.81 0.00 0.00 [475,] 0.11 3.54 1.10 0.11 0.00 [476,] 0.26 4.60 0.20 0.26 0.00 [477,] 0.18 5.83 2.73 0.00 0.18 [478,] 0.11 2.34 0.74 0.00 0.00 [479,] 0.17 5.51 1.21 0.00 0.00 [480,] 0.12 3.33 0.49 0.12 0.00 [481,] 0.08 2.55 1.19 0.00 0.08 [482,] 0.18 7.46 2.66 0.18 0.00 [483,] 0.13 5.58 1.99 0.00 0.13 [484,] 0.17 5.58 1.74 0.17 0.00 [485,] 0.10 3.30 1.55 0.00 0.10 [486,] 0.27 12.89 4.11 0.00 0.00 [487,] 0.04 1.48 0.53 0.04 0.00 [488,] 0.15 5.70 2.31 0.00 0.15 [489,] 0.15 3.93 1.45 0.15 0.00 [490,] 0.06 2.35 0.95 0.06 0.00 [491,] 0.07 2.22 1.04 0.00 0.00 [492,] 0.11 3.43 1.07 0.00 0.11 [493,] 0.01 0.23 0.09 0.00 0.00 [494,] 0.25 6.78 0.38 0.25 0.00 [495,] 0.03 1.59 0.51 0.03 0.00 [496,] 0.21 7.79 3.16 0.21 0.00 [497,] 0.31 8.24 1.22 0.31 0.00 [498,] 0.18 4.89 1.81 0.00 0.00 [499,] 0.07 1.55 0.28 0.00 0.07 [500,] 0.39 20.17 2.72 0.00 0.00 [501,] 0.11 2.96 0.44 0.00 0.00 [502,] 0.13 4.85 1.97 0.13 0.00 [503,] -0.03 -0.74 -0.11 0.00 0.00 [504,] 0.32 5.59 0.24 0.32 0.00 [505,] 0.17 3.76 0.68 0.00 0.00 [506,] 0.18 5.62 0.70 0.00 0.00 [507,] 0.03 0.70 0.05 0.00 0.03 [508,] 0.09 3.79 1.35 0.09 0.00 [509,] 0.15 4.79 1.05 0.00 0.00 [510,] 0.12 4.44 1.80 0.00 0.12 [511,] 0.00 0.19 0.07 0.00 0.00 [512,] 0.05 1.32 0.19 0.00 0.00 [513,] 0.06 2.33 0.94 0.00 0.00 [514,] 0.11 4.25 1.72 0.00 0.11 [515,] 0.12 2.72 0.19 0.12 0.00 [516,] 0.06 1.93 0.24 0.00 0.06 [517,] 0.22 11.45 3.30 0.00 0.00 [518,] 0.20 9.51 3.03 0.00 0.00 [519,] 0.12 3.76 1.76 0.00 0.00 [520,] 0.05 1.67 0.78 0.00 0.05 [521,] 0.18 4.73 1.23 0.00 0.00 [522,] 0.13 5.54 1.98 0.00 0.13 [523,] 0.09 3.88 1.38 0.09 0.00 [524,] 0.25 6.70 1.74 0.25 0.00 [525,] 0.12 3.90 1.22 0.00 0.00 [526,] 0.08 3.86 1.23 0.00 0.08 [527,] 0.11 2.37 0.16 0.00 0.00 [528,] 0.16 5.23 1.63 0.16 0.00 [529,] 0.12 3.78 1.18 0.12 0.00 [530,] 0.00 -0.04 -0.01 0.00 0.00 [531,] 0.08 2.51 1.17 0.00 0.08 religiousness4 occupation rating logSigma [1,] 0.00 -0.49 -0.28 -0.21 [2,] -0.03 -0.18 -0.12 -0.28 [3,] 0.00 -0.11 -0.43 0.11 [4,] 0.00 -0.35 -0.17 -0.27 [5,] 0.00 -0.11 -0.11 -0.24 [6,] 0.00 -0.04 -0.13 -0.29 [7,] 0.00 -0.19 -0.19 -0.29 [8,] -0.08 -0.08 -0.17 -0.13 [9,] -0.02 -0.06 -0.08 -0.20 [10,] 0.00 -0.90 -0.26 0.40 [11,] -0.03 -0.18 -0.12 -0.28 [12,] 0.00 -0.21 -0.17 -0.29 [13,] -0.03 -0.14 -0.11 -0.27 [14,] 0.00 -0.46 -0.46 -0.05 [15,] 0.00 -0.37 -0.28 -0.04 [16,] 0.00 -0.24 -0.19 -0.29 [17,] 0.00 -0.15 -0.15 -0.27 [18,] 0.00 -0.04 -0.20 -0.29 [19,] 0.00 -0.15 -0.15 -0.27 [20,] 0.00 -0.15 -0.15 -0.27 [21,] -0.05 -0.23 -0.18 -0.29 [22,] 0.00 -0.04 -0.21 -0.29 [23,] 0.00 -0.23 -0.15 -0.29 [24,] 0.00 -0.15 -0.15 -0.27 [25,] -0.02 -0.02 -0.09 -0.22 [26,] 0.00 -0.19 -0.19 -0.29 [27,] 0.00 -0.26 -0.21 -0.28 [28,] -0.07 -0.40 -0.20 -0.23 [29,] 0.00 -0.18 -0.18 -0.29 [30,] -0.02 -0.13 -0.09 -0.24 [31,] 0.00 -0.44 -0.29 -0.20 [32,] -0.04 -0.19 -0.11 -0.29 [33,] 0.00 -0.07 -0.26 -0.24 [34,] -0.02 -0.11 -0.11 -0.24 [35,] 0.00 -0.15 -0.24 -0.29 [36,] 0.00 -0.03 -0.13 -0.26 [37,] 0.00 -0.63 -0.11 0.10 [38,] 0.00 -0.22 -0.22 -0.29 [39,] -0.03 -0.16 -0.13 -0.26 [40,] 0.00 -0.35 -0.28 -0.22 [41,] -0.01 -0.04 -0.06 -0.17 [42,] 0.00 -0.44 -0.29 -0.20 [43,] 0.00 -0.32 -0.21 -0.28 [44,] -0.05 -0.29 -0.19 -0.29 [45,] 0.00 -0.32 -0.32 -0.24 [46,] -0.10 -0.31 -0.10 0.08 [47,] -0.03 -0.03 -0.15 -0.27 [48,] -0.02 -0.10 -0.08 -0.24 [49,] -0.04 -0.26 -0.17 -0.29 [50,] 0.00 -0.40 -0.27 -0.23 [51,] 0.00 -0.14 -0.14 -0.27 [52,] 0.00 -0.12 -0.15 -0.28 [53,] -0.03 -0.20 -0.14 -0.29 [54,] 0.00 -0.14 -0.23 -0.29 [55,] -0.04 -0.23 -0.15 -0.29 [56,] 0.00 -0.34 -0.28 -0.27 [57,] 0.00 -0.24 -0.16 -0.29 [58,] 0.00 -0.19 -0.19 -0.29 [59,] 0.00 -0.13 -0.13 -0.26 [60,] -0.05 -0.22 -0.16 -0.28 [61,] 0.00 -0.50 -0.50 0.03 [62,] -0.02 -0.12 -0.12 -0.26 [63,] 0.00 -0.18 -0.18 -0.29 [64,] 0.00 -0.24 -0.20 -0.29 [65,] 0.00 -0.18 -0.18 -0.29 [66,] 0.00 -0.67 -0.19 -0.01 [67,] 0.00 -0.25 -0.21 -0.29 [68,] -0.02 -0.14 -0.12 -0.25 [69,] 0.00 -0.11 -0.14 -0.29 [70,] -0.02 -0.07 -0.07 -0.25 [71,] -0.01 -0.01 -0.06 -0.17 [72,] 0.00 -0.42 -0.28 -0.21 [73,] -0.04 -0.21 -0.21 -0.29 [74,] 0.00 -0.39 -0.16 -0.16 [75,] 0.00 -0.03 -0.14 -0.27 [76,] 0.00 -0.07 -0.29 -0.20 [77,] -0.05 -0.16 -0.21 -0.28 [78,] 0.00 -0.37 -0.22 -0.19 [79,] -0.04 -0.18 -0.14 -0.29 [80,] 0.00 -0.17 -0.22 -0.28 [81,] 0.00 -0.75 -0.13 0.36 [82,] 0.00 -0.56 -0.37 -0.04 [83,] 0.00 -0.33 -0.33 -0.23 [84,] -0.04 -0.24 -0.20 -0.29 [85,] 0.00 -0.35 -0.23 -0.27 [86,] -0.01 -0.05 -0.07 -0.18 [87,] -0.06 -0.33 -0.22 -0.28 [88,] -0.03 -0.21 -0.14 -0.29 [89,] -0.04 -0.18 -0.15 -0.29 [90,] -0.03 -0.16 -0.13 -0.28 [91,] 0.00 -0.72 -0.36 0.28 [92,] 0.00 -0.21 -0.17 -0.29 [93,] -0.02 -0.02 -0.08 -0.20 [94,] -0.04 -0.24 -0.20 -0.29 [95,] 0.00 -0.32 -0.26 -0.24 [96,] -0.04 -0.24 -0.20 -0.29 [97,] -0.08 -0.25 -0.17 -0.13 [98,] -0.01 -0.01 -0.06 -0.16 [99,] -0.03 -0.06 -0.12 -0.28 [100,] 0.00 -0.07 -0.17 -0.29 [101,] -0.06 -0.32 -0.19 -0.25 [102,] 0.00 -0.08 -0.16 -0.15 [103,] 0.00 -0.18 -0.18 -0.29 [104,] 0.00 -0.10 -0.12 -0.25 [105,] -0.01 -0.07 -0.07 -0.19 [106,] 0.00 -0.14 -0.12 -0.25 [107,] 0.00 -0.17 -0.14 -0.27 [108,] -0.05 -0.05 -0.15 -0.29 [109,] -0.04 -0.21 -0.18 -0.29 [110,] 0.00 -0.17 -0.14 -0.27 [111,] 0.00 -0.39 -0.19 -0.24 [112,] 0.00 -0.45 -0.27 -0.06 [113,] 0.00 -0.47 -0.37 -0.04 [114,] 0.00 -0.18 -0.15 -0.27 [115,] 0.00 -0.06 -0.25 -0.25 [116,] -0.02 -0.09 -0.09 -0.22 [117,] 0.00 -0.38 -0.30 -0.18 [118,] 0.00 -0.06 -0.22 -0.27 [119,] 0.00 -0.28 -0.23 -0.27 [120,] 0.00 -0.44 -0.22 -0.19 [121,] 0.00 -0.32 -0.25 -0.25 [122,] -0.03 -0.14 -0.11 -0.27 [123,] -0.03 -0.21 -0.14 -0.29 [124,] 0.00 -0.45 -0.36 -0.07 [125,] -0.04 -0.26 -0.13 -0.29 [126,] 0.00 -0.21 -0.16 -0.28 [127,] 0.00 -0.28 -0.17 -0.28 [128,] -0.07 -0.44 -0.22 -0.20 [129,] 0.00 -0.21 -0.16 -0.28 [130,] -0.07 -0.40 -0.20 -0.23 [131,] -0.08 -0.08 -0.15 -0.18 [132,] -0.05 -0.25 -0.15 -0.29 [133,] 0.00 -0.14 -0.14 -0.27 [134,] 0.00 -0.21 -0.17 -0.29 [135,] 0.00 -0.16 -0.16 -0.28 [136,] 0.00 -0.06 -0.23 -0.27 [137,] -0.04 -0.04 -0.07 -0.29 [138,] 0.00 -0.44 -0.18 -0.08 [139,] -0.03 -0.13 -0.10 -0.26 [140,] 0.00 -0.22 -0.29 -0.20 [141,] -0.05 -0.11 -0.11 -0.28 [142,] -0.02 -0.14 -0.12 -0.25 [143,] -0.01 -0.05 -0.07 -0.18 [144,] -0.02 -0.02 -0.11 -0.24 [145,] 0.00 -0.39 -0.31 -0.16 [146,] 0.00 -0.17 -0.14 -0.27 [147,] 0.00 -0.29 -0.12 -0.27 [148,] 0.00 -0.26 -0.21 -0.28 [149,] 0.00 -0.06 -0.22 -0.27 [150,] -0.04 -0.04 -0.11 -0.29 [151,] -0.05 -0.26 -0.21 -0.28 [152,] -0.01 -0.08 -0.07 -0.18 [153,] 0.00 -0.32 -0.19 -0.24 [154,] -0.02 -0.09 -0.09 -0.22 [155,] 0.00 -0.09 -0.14 -0.27 [156,] -0.02 -0.02 -0.09 -0.22 [157,] 0.00 -0.15 -0.15 -0.27 [158,] 0.00 -0.16 -0.16 -0.28 [159,] -0.03 -0.11 -0.13 -0.26 [160,] 0.00 -0.50 -0.50 0.03 [161,] 0.00 -0.12 -0.16 -0.28 [162,] -0.03 -0.03 -0.11 -0.27 [163,] -0.02 -0.10 -0.08 -0.23 [164,] -0.05 -0.26 -0.21 -0.28 [165,] 0.00 -0.08 -0.25 -0.13 [166,] 0.00 -0.42 -0.33 -0.12 [167,] -0.07 -0.35 -0.21 -0.22 [168,] 0.00 -0.11 -0.14 -0.27 [169,] -0.07 -0.43 -0.14 -0.20 [170,] -0.02 -0.08 -0.08 -0.21 [171,] 0.00 -0.07 -0.12 -0.25 [172,] 0.00 -0.23 -0.19 -0.29 [173,] 0.00 -0.13 -0.13 -0.29 [174,] 0.00 -0.51 -0.21 0.06 [175,] 0.00 -0.25 -0.15 -0.29 [176,] -0.08 -0.08 -0.15 -0.18 [177,] 0.00 -0.04 -0.22 -0.29 [178,] 0.00 -0.21 -0.21 -0.28 [179,] -0.07 -0.35 -0.21 -0.22 [180,] 0.00 -0.28 -0.28 -0.05 [181,] 0.00 -0.18 -0.18 -0.29 [182,] 0.00 -0.38 -0.13 -0.25 [183,] 0.00 -0.12 -0.12 -0.25 [184,] 0.00 -0.12 -0.15 -0.28 [185,] -0.02 -0.11 -0.09 -0.22 [186,] -0.05 -0.23 -0.14 -0.29 [187,] 0.00 -0.42 -0.33 -0.12 [188,] 0.00 -0.52 -0.26 -0.10 [189,] -0.04 -0.21 -0.18 -0.29 [190,] -0.03 -0.15 -0.15 -0.27 [191,] -0.03 -0.13 -0.10 -0.26 [192,] 0.00 -0.06 -0.23 -0.27 [193,] 0.00 -0.21 -0.35 -0.21 [194,] -0.04 -0.20 -0.16 -0.29 [195,] 0.00 -0.06 -0.12 -0.28 [196,] -0.04 -0.19 -0.19 -0.29 [197,] 0.00 -0.10 -0.12 -0.25 [198,] -0.02 -0.11 -0.11 -0.24 [199,] -0.04 -0.21 -0.18 -0.29 [200,] -0.03 -0.18 -0.12 -0.28 [201,] -0.02 -0.10 -0.08 -0.21 [202,] -0.04 -0.20 -0.12 -0.29 [203,] 0.00 -0.26 -0.26 -0.28 [204,] -0.05 -0.20 -0.20 -0.29 [205,] 0.00 -0.25 -0.20 -0.29 [206,] 0.00 -0.07 -0.26 -0.24 [207,] 0.00 -0.50 -0.50 0.03 [208,] 0.00 -0.19 -0.19 -0.29 [209,] -0.05 -0.16 -0.21 -0.28 [210,] 0.00 -0.08 -0.16 -0.29 [211,] 0.00 -0.33 -0.20 -0.23 [212,] -0.03 -0.09 -0.15 -0.27 [213,] 0.00 -0.33 -0.26 -0.24 [214,] -0.05 -0.10 -0.15 -0.29 [215,] 0.00 -0.32 -0.22 -0.28 [216,] -0.02 -0.11 -0.11 -0.24 [217,] -0.03 -0.03 -0.11 -0.27 [218,] 0.00 -0.34 -0.29 -0.27 [219,] 0.00 -0.05 -0.19 -0.29 [220,] 0.00 -0.02 -0.11 -0.24 [221,] 0.00 -0.44 -0.29 -0.20 [222,] -0.03 -0.05 -0.11 -0.26 [223,] 0.00 -0.03 -0.16 -0.28 [224,] 0.00 -0.03 -0.17 -0.29 [225,] -0.07 -0.35 -0.21 -0.22 [226,] 0.00 -0.52 -0.34 -0.10 [227,] 0.00 -0.15 -0.15 -0.27 [228,] 0.00 -0.05 -0.16 -0.28 [229,] 0.00 -0.21 -0.21 -0.29 [230,] 0.00 -0.13 -0.13 0.39 [231,] 0.00 -0.23 -0.23 -0.29 [232,] -0.03 -0.12 -0.16 -0.28 [233,] 0.00 -0.14 -0.18 -0.29 [234,] -0.04 -0.24 -0.20 -0.29 [235,] 0.00 -0.16 -0.16 -0.28 [236,] 0.00 -0.34 -0.17 -0.27 [237,] 0.00 -0.51 -0.25 -0.12 [238,] -0.03 -0.14 -0.11 -0.27 [239,] 0.00 -0.59 -0.12 0.25 [240,] 0.00 -0.29 -0.29 -0.27 [241,] -0.04 -0.27 -0.18 -0.29 [242,] 0.00 -0.24 -0.24 -0.29 [243,] -0.06 -0.33 -0.22 -0.28 [244,] -0.05 -0.05 -0.19 -0.29 [245,] -0.03 -0.16 -0.13 -0.28 [246,] 0.00 -0.08 -0.25 -0.11 [247,] 0.00 -0.05 -0.21 -0.28 [248,] 0.00 -0.30 -0.15 -0.18 [249,] 0.00 -0.09 -0.16 -0.28 [250,] 0.00 -0.35 -0.17 -0.09 [251,] 0.00 -0.09 -0.18 -0.06 [252,] 0.00 -0.38 -0.30 -0.18 [253,] 0.00 -0.20 -0.20 -0.29 [254,] 0.00 -0.15 -0.15 -0.27 [255,] -0.04 -0.19 -0.11 -0.29 [256,] -0.02 -0.02 -0.11 -0.24 [257,] 0.00 -0.31 -0.19 -0.25 [258,] -0.02 -0.08 -0.08 -0.19 [259,] 0.00 -0.16 -0.16 -0.28 [260,] -0.07 -0.35 -0.21 -0.22 [261,] 0.00 -0.36 -0.18 -0.06 [262,] 0.00 -0.14 -0.14 -0.27 [263,] 0.00 -0.26 -0.17 -0.29 [264,] 0.00 -0.13 -0.17 -0.28 [265,] -0.03 -0.09 -0.11 -0.27 [266,] 0.00 -0.22 -0.18 -0.29 [267,] -0.03 -0.11 -0.13 -0.26 [268,] -0.07 -0.35 -0.21 -0.22 [269,] 0.00 -0.13 -0.16 -0.28 [270,] 0.00 -0.19 -0.13 -0.25 [271,] -0.02 -0.02 -0.11 -0.24 [272,] 0.00 -0.29 -0.29 -0.20 [273,] -0.01 -0.06 -0.06 -0.16 [274,] 0.00 -0.02 -0.11 -0.24 [275,] 0.00 -0.44 -0.25 -0.25 [276,] 0.00 -0.03 -0.13 -0.28 [277,] -0.03 -0.06 -0.12 -0.28 [278,] 0.00 -0.02 -0.12 -0.25 [279,] 0.00 -0.15 -0.15 -0.27 [280,] -0.03 -0.18 -0.12 -0.28 [281,] -0.03 -0.03 -0.17 -0.28 [282,] 0.00 -0.07 -0.11 -0.24 [283,] 0.00 -0.33 -0.27 -0.28 [284,] 0.00 -0.54 -0.36 -0.07 [285,] 0.00 -0.29 -0.24 -0.29 [286,] -0.03 -0.13 -0.08 -0.26 [287,] 0.00 -0.12 -0.12 -0.28 [288,] -0.03 -0.03 -0.13 -0.28 [289,] -0.04 -0.21 -0.17 -0.29 [290,] 0.00 -0.14 -0.18 -0.29 [291,] -0.09 -0.34 -0.17 -0.10 [292,] -0.01 -0.07 -0.07 -0.18 [293,] -0.03 -0.03 -0.15 -0.27 [294,] 0.00 -0.06 -0.30 -0.26 [295,] 0.00 -0.04 -0.20 -0.29 [296,] 0.00 -0.26 -0.22 -0.29 [297,] 0.00 -0.08 -0.25 -0.13 [298,] 0.00 -0.14 -0.14 -0.29 [299,] 0.00 -0.02 -0.10 -0.23 [300,] -0.03 -0.05 -0.11 -0.26 [301,] -0.01 -0.08 -0.05 -0.18 [302,] -0.02 -0.09 -0.08 -0.20 [303,] 0.00 -0.52 -0.26 -0.09 [304,] -0.04 -0.04 -0.12 -0.29 [305,] 0.00 -0.13 -0.13 -0.26 [306,] 0.00 -0.16 -0.16 -0.28 [307,] -0.01 -0.01 -0.07 -0.18 [308,] -0.01 -0.01 -0.07 -0.18 [309,] 0.00 -0.23 -0.23 -0.29 [310,] 0.00 -0.58 -0.35 0.22 [311,] 0.00 -0.14 -0.14 -0.27 [312,] -0.03 -0.16 -0.10 -0.26 [313,] 0.00 -0.18 -0.15 -0.27 [314,] 0.00 -0.33 -0.28 -0.28 [315,] 0.00 -0.04 -0.21 -0.29 [316,] -0.02 -0.11 -0.11 -0.24 [317,] 0.00 -0.24 -0.20 -0.29 [318,] -0.02 -0.02 -0.08 -0.20 [319,] -0.02 -0.02 -0.09 -0.22 [320,] 0.00 -0.08 -0.33 -0.13 [321,] 0.00 -0.05 -0.19 -0.29 [322,] -0.06 -0.19 -0.12 -0.25 [323,] 0.00 -0.18 -0.18 -0.29 [324,] -0.03 -0.03 -0.08 -0.26 [325,] -0.02 -0.08 -0.08 -0.21 [326,] -0.03 -0.13 -0.10 -0.26 [327,] 0.00 -0.03 -0.17 -0.28 [328,] 0.00 -0.36 -0.29 -0.20 [329,] 0.00 -0.03 -0.13 -0.26 [330,] 0.00 -0.15 -0.15 -0.27 [331,] 0.00 -0.29 -0.29 -0.27 [332,] -0.03 -0.13 -0.13 -0.26 [333,] -0.03 -0.18 -0.15 -0.28 [334,] -0.04 -0.27 -0.18 -0.29 [335,] 0.00 -0.08 -0.16 -0.15 [336,] -0.02 -0.06 -0.08 -0.20 [337,] 0.00 -0.31 -0.25 -0.25 [338,] 0.00 -0.20 -0.16 -0.29 [339,] 0.00 -0.15 -0.15 -0.27 [340,] 0.00 -0.49 -0.33 -0.13 [341,] -0.04 -0.18 -0.18 -0.29 [342,] 0.00 -0.49 -0.20 0.02 [343,] 0.00 -0.76 -0.25 0.38 [344,] 0.00 -0.07 -0.15 -0.19 [345,] 0.00 -0.34 -0.29 -0.27 [346,] 0.00 -0.20 -0.24 -0.29 [347,] -0.03 -0.03 -0.17 -0.28 [348,] 0.00 -0.29 -0.17 -0.27 [349,] -0.01 -0.01 -0.07 -0.18 [350,] 0.00 -0.37 -0.16 -0.28 [351,] -0.07 -0.22 -0.07 -0.19 [352,] -0.02 -0.09 -0.09 -0.22 [353,] 0.00 -0.28 -0.23 -0.27 [354,] -0.03 -0.19 -0.16 -0.28 [355,] 0.00 -0.28 -0.23 -0.27 [356,] -0.02 -0.12 -0.12 -0.26 [357,] 0.00 -0.19 -0.19 -0.25 [358,] 0.00 -0.20 -0.17 -0.29 [359,] -0.05 -0.19 -0.14 -0.29 [360,] -0.03 -0.20 -0.14 -0.29 [361,] 0.00 -0.03 -0.14 -0.27 [362,] 0.00 -0.06 -0.17 -0.27 [363,] -0.02 -0.14 -0.12 -0.25 [364,] -0.07 -0.27 -0.20 -0.23 [365,] 0.00 -0.24 -0.24 -0.29 [366,] -0.04 -0.15 -0.19 -0.29 [367,] -0.02 -0.11 -0.11 -0.24 [368,] 0.00 -0.04 -0.14 -0.29 [369,] 0.00 -0.72 -0.14 0.67 [370,] 0.00 -0.28 -0.23 -0.27 [371,] -0.02 -0.09 -0.09 -0.22 [372,] 0.00 -0.13 -0.22 -0.29 [373,] -0.03 -0.03 -0.11 -0.27 [374,] -0.04 -0.20 -0.16 -0.29 [375,] 0.00 -0.23 -0.31 -0.18 [376,] 0.00 -0.18 -0.18 -0.29 [377,] -0.03 -0.11 -0.11 -0.26 [378,] 0.00 -0.08 -0.16 -0.15 [379,] 0.00 -0.05 -0.21 -0.28 [380,] 0.00 -0.06 -0.23 -0.27 [381,] -0.05 -0.23 -0.19 -0.29 [382,] -0.05 -0.14 -0.14 -0.29 [383,] 0.00 -0.59 -0.20 0.02 [384,] 0.00 -0.28 -0.28 -0.05 [385,] 0.00 -0.10 -0.17 -0.29 [386,] 0.00 -0.05 -0.21 -0.28 [387,] 0.00 -0.11 -0.14 -0.27 [388,] -0.02 -0.02 -0.09 -0.24 [389,] -0.02 -0.11 -0.11 -0.24 [390,] 0.00 -0.11 -0.14 -0.27 [391,] -0.04 -0.21 -0.17 -0.29 [392,] 0.00 -0.59 -0.20 0.01 [393,] 0.00 -0.43 -0.34 -0.10 [394,] 0.00 -0.04 -0.14 -0.29 [395,] 0.00 0.59 0.59 0.48 [396,] 0.00 0.18 0.90 1.21 [397,] 0.00 0.76 0.30 0.58 [398,] 0.21 1.03 1.03 1.91 [399,] 0.00 0.17 0.87 1.07 [400,] 0.19 0.94 0.38 1.39 [401,] 0.00 0.65 0.87 2.19 [402,] 0.00 0.35 0.23 -0.77 [403,] 0.06 0.17 0.11 -0.79 [404,] 0.03 0.13 0.06 -0.93 [405,] 0.00 0.16 0.63 0.66 [406,] 0.00 1.05 0.35 1.07 [407,] 0.00 0.14 0.27 0.28 [408,] 0.00 0.64 0.53 -0.23 [409,] 0.00 0.91 0.39 0.14 [410,] 0.00 1.08 1.08 2.16 [411,] 0.14 0.70 0.70 0.34 [412,] 0.00 0.91 0.73 1.24 [413,] 0.00 1.37 1.10 4.11 [414,] 0.22 1.32 0.44 2.27 [415,] 0.14 0.68 0.54 0.25 [416,] 0.15 1.06 0.45 0.55 [417,] 0.00 0.30 0.12 -0.75 [418,] 0.16 0.97 0.64 0.76 [419,] 0.00 0.06 0.18 -0.76 [420,] 0.17 0.17 0.70 1.05 [421,] 0.00 0.41 0.12 -0.77 [422,] 0.18 0.18 0.89 1.17 [423,] 0.00 1.08 0.72 1.22 [424,] 0.00 0.79 0.52 0.17 [425,] 0.00 0.40 0.24 -0.56 [426,] 0.00 1.36 1.36 4.00 [427,] 0.00 1.00 0.67 0.90 [428,] 0.00 0.08 0.23 -0.59 [429,] 0.00 0.27 0.13 -0.86 [430,] 0.00 0.45 0.38 -0.61 [431,] 0.00 0.76 1.02 3.41 [432,] 0.00 0.68 0.23 -0.13 [433,] 0.00 0.64 0.64 0.10 [434,] 0.31 1.85 1.54 5.47 [435,] 0.19 0.19 0.19 1.43 [436,] 0.00 0.58 0.77 1.54 [437,] 0.27 1.09 1.36 4.04 [438,] 0.00 0.43 0.58 0.42 [439,] 0.00 1.05 0.84 2.01 [440,] 0.00 1.08 0.72 1.19 [441,] 0.00 0.60 0.75 0.52 [442,] 0.00 0.21 0.21 -0.66 [443,] 0.13 0.13 0.53 0.19 [444,] 0.00 0.49 0.28 -0.66 [445,] 0.00 0.36 0.09 -0.45 [446,] 0.00 0.09 0.06 -0.94 [447,] 0.00 0.98 0.65 0.82 [448,] 0.19 1.13 0.75 1.42 [449,] 0.00 0.25 0.20 -0.82 [450,] 0.00 0.62 0.25 0.06 [451,] 0.00 0.22 0.22 -0.80 [452,] 0.26 0.26 1.02 3.45 [453,] 0.00 0.23 0.23 -0.78 [454,] 0.13 0.63 0.25 0.08 [455,] 0.00 0.40 0.24 -0.56 [456,] 0.00 -0.21 -0.03 -0.92 [457,] 0.00 0.30 0.30 -0.62 [458,] 0.00 0.25 0.15 -0.83 [459,] 0.00 0.79 0.39 1.65 [460,] 0.19 0.39 0.97 1.58 [461,] 0.00 0.56 0.37 -0.41 [462,] 0.00 0.92 0.92 1.31 [463,] 0.00 0.10 0.29 -0.35 [464,] 0.00 1.04 1.04 1.96 [465,] 0.00 0.24 0.14 -0.84 [466,] 0.00 0.32 0.63 0.70 [467,] 0.22 0.22 0.45 2.39 [468,] 0.00 0.74 0.59 0.47 [469,] 0.00 0.73 0.36 -0.01 [470,] 0.00 0.38 0.23 -0.62 [471,] 0.00 0.71 0.53 1.15 [472,] 0.00 1.05 0.35 1.07 [473,] 0.00 0.49 0.49 0.85 [474,] 0.12 0.12 0.23 -0.08 [475,] 0.00 0.44 0.44 -0.17 [476,] 0.00 0.26 0.79 3.69 [477,] 0.00 0.91 0.73 1.26 [478,] 0.11 0.42 0.32 -0.23 [479,] 0.17 1.03 0.86 1.02 [480,] 0.00 0.74 0.25 0.03 [481,] 0.00 0.40 0.08 -0.57 [482,] 0.00 0.18 0.36 1.15 [483,] 0.00 0.66 0.53 0.20 [484,] 0.00 0.70 0.35 1.07 [485,] 0.00 0.10 0.10 -0.28 [486,] 0.27 1.65 1.10 4.11 [487,] 0.00 0.21 0.11 -0.92 [488,] 0.00 0.92 0.46 0.62 [489,] 0.00 0.87 0.58 0.44 [490,] 0.00 0.32 0.25 -0.73 [491,] 0.00 0.35 0.14 -0.67 [492,] 0.00 0.64 0.32 -0.22 [493,] 0.01 0.03 0.01 -1.00 [494,] 0.00 1.26 1.26 3.28 [495,] 0.00 0.17 0.07 -0.92 [496,] 0.00 1.05 0.84 2.01 [497,] 0.00 1.53 1.53 5.34 [498,] 0.18 0.18 0.91 1.23 [499,] 0.00 0.07 0.21 -0.66 [500,] 0.39 1.94 1.94 9.23 [501,] 0.00 0.33 0.55 -0.18 [502,] 0.00 0.79 0.52 0.17 [503,] 0.00 -0.08 -0.03 -0.95 [504,] 0.00 0.96 1.60 5.93 [505,] 0.00 0.51 0.85 0.99 [506,] 0.18 1.05 0.70 1.10 [507,] 0.00 0.16 0.06 -0.93 [508,] 0.00 0.45 0.36 -0.45 [509,] 0.15 0.60 0.60 0.52 [510,] 0.00 0.72 0.24 -0.02 [511,] 0.00 0.03 0.01 -1.00 [512,] 0.00 0.24 0.19 -0.84 [513,] 0.06 0.44 0.19 -0.73 [514,] 0.00 0.69 0.46 -0.10 [515,] 0.00 0.37 0.37 0.04 [516,] 0.00 0.36 0.12 -0.75 [517,] 0.00 1.10 1.10 2.30 [518,] 0.00 1.21 1.01 1.78 [519,] 0.12 0.47 0.47 -0.06 [520,] 0.00 0.16 0.10 -0.82 [521,] 0.18 0.18 0.35 1.09 [522,] 0.00 0.79 0.26 0.18 [523,] 0.00 0.28 0.18 -0.42 [524,] 0.00 1.24 0.99 3.18 [525,] 0.12 0.49 0.37 0.01 [526,] 0.00 0.33 0.16 -0.54 [527,] 0.00 0.22 0.54 -0.21 [528,] 0.00 0.82 0.65 0.82 [529,] 0.00 0.71 0.59 -0.05 [530,] 0.00 -0.01 0.00 -1.00 [531,] 0.00 0.08 0.39 -0.58 $control A 'MaxControl' object with slots: tol = 1e-08 reltol = 1.4901e-08 gradtol = 1e-06 steptol = 1e-10 lambdatol = 1e-06 qrtol = 1e-10 qac = stephalving marquardt_lambda0 = 0.01 marquardt_lambdaStep = 2 marquardt_maxLambda = 1e+12 nm_alpha = 1 nm_beta = 0.5 nm_gamma = 2 sann_cand = sann_temp = 10 sann_tmax = 10 sann_randomSeed = 123 SGA_momentum = 0 Adam_momentum1 = 0.9 Adam_momentum2 = 0.999 SG_patience = SG_patienceStep = 1 SG_learningRate = 0.1 SG_batchSize = SG_clip = iterlim = 150 max.rows = 20 max.cols = 7 printLevel = 0 storeValues = FALSE storeParameters = FALSE $objectiveFn function (beta, yVec, xMat, left, right, obsBelow, obsBetween, obsAbove) { yHat <- xMat %*% beta[-length(beta)] sigma <- exp(beta[length(beta)]) ll <- rep(NA, length(yVec)) ll[obsBelow] <- pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE) ll[obsBetween] <- dnorm((yVec - yHat)[obsBetween]/sigma, log = TRUE) - log(sigma) ll[obsAbove] <- pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE) grad <- matrix(NA, nrow = length(yVec), ncol = length(beta)) grad[obsBelow, ] <- exp(dnorm((left - yHat[obsBelow])/sigma, log = TRUE) - pnorm((left - yHat[obsBelow])/sigma, log.p = TRUE)) * cbind(-xMat[obsBelow, , drop = FALSE]/sigma, -(left - yHat[obsBelow])/sigma) grad[obsBetween, ] <- cbind(((yVec - yHat)[obsBetween]/sigma) * xMat[obsBetween, , drop = FALSE]/sigma, ((yVec - yHat)[obsBetween]/sigma)^2 - 1) grad[obsAbove, ] <- exp(dnorm((yHat[obsAbove] - right)/sigma, log = TRUE) - pnorm((yHat[obsAbove] - right)/sigma, log.p = TRUE)) * cbind(xMat[obsAbove, , drop = FALSE]/sigma, -(yHat[obsAbove] - right)/sigma) attr(ll, "gradient") <- grad return(ll) } $xMean (Intercept) age yearsmarried religiousness2 religiousness3 1.00000 31.92938 7.89517 0.30885 0.24294 religiousness4 occupation rating 0.35782 4.17702 3.92090 $call censReg(formula = affairsFormula, data = Affairs2) $terms affairs ~ age + yearsmarried + religiousness + occupation + rating attr(,"variables") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"factors") age yearsmarried religiousness occupation rating affairs 0 0 0 0 0 age 1 0 0 0 0 yearsmarried 0 1 0 0 0 religiousness 0 0 1 0 0 occupation 0 0 0 1 0 rating 0 0 0 0 1 attr(,"term.labels") [1] "age" "yearsmarried" "religiousness" "occupation" [5] "rating" attr(,"order") [1] 1 1 1 1 1 attr(,"intercept") [1] 1 attr(,"response") [1] 1 attr(,".Environment") attr(,"predvars") list(affairs, age, yearsmarried, religiousness, occupation, rating) attr(,"dataClasses") affairs age yearsmarried religiousness occupation "numeric" "numeric" "numeric" "factor" "numeric" rating "numeric" $nObs Total Left-censored Uncensored Right-censored 531 394 137 0 $df.residual [1] 522 $start (Intercept) age yearsmarried religiousness2 religiousness3 5.366141 -0.051737 0.165079 -1.119963 -0.896859 religiousness4 occupation rating logSigma -2.018423 0.137126 -0.707299 2.289213 $left [1] 0 $right [1] Inf class [1] "censReg" "maxLik" "maxim" "list" print( x, digits = 2 ) Call: censReg(formula = affairsFormula, data = Affairs2) Coefficients: (Intercept) age yearsmarried religiousness2 religiousness3 7.31 -0.16 0.52 -4.12 -3.02 religiousness4 occupation rating logSigma -7.22 0.40 -2.33 2.11 print( round( margEff( x ), digits = 2 ) ) age yearsmarried religiousness2 religiousness3 religiousness4 -0.04 0.13 -0.99 -0.72 -1.73 occupation rating 0.10 -0.56 printME( margEff( x ) ) age yearsmarried religiousness2 religiousness3 religiousness4 -0.039 0.126 -0.990 -0.725 -1.734 occupation rating 0.096 -0.559 attr(,"vcov") age yearsmarried religiousness2 religiousness3 religiousness4 age 0.000 -0.001 0.000 0.000 0.000 yearsmarried -0.001 0.001 0.000 -0.001 -0.002 religiousness2 0.000 0.000 0.156 0.114 0.115 religiousness3 0.000 -0.001 0.114 0.162 0.115 religiousness4 0.000 -0.002 0.115 0.115 0.163 occupation 0.000 0.000 -0.001 0.001 0.001 rating 0.000 0.000 0.005 0.005 0.003 occupation rating age 0.000 0.000 yearsmarried 0.000 0.000 religiousness2 -0.001 0.005 religiousness3 0.001 0.005 religiousness4 0.001 0.003 occupation 0.004 0.000 rating 0.000 0.011 attr(,"df.residual") [1] 522 attr(,"class") [1] "margEff.censReg" "numeric" print( summary( margEff( x ) ), digits = sDigits ) Marg. Eff. Std. Error t value Pr(>|t|) age -0.039 0.021 -1.9 0.06 . yearsmarried 0.126 0.034 3.7 3e-04 *** religiousness2 -0.990 0.395 -2.5 0.01 * religiousness3 -0.725 0.402 -1.8 0.07 . religiousness4 -1.734 0.403 -4.3 2e-05 *** occupation 0.096 0.066 1.5 0.14 rating -0.559 0.104 -5.4 1e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 print( maxLik:::summary.maxLik( x ), sDigits ) -------------------------------------------- Maximum Likelihood estimation Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-Likelihood: -638.42 9 free parameters Estimates: Estimate Std. error t value Pr(> t) (Intercept) 7.311 3.120 2.3 0.02 * age -0.164 0.088 -1.9 0.06 . yearsmarried 0.523 0.144 3.6 3e-04 *** religiousness2 -4.120 1.648 -2.5 0.01 * religiousness3 -3.017 1.675 -1.8 0.07 . religiousness4 -7.218 1.697 -4.3 2e-05 *** occupation 0.401 0.273 1.5 0.14 rating -2.328 0.438 -5.3 1e-07 *** logSigma 2.110 0.070 30.1 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 -------------------------------------------- print( summary( x ), digits = sDigits ) Call: censReg(formula = affairsFormula, data = Affairs2) Observations: Total Left-censored Uncensored Right-censored 531 394 137 0 Coefficients: Estimate Std. error t value Pr(> t) (Intercept) 7.311 3.120 2.3 0.02 * age -0.164 0.088 -1.9 0.06 . yearsmarried 0.523 0.144 3.6 3e-04 *** religiousness2 -4.120 1.648 -2.5 0.01 * religiousness3 -3.017 1.675 -1.8 0.07 . religiousness4 -7.218 1.697 -4.3 2e-05 *** occupation 0.401 0.273 1.5 0.14 rating -2.328 0.438 -5.3 1e-07 *** logSigma 2.110 0.070 30.1 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Newton-Raphson maximisation, 0 iterations Return code 0: removed message Log-likelihood: -638.42 on 9 Df > round( coef( estResultEmpty ), 2 ) (Intercept) age yearsmarried religiousness2 religiousness3 7.31 -0.16 0.52 -4.12 -3.02 religiousness4 occupation rating logSigma -7.22 0.40 -2.33 2.11 > round( vcov( estResultEmpty ), 2 ) (Intercept) age yearsmarried religiousness2 religiousness3 (Intercept) 9.74 -0.15 0.09 -2.23 -2.37 age -0.15 0.01 -0.01 0.00 0.01 yearsmarried 0.09 -0.01 0.02 0.00 -0.02 religiousness2 -2.23 0.00 0.00 2.72 1.98 religiousness3 -2.37 0.01 -0.02 1.98 2.81 religiousness4 -2.05 0.01 -0.03 2.01 2.02 occupation -0.17 -0.01 0.01 -0.01 0.01 rating -0.78 0.00 0.01 0.10 0.08 logSigma 0.01 0.00 0.00 -0.02 -0.01 religiousness4 occupation rating logSigma (Intercept) -2.05 -0.17 -0.78 0.01 age 0.01 -0.01 0.00 0.00 yearsmarried -0.03 0.01 0.01 0.00 religiousness2 2.01 -0.01 0.10 -0.02 religiousness3 2.02 0.01 0.08 -0.01 religiousness4 2.88 0.02 0.07 -0.02 occupation 0.02 0.07 -0.01 0.00 rating 0.07 -0.01 0.19 -0.01 logSigma -0.02 0.00 -0.01 0.00 > formula( estResultEmpty ) affairs ~ age + yearsmarried + religiousness + occupation + rating > model.frame( estResultEmpty ) affairs age yearsmarried religiousness occupation rating 4 0 37.0 10.000 3 7 4 5 0 27.0 4.000 4 6 4 11 0 32.0 15.000 1 1 4 23 0 22.0 0.750 2 6 3 29 0 32.0 1.500 2 5 5 44 0 22.0 0.750 2 1 3 45 0 57.0 15.000 2 4 4 47 0 32.0 15.000 4 1 2 49 0 22.0 1.500 4 4 5 50 0 37.0 15.000 2 7 2 55 0 27.0 4.000 4 6 4 80 0 22.0 1.500 2 5 4 86 0 27.0 4.000 4 5 4 93 0 37.0 15.000 1 5 5 108 0 37.0 15.000 2 4 3 114 0 22.0 0.750 3 5 4 115 0 22.0 1.500 2 5 5 116 0 27.0 10.000 2 1 5 123 0 22.0 1.500 2 5 5 127 0 22.0 1.500 2 5 5 129 0 27.0 10.000 4 5 4 134 0 32.0 10.000 3 1 5 137 0 37.0 4.000 2 6 4 139 0 22.0 1.500 2 5 5 147 0 27.0 7.000 4 1 5 153 0 27.0 4.000 3 5 5 155 0 27.0 4.000 3 5 4 162 0 42.0 15.000 4 6 3 163 0 22.0 1.500 3 5 5 165 0 27.0 0.417 4 6 4 170 0 32.0 4.000 1 6 4 172 0 22.0 1.500 4 5 3 184 0 42.0 15.000 3 1 4 187 0 22.0 4.000 4 5 5 192 0 22.0 1.500 1 3 5 194 0 22.0 0.750 3 1 5 224 0 27.0 4.000 2 6 1 228 0 22.0 4.000 3 5 5 239 0 27.0 7.000 4 6 5 241 0 42.0 15.000 2 5 4 245 0 27.0 1.500 4 3 5 249 0 42.0 15.000 2 6 4 265 0 32.0 7.000 2 6 4 269 0 27.0 10.000 4 6 4 271 0 22.0 4.000 1 5 5 277 0 37.0 15.000 4 3 1 292 0 37.0 15.000 4 1 5 293 0 27.0 0.750 4 5 4 295 0 32.0 10.000 4 6 4 320 0 37.0 10.000 3 6 4 321 0 22.0 0.750 2 5 5 324 0 27.0 4.000 2 4 5 334 0 32.0 7.000 4 6 4 351 0 42.0 15.000 2 3 5 355 0 37.0 10.000 4 6 4 361 0 47.0 15.000 3 6 5 366 0 27.0 1.500 2 6 4 370 0 27.0 4.000 3 5 5 378 0 22.0 0.125 2 5 5 381 0 47.0 15.000 4 4 3 382 0 32.0 15.000 1 5 5 383 0 27.0 7.000 4 5 5 384 0 22.0 1.500 3 5 5 400 0 27.0 4.000 3 6 5 403 0 22.0 1.500 3 5 5 409 0 57.0 15.000 2 7 2 412 0 17.5 1.500 3 6 5 413 0 57.0 15.000 4 6 5 416 0 22.0 0.750 2 3 4 418 0 42.0 4.000 4 3 3 422 0 22.0 1.500 4 1 5 435 0 22.0 0.417 1 6 4 439 0 32.0 15.000 4 5 5 445 0 27.0 1.500 3 5 2 447 0 22.0 1.500 3 1 5 448 0 37.0 15.000 3 1 4 449 0 32.0 15.000 4 3 4 478 0 37.0 10.000 2 5 3 482 0 37.0 10.000 4 5 4 489 0 27.0 0.417 1 3 4 491 0 57.0 15.000 3 6 1 492 0 37.0 10.000 1 6 4 503 0 37.0 15.000 3 5 5 508 0 37.0 15.000 4 6 5 512 0 37.0 10.000 2 6 4 515 0 22.0 0.125 4 4 5 532 0 37.0 15.000 4 6 4 533 0 22.0 4.000 4 6 4 535 0 27.0 7.000 4 5 4 537 0 57.0 15.000 4 5 4 538 0 32.0 15.000 3 6 3 543 0 22.0 1.500 2 5 4 547 0 32.0 7.000 4 1 5 550 0 37.0 15.000 4 6 5 578 0 27.0 7.000 3 5 4 583 0 37.0 15.000 4 6 5 586 0 37.0 15.000 4 3 2 597 0 22.0 0.750 4 1 5 602 0 27.0 7.000 4 2 4 603 0 27.0 7.000 2 2 5 612 0 42.0 15.000 4 5 3 613 0 27.0 7.000 2 1 2 621 0 22.0 1.500 3 5 5 630 0 22.0 0.125 2 4 5 631 0 27.0 1.500 4 5 5 632 0 32.0 1.500 2 6 5 639 0 27.0 1.500 2 6 5 645 0 27.0 10.000 4 1 3 647 0 42.0 15.000 4 6 5 648 0 27.0 1.500 2 6 5 651 0 27.0 4.000 2 6 3 655 0 32.0 10.000 3 5 3 667 0 32.0 15.000 3 5 4 670 0 22.0 0.750 2 6 5 671 0 37.0 15.000 2 1 4 673 0 27.0 4.000 4 5 5 701 0 27.0 4.000 1 5 4 705 0 27.0 10.000 2 1 4 717 0 52.0 15.000 2 5 4 719 0 27.0 4.000 3 6 3 723 0 37.0 4.000 1 5 4 724 0 27.0 4.000 4 5 4 734 0 57.0 15.000 4 6 4 735 0 27.0 7.000 1 5 4 736 0 37.0 7.000 4 6 3 737 0 22.0 0.750 2 4 3 739 0 32.0 4.000 2 5 3 743 0 37.0 15.000 4 6 3 745 0 22.0 0.750 2 4 3 747 0 42.0 15.000 4 6 3 752 0 37.0 15.000 4 1 2 754 0 27.0 7.000 4 5 3 760 0 32.0 4.000 2 5 5 763 0 27.0 4.000 2 6 5 774 0 27.0 4.000 2 5 5 784 0 32.0 10.000 3 1 4 788 0 27.0 1.500 4 1 2 794 0 57.0 15.000 2 5 2 795 0 22.0 1.500 4 5 4 798 0 42.0 15.000 3 3 4 800 0 57.0 15.000 4 2 2 803 0 57.0 15.000 4 6 5 807 0 22.0 0.125 4 4 5 812 0 32.0 10.000 4 1 5 820 0 42.0 15.000 3 5 4 823 0 27.0 1.500 2 6 5 830 0 32.0 0.125 2 5 2 843 0 27.0 4.000 3 5 4 848 0 27.0 10.000 2 1 4 851 0 32.0 7.000 4 1 3 854 0 37.0 15.000 4 5 4 857 0 32.0 1.500 4 6 5 859 0 32.0 4.000 3 5 3 863 0 37.0 7.000 4 5 5 865 0 22.0 0.417 3 3 5 867 0 27.0 7.000 4 1 5 870 0 27.0 0.750 3 5 5 873 0 27.0 4.000 2 5 5 875 0 32.0 10.000 4 4 5 876 0 32.0 15.000 1 5 5 877 0 22.0 0.750 3 4 5 880 0 27.0 7.000 4 1 4 903 0 27.0 0.417 4 5 4 904 0 37.0 15.000 4 5 4 905 0 37.0 15.000 2 1 3 908 0 22.0 4.000 1 5 4 909 0 37.0 15.000 4 5 3 910 0 22.0 1.500 2 4 5 912 0 52.0 15.000 4 6 2 914 0 22.0 1.500 4 5 5 916 0 32.0 4.000 2 3 5 920 0 22.0 1.500 3 6 5 921 0 27.0 0.750 2 3 3 925 0 22.0 7.000 2 5 2 926 0 27.0 0.750 2 5 3 929 0 37.0 15.000 4 1 2 931 0 22.0 1.500 1 1 5 945 0 37.0 10.000 2 4 4 947 0 37.0 15.000 4 5 3 949 0 42.0 15.000 3 3 3 950 0 22.0 4.000 2 5 5 961 0 52.0 7.000 2 6 2 965 0 27.0 0.750 2 5 5 966 0 27.0 4.000 2 4 5 987 0 22.0 1.500 4 6 5 990 0 22.0 4.000 4 5 3 992 0 22.0 4.000 1 5 4 1009 0 37.0 10.000 3 6 3 1021 0 42.0 15.000 4 6 5 1026 0 47.0 15.000 4 5 5 1027 0 22.0 1.500 4 5 4 1030 0 32.0 10.000 3 1 4 1031 0 22.0 7.000 1 3 5 1034 0 32.0 10.000 4 5 4 1037 0 27.0 1.500 2 2 4 1038 0 37.0 15.000 4 5 5 1039 0 42.0 4.000 3 4 5 1046 0 32.0 7.000 4 5 5 1054 0 42.0 15.000 4 6 5 1059 0 27.0 4.000 4 6 4 1063 0 22.0 0.750 4 6 5 1068 0 27.0 4.000 4 5 3 1073 0 32.0 10.000 3 5 5 1077 0 37.0 15.000 4 4 4 1081 0 32.0 7.000 2 5 4 1083 0 42.0 15.000 3 1 4 1084 0 32.0 15.000 1 5 5 1086 0 27.0 4.000 3 5 5 1087 0 32.0 15.000 4 3 4 1089 0 22.0 0.750 3 2 4 1096 0 22.0 1.500 3 5 3 1102 0 42.0 15.000 4 3 5 1103 0 52.0 15.000 3 5 4 1109 0 47.0 15.000 4 2 3 1115 0 57.0 15.000 2 6 4 1119 0 32.0 7.000 4 5 5 1124 0 27.0 7.000 4 1 4 1126 0 22.0 1.500 1 6 5 1128 0 22.0 4.000 3 1 4 1129 0 22.0 1.500 2 1 5 1130 0 42.0 15.000 2 6 4 1133 0 57.0 15.000 4 2 4 1140 0 27.0 7.000 2 1 5 1143 0 22.0 4.000 3 1 5 1146 0 37.0 15.000 4 5 3 1153 0 32.0 7.000 1 6 4 1156 0 22.0 1.500 2 5 5 1157 0 22.0 1.500 3 1 3 1158 0 52.0 15.000 2 5 5 1160 0 37.0 15.000 2 1 1 1161 0 32.0 10.000 2 5 5 1166 0 42.0 15.000 4 4 5 1177 0 27.0 4.000 3 4 5 1178 0 37.0 15.000 4 6 5 1180 0 27.0 1.500 3 5 5 1187 0 22.0 0.125 2 6 3 1191 0 32.0 10.000 2 6 3 1195 0 27.0 4.000 4 5 4 1207 0 27.0 7.000 2 5 1 1209 0 37.0 15.000 2 5 5 1211 0 47.0 15.000 4 6 4 1215 0 27.0 1.500 1 5 5 1221 0 37.0 15.000 4 6 4 1226 0 32.0 15.000 4 1 4 1229 0 32.0 7.000 4 5 4 1231 0 42.0 15.000 3 1 3 1234 0 27.0 7.000 3 1 4 1235 0 27.0 1.500 3 4 2 1242 0 22.0 1.500 3 3 5 1245 0 27.0 4.000 3 4 2 1260 0 27.0 7.000 3 1 2 1266 0 37.0 15.000 2 5 4 1271 0 37.0 7.000 3 4 4 1273 0 22.0 1.500 2 5 5 1280 0 22.0 1.500 4 5 3 1282 0 32.0 10.000 4 1 5 1285 0 27.0 4.000 2 5 3 1295 0 22.0 0.417 4 5 5 1298 0 27.0 4.000 2 5 5 1299 0 37.0 15.000 4 5 3 1305 0 27.0 7.000 2 4 2 1311 0 32.0 4.000 2 5 5 1314 0 32.0 4.000 2 6 4 1319 0 22.0 1.500 3 4 5 1322 0 22.0 4.000 4 3 4 1324 0 17.5 0.750 2 5 4 1327 0 32.0 10.000 4 4 5 1330 0 37.0 15.000 4 5 3 1332 0 32.0 4.000 3 4 5 1333 0 27.0 1.500 2 3 2 1336 0 22.0 7.000 4 1 5 1344 0 27.0 4.000 1 4 4 1358 0 42.0 4.000 4 5 5 1359 0 32.0 4.000 2 1 5 1361 0 52.0 15.000 2 7 4 1364 0 22.0 1.500 2 1 4 1368 0 52.0 15.000 4 2 4 1384 0 22.0 0.417 3 1 5 1390 0 22.0 1.500 2 5 5 1393 0 27.0 4.000 4 6 4 1394 0 32.0 15.000 4 1 5 1402 0 27.0 1.500 2 3 5 1407 0 32.0 4.000 1 6 5 1408 0 37.0 15.000 3 6 4 1412 0 32.0 10.000 2 6 5 1416 0 37.0 1.500 4 5 3 1417 0 32.0 1.500 2 4 4 1418 0 32.0 10.000 4 1 4 1419 0 47.0 15.000 4 5 4 1423 0 27.0 4.000 3 4 5 1424 0 37.0 15.000 4 4 2 1432 0 27.0 0.750 4 5 5 1433 0 37.0 15.000 4 1 5 1437 0 32.0 15.000 3 1 5 1438 0 27.0 10.000 2 1 5 1439 0 27.0 7.000 2 6 5 1446 0 37.0 15.000 2 1 3 1450 0 27.0 1.500 2 4 4 1451 0 22.0 0.750 2 1 5 1452 0 22.0 4.000 4 2 4 1453 0 42.0 0.125 4 6 4 1456 0 27.0 1.500 4 6 5 1464 0 27.0 7.000 3 6 3 1469 0 52.0 15.000 4 1 3 1481 0 27.0 1.500 2 5 5 1482 0 27.0 1.500 3 5 5 1497 0 27.0 4.000 4 1 5 1504 0 27.0 4.000 4 1 5 1513 0 47.0 15.000 2 5 5 1515 0 32.0 15.000 3 5 3 1534 0 42.0 7.000 2 5 5 1535 0 22.0 0.750 4 6 4 1536 0 27.0 0.125 3 6 5 1540 0 32.0 10.000 3 6 5 1557 0 32.0 10.000 3 1 5 1566 0 57.0 15.000 4 5 5 1567 0 27.0 4.000 3 6 5 1576 0 32.0 7.000 4 1 5 1584 0 37.0 10.000 4 1 5 1585 0 32.0 10.000 1 1 4 1590 0 22.0 4.000 3 1 4 1594 0 27.0 7.000 4 3 2 1603 0 32.0 7.000 2 5 5 1608 0 27.0 1.500 4 1 3 1609 0 22.0 1.500 4 5 5 1615 0 22.0 1.500 4 5 4 1616 0 32.0 7.000 3 1 5 1617 0 47.0 15.000 3 5 4 1620 0 22.0 0.750 3 1 5 1621 0 22.0 1.500 2 5 5 1637 0 27.0 4.000 1 5 5 1638 0 52.0 15.000 4 5 5 1650 0 32.0 10.000 4 6 5 1654 0 47.0 15.000 4 6 4 1665 0 27.0 7.000 2 1 2 1670 0 22.0 1.500 4 4 5 1671 0 32.0 10.000 2 5 4 1675 0 22.0 0.750 2 5 4 1688 0 22.0 1.500 2 5 5 1691 0 42.0 15.000 3 6 4 1698 0 42.0 15.000 4 4 4 1704 0 57.0 15.000 3 5 2 1705 0 42.0 15.000 3 6 2 1711 0 32.0 7.000 2 1 2 1723 0 22.0 1.500 1 6 5 1726 0 22.0 0.750 1 4 5 1749 0 32.0 15.000 4 1 5 1752 0 22.0 1.500 2 5 3 1758 0 27.0 4.000 4 1 5 1773 0 32.0 1.500 2 7 3 1775 0 57.0 15.000 4 3 1 1786 0 37.0 7.000 4 5 5 1793 0 52.0 15.000 2 5 4 1799 0 47.0 15.000 4 6 5 1803 0 27.0 7.000 2 5 4 1806 0 27.0 7.000 4 5 5 1807 0 22.0 4.000 2 3 3 1808 0 37.0 7.000 2 6 5 1814 0 27.0 7.000 4 4 3 1815 0 42.0 10.000 4 6 4 1818 0 22.0 1.500 3 1 5 1827 0 22.0 4.000 2 1 3 1834 0 57.0 15.000 4 6 5 1835 0 37.0 15.000 4 4 3 1843 0 27.0 7.000 3 5 5 1846 0 17.5 10.000 4 4 5 1850 0 22.0 4.000 4 5 5 1851 0 27.0 4.000 2 1 4 1854 0 37.0 15.000 2 5 1 1861 0 27.0 7.000 2 5 4 1866 0 27.0 4.000 4 5 5 1873 0 22.0 0.125 1 3 5 1875 0 27.0 7.000 4 1 4 1892 0 32.0 10.000 4 5 4 1895 0 32.0 15.000 2 3 4 1896 0 22.0 1.500 3 5 5 1897 0 27.0 4.000 4 4 4 1904 0 27.0 7.000 2 1 2 1905 0 27.0 7.000 3 1 4 1908 0 42.0 15.000 2 1 4 1916 0 42.0 15.000 4 5 4 1918 0 27.0 7.000 4 3 3 1920 0 27.0 7.000 2 6 2 1930 0 42.0 15.000 3 3 3 1940 0 27.0 4.000 3 3 5 1947 0 27.0 7.000 3 1 4 1949 0 22.0 1.500 2 4 5 1951 0 27.0 4.000 4 1 4 1952 0 22.0 4.000 4 5 5 1960 0 22.0 1.500 2 4 5 9001 0 47.0 15.000 4 5 4 9012 0 37.0 10.000 2 6 2 9023 0 37.0 15.000 3 5 4 9029 0 27.0 4.000 2 1 4 6 3 27.0 1.500 3 4 4 12 3 27.0 4.000 3 1 5 53 12 32.0 10.000 3 5 2 67 1 22.0 0.125 4 5 5 79 1 22.0 1.500 2 1 5 122 12 37.0 15.000 4 5 2 126 7 22.0 1.500 2 3 4 133 2 37.0 15.000 2 6 4 138 3 32.0 15.000 4 3 2 154 1 37.0 15.000 4 4 2 159 7 42.0 15.000 3 1 4 176 12 37.0 10.000 2 6 2 181 12 32.0 15.000 3 1 2 182 3 27.0 4.000 1 6 5 186 7 37.0 10.000 2 7 3 189 7 27.0 4.000 3 5 5 204 1 42.0 15.000 4 5 5 232 7 27.0 4.000 3 5 4 252 12 27.0 1.500 3 5 4 253 12 27.0 7.000 4 6 2 274 3 42.0 15.000 4 5 4 275 7 27.0 10.000 4 7 3 287 1 27.0 1.500 2 5 2 288 1 32.0 4.000 4 6 4 325 1 27.0 7.000 3 1 3 328 3 32.0 10.000 4 1 4 344 3 27.0 4.000 2 7 2 354 1 32.0 10.000 4 1 5 367 7 32.0 7.000 2 6 4 369 7 37.0 15.000 2 6 4 390 7 37.0 10.000 1 5 3 392 12 32.0 10.000 2 5 5 423 7 52.0 15.000 2 6 4 432 7 42.0 15.000 1 1 3 436 1 52.0 15.000 2 6 3 483 2 37.0 15.000 3 6 5 513 12 22.0 4.000 3 3 4 516 12 27.0 7.000 1 6 2 518 1 27.0 4.000 3 5 5 520 12 47.0 15.000 4 6 5 526 12 42.0 15.000 4 1 1 528 7 27.0 4.000 3 3 4 553 7 32.0 7.000 4 4 5 576 1 32.0 0.417 3 3 4 625 12 37.0 15.000 2 5 4 635 7 22.0 4.000 2 6 4 646 1 27.0 4.000 2 4 5 659 1 27.0 4.000 3 3 3 666 1 27.0 10.000 4 1 4 679 1 32.0 7.000 3 7 4 729 7 32.0 7.000 2 4 1 755 3 22.0 1.500 1 3 2 758 7 22.0 4.000 3 6 4 770 7 42.0 15.000 4 6 4 786 2 57.0 15.000 1 5 4 797 7 32.0 4.000 3 5 2 811 1 27.0 4.000 1 4 4 834 7 32.0 7.000 4 1 4 858 2 57.0 15.000 1 4 4 885 7 42.0 15.000 4 5 2 893 7 37.0 10.000 1 5 3 927 3 42.0 15.000 3 6 1 928 1 52.0 15.000 3 4 4 933 2 27.0 7.000 3 5 3 951 12 32.0 7.000 2 4 2 968 1 22.0 4.000 4 2 5 972 3 27.0 7.000 3 6 4 975 12 37.0 15.000 1 5 5 977 7 32.0 15.000 3 1 3 981 7 27.0 7.000 2 5 5 986 1 32.0 7.000 3 5 3 1002 1 32.0 1.500 2 2 4 1007 12 42.0 15.000 4 1 2 1011 7 32.0 10.000 3 5 4 1035 7 37.0 4.000 1 6 3 1050 1 27.0 4.000 2 5 3 1056 12 42.0 15.000 3 4 3 1075 12 37.0 10.000 2 6 2 1080 12 27.0 7.000 1 3 3 1125 3 27.0 7.000 4 1 2 1131 3 32.0 10.000 2 4 4 1138 12 17.5 0.750 2 1 3 1150 12 32.0 15.000 3 5 4 1163 2 22.0 7.000 4 4 3 1169 1 32.0 7.000 4 6 5 1198 7 27.0 4.000 2 6 2 1218 12 32.0 15.000 3 5 1 1230 12 42.0 15.000 2 1 2 1236 7 42.0 15.000 3 5 4 1247 12 32.0 10.000 2 4 2 1259 12 32.0 15.000 3 1 1 1353 12 47.0 15.000 4 6 4 1370 2 42.0 15.000 2 6 3 1427 12 37.0 15.000 3 6 3 1460 7 27.0 10.000 2 6 4 1480 2 37.0 15.000 2 5 4 1505 12 32.0 15.000 1 5 2 1543 7 32.0 10.000 3 6 3 1548 2 37.0 15.000 4 5 1 1550 7 27.0 1.500 2 5 5 1561 3 47.0 15.000 2 5 2 1564 12 37.0 15.000 2 5 4 1573 12 27.0 4.000 2 5 5 1575 2 27.0 10.000 4 1 5 1599 1 22.0 4.000 3 1 3 1622 12 52.0 7.000 4 5 5 1629 2 27.0 4.000 1 3 5 1664 7 37.0 15.000 2 6 4 1669 2 27.0 4.000 1 3 1 1674 12 17.5 0.750 2 3 5 1685 7 22.0 4.000 1 3 5 1697 2 32.0 4.000 4 6 4 1716 1 22.0 1.500 3 5 2 1730 3 42.0 15.000 2 5 4 1731 1 32.0 7.000 4 4 4 1732 12 37.0 15.000 3 6 2 1743 1 42.0 15.000 3 6 3 1751 1 27.0 4.000 1 5 4 1757 2 37.0 15.000 4 7 3 1763 7 37.0 15.000 3 6 4 1766 3 22.0 1.500 2 3 3 1772 3 32.0 4.000 3 6 2 1782 12 52.0 15.000 1 5 5 1784 12 47.0 15.000 1 6 5 1791 3 32.0 15.000 4 4 4 1831 7 32.0 15.000 3 3 2 1840 7 27.0 7.000 4 1 2 1844 12 42.0 15.000 3 6 2 1856 7 42.0 15.000 2 3 2 1876 12 27.0 7.000 2 5 4 1929 3 32.0 10.000 4 4 3 1935 7 47.0 15.000 3 4 2 1938 1 22.0 1.500 1 2 5 1941 7 32.0 10.000 2 5 4 1954 2 32.0 10.000 2 6 5 1959 2 22.0 7.000 3 6 2 9010 1 32.0 15.000 3 1 5 > round( estfun( estResultEmpty )[ 20 * c(1:26), ], 2 ) (Intercept) age yearsmarried religiousness2 religiousness3 [1,] -0.03 -0.65 -0.04 -0.03 0.00 [2,] -0.07 -2.92 -1.04 -0.07 0.00 [3,] -0.05 -2.56 -0.82 0.00 0.00 [4,] -0.06 -1.49 -0.02 0.00 0.00 [5,] -0.03 -0.92 -0.24 -0.03 0.00 [6,] -0.07 -1.99 -0.30 0.00 -0.07 [7,] -0.07 -3.02 -1.08 0.00 -0.07 [8,] -0.10 -3.19 -1.49 0.00 0.00 [9,] -0.09 -3.87 -1.38 0.00 -0.09 [10,] -0.03 -0.82 -0.12 0.00 0.00 [11,] -0.02 -0.49 -0.03 -0.02 0.00 [12,] -0.06 -2.14 -0.87 -0.06 0.00 [13,] -0.07 -2.58 -1.05 0.00 0.00 [14,] -0.03 -0.82 -0.12 0.00 0.00 [15,] -0.03 -0.58 -0.11 0.00 0.00 [16,] -0.08 -2.63 -0.82 0.00 0.00 [17,] -0.08 -3.45 -1.23 0.00 -0.08 [18,] -0.03 -1.42 -0.34 0.00 0.00 [19,] -0.06 -2.40 -0.86 -0.06 0.00 [20,] 0.19 6.94 2.81 0.00 0.00 [21,] 0.17 5.56 1.74 0.00 0.00 [22,] 0.18 3.95 0.72 0.18 0.00 [23,] 0.19 4.29 0.78 0.00 0.00 [24,] 0.12 3.33 0.49 0.12 0.00 [25,] 0.39 20.17 2.72 0.00 0.00 [26,] 0.05 1.67 0.78 0.00 0.05 religiousness4 occupation rating logSigma [1,] 0.00 -0.15 -0.15 -0.27 [2,] 0.00 -0.35 -0.28 -0.22 [3,] -0.05 -0.22 -0.16 -0.28 [4,] 0.00 -0.17 -0.22 -0.28 [5,] 0.00 -0.07 -0.17 -0.29 [6,] 0.00 -0.44 -0.22 -0.19 [7,] 0.00 -0.22 -0.29 -0.20 [8,] 0.00 -0.50 -0.50 0.03 [9,] 0.00 -0.28 -0.28 -0.05 [10,] -0.03 -0.18 -0.12 -0.28 [11,] 0.00 -0.02 -0.11 -0.24 [12,] 0.00 -0.29 -0.29 -0.27 [13,] -0.07 -0.35 -0.21 -0.22 [14,] -0.03 -0.18 -0.12 -0.28 [15,] -0.03 -0.05 -0.11 -0.26 [16,] 0.00 -0.08 -0.33 -0.13 [17,] 0.00 -0.49 -0.33 -0.13 [18,] -0.03 -0.20 -0.14 -0.29 [19,] 0.00 -0.06 -0.23 -0.27 [20,] 0.19 0.94 0.38 1.39 [21,] 0.17 0.17 0.70 1.05 [22,] 0.00 1.08 0.72 1.19 [23,] 0.19 0.39 0.97 1.58 [24,] 0.00 0.74 0.25 0.03 [25,] 0.39 1.94 1.94 9.23 [26,] 0.00 0.16 0.10 -0.82 > round( meat( estResultEmpty ), 2 ) (Intercept) age yearsmarried religiousness2 religiousness3 (Intercept) 0.01 0.28 0.07 0.00 0.00 age 0.28 9.92 2.73 0.09 0.06 yearsmarried 0.07 2.73 0.87 0.02 0.02 religiousness2 0.00 0.09 0.02 0.00 0.00 religiousness3 0.00 0.06 0.02 0.00 0.00 religiousness4 0.00 0.10 0.03 0.00 0.00 occupation 0.04 1.22 0.32 0.01 0.01 rating 0.03 1.04 0.26 0.01 0.01 logSigma 0.06 1.96 0.43 0.02 0.01 religiousness4 occupation rating logSigma (Intercept) 0.00 0.04 0.03 0.06 age 0.10 1.22 1.04 1.96 yearsmarried 0.03 0.32 0.26 0.43 religiousness2 0.00 0.01 0.01 0.02 religiousness3 0.00 0.01 0.01 0.01 religiousness4 0.00 0.01 0.01 0.03 occupation 0.01 0.18 0.14 0.25 rating 0.01 0.14 0.13 0.25 logSigma 0.03 0.25 0.25 0.84 > round( bread( estResultEmpty ), 1 ) (Intercept) age yearsmarried religiousness2 religiousness3 (Intercept) 5169.4 -78.2 47.6 -1183.5 -1258.2 age -78.2 4.1 -5.1 1.0 4.4 yearsmarried 47.6 -5.1 11.0 -1.2 -9.8 religiousness2 -1183.5 1.0 -1.2 1442.7 1053.4 religiousness3 -1258.2 4.4 -9.8 1053.4 1490.3 religiousness4 -1090.9 2.7 -16.8 1068.4 1071.9 occupation -88.0 -3.0 2.9 -7.5 4.6 rating -413.6 0.5 3.5 53.0 44.4 logSigma 3.1 -0.3 0.8 -8.1 -5.4 religiousness4 occupation rating logSigma (Intercept) -1090.9 -88.0 -413.6 3.1 age 2.7 -3.0 0.5 -0.3 yearsmarried -16.8 2.9 3.5 0.8 religiousness2 1068.4 -7.5 53.0 -8.1 religiousness3 1071.9 4.6 44.4 -5.4 religiousness4 1529.9 8.1 34.8 -13.0 occupation 8.1 39.5 -3.9 0.6 rating 34.8 -3.9 101.8 -4.2 logSigma -13.0 0.6 -4.2 2.6 > round( sandwich( estResultEmpty ), 2 ) (Intercept) age yearsmarried religiousness2 religiousness3 (Intercept) 10.32 -0.20 0.17 -1.13 -1.67 age -0.20 0.01 -0.01 -0.01 0.01 yearsmarried 0.17 -0.01 0.02 0.00 -0.03 religiousness2 -1.13 -0.01 0.00 2.37 1.58 religiousness3 -1.67 0.01 -0.03 1.58 2.34 religiousness4 -1.64 0.02 -0.06 1.58 1.68 occupation -0.22 0.00 0.01 -0.01 0.01 rating -0.71 0.00 0.00 0.01 0.00 logSigma -0.05 0.00 0.00 0.00 0.00 religiousness4 occupation rating logSigma (Intercept) -1.64 -0.22 -0.71 -0.05 age 0.02 0.00 0.00 0.00 yearsmarried -0.06 0.01 0.00 0.00 religiousness2 1.58 -0.01 0.01 0.00 religiousness3 1.68 0.01 0.00 0.00 religiousness4 2.49 -0.01 -0.03 0.00 occupation -0.01 0.07 0.00 0.00 rating -0.03 0.00 0.17 0.00 logSigma 0.00 0.00 0.00 0.00 > # all.equal( sandwich( estResultEmpty ), vcov( estResultEmpty ) ) > waldtest( estResultEmpty, . ~ . - age ) Wald test Model 1: affairs ~ age + yearsmarried + religiousness + occupation + rating Model 2: affairs ~ yearsmarried + religiousness + occupation + rating Res.Df Df Chisq Pr(>Chisq) 1 522 2 523 -1 3.49 0.062 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > waldtest( estResultEmpty, . ~ . - age, vcov = sandwich( estResultEmpty ) ) Wald test Model 1: affairs ~ age + yearsmarried + religiousness + occupation + rating Model 2: affairs ~ yearsmarried + religiousness + occupation + rating Res.Df Df Chisq Pr(>Chisq) 1 522 2 523 -1 2.85 0.092 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > > # returning log-likelihood contributions only (no estimations) > logLikBhhh <- censReg( affairsFormula, data = Affairs, method = "BHHH", + start = coef( estResultBhhh ), logLikOnly = TRUE ) > round( c( logLikBhhh ), 2 ) [1] -0.33 -0.17 -0.72 -0.08 -0.40 -0.12 -0.30 -0.32 -0.67 -0.08 -1.19 -0.17 [13] -0.20 -0.27 -0.16 -0.55 -0.79 -0.18 -0.18 -0.29 -0.18 -0.18 -0.30 -0.18 [25] -0.23 -0.18 -0.10 -0.24 -0.14 -0.22 -0.47 -0.13 -0.11 -0.22 -0.36 -0.22 [37] -0.32 -0.12 -0.22 -0.08 -0.12 -0.26 -0.06 -0.89 -0.21 -0.17 -0.15 -0.52 [49] -0.06 -0.55 -0.04 -0.36 -0.07 -0.31 -0.32 -0.88 -0.09 -0.18 -0.11 -0.27 [61] -0.47 -0.31 -0.16 -0.18 -0.20 -0.33 -0.23 -0.24 -0.06 -0.25 -0.14 -0.11 [73] -0.15 -0.36 -0.62 -0.14 -0.13 -0.15 -0.13 -0.75 -0.16 -0.12 -0.23 -0.13 [85] -0.06 -0.35 -0.28 -0.39 -0.09 -0.38 -0.37 -0.56 -0.21 -0.20 -0.25 -0.11 [97] -0.78 -0.53 -0.32 -0.25 -0.10 -0.41 -0.07 -0.08 -0.37 -0.20 -0.22 -0.18 [109] -0.77 -0.27 -0.08 -0.25 -0.04 -0.14 -0.30 -0.25 -0.65 -0.20 -0.06 -0.18 [121] -0.22 -0.22 -0.44 -0.65 -0.13 -0.18 -0.14 -0.07 -0.13 -0.16 -0.35 -0.21 [133] -0.16 -0.46 -0.49 -0.53 -0.17 -0.49 -0.10 -0.40 -0.42 -0.33 -0.24 -0.39 [145] -0.35 -0.29 -0.16 -0.19 -0.19 -0.52 -0.26 -0.36 -0.38 -0.53 -0.36 -0.47 [157] -0.43 -0.59 -0.33 -0.16 -0.21 -0.20 -0.18 -0.19 -0.27 -0.23 -0.69 -0.14 [169] -0.36 -0.35 -0.12 -0.07 -0.12 -0.40 -0.16 -0.39 -0.22 -0.42 -0.22 -0.35 [181] -0.51 -0.06 -0.28 -0.09 -0.09 -0.10 -0.09 -0.20 -0.15 -0.62 -0.11 -0.17 [193] -0.10 -0.35 -0.69 -0.46 -0.51 -0.17 -0.50 -0.09 -0.04 -0.14 -0.13 -0.29 [205] -0.88 -0.33 -0.59 -0.19 -0.37 -0.51 -0.52 -0.23 -0.43 -0.14 -0.18 -0.02 [217] -0.09 -0.29 -0.46 -0.16 -0.45 -0.21 -0.16 -0.14 -0.27 -0.38 -0.25 -0.19 [229] -0.23 -0.07 -0.26 -0.11 -0.21 -0.17 -0.08 -0.25 -0.04 -0.09 -0.23 -0.33 [241] -0.34 -0.32 -0.62 -0.14 -0.37 -0.15 -0.31 -0.17 -0.30 -0.28 -0.32 -0.36 [253] -0.11 -0.17 -0.26 -0.20 -0.14 -0.55 -0.15 -0.21 -0.13 -0.51 -0.48 -0.18 [265] -0.24 -0.27 -1.23 -0.31 -0.18 -0.13 -0.25 -0.10 -0.38 -0.67 -0.16 -1.05 [277] -0.16 -0.42 -0.27 -0.21 -0.37 -0.33 -0.18 -0.47 -0.24 -0.37 -0.11 -0.46 [289] -0.51 -0.60 -0.20 -0.18 -0.26 -0.22 -0.12 -0.44 -0.07 -0.20 -0.51 -0.17 [301] -0.75 -0.16 -0.27 -0.12 -0.17 -0.29 -0.15 -0.09 -0.51 -0.11 -0.46 -0.13 [313] -0.12 -0.38 -0.31 -0.05 -0.12 -0.44 -0.22 -0.18 -0.08 -0.18 -0.17 -0.22 [325] -0.13 -0.24 -0.49 -0.33 -0.11 -0.13 -0.18 -0.20 -0.26 -0.10 -0.13 -0.68 [337] -0.06 -0.18 -0.30 -0.29 -0.28 -0.69 -0.22 -0.12 -0.15 -0.06 -0.07 -0.46 [349] -0.26 -0.21 -0.15 -0.10 -0.08 -0.07 -0.07 -0.31 -0.74 -0.16 -0.14 -0.09 [361] -0.24 -0.04 -0.14 -0.18 -0.11 -0.15 -0.08 -0.10 -0.48 -0.20 -0.43 -0.31 [373] -0.23 -0.14 -0.09 -0.14 -0.12 -0.35 -0.08 -0.18 -0.27 -0.14 -0.17 -0.27 [385] -0.65 -0.08 -0.45 -0.25 -0.18 -0.42 -0.09 -0.28 -0.54 -0.82 -0.57 -0.07 [397] -0.26 -0.21 -0.22 -0.41 -0.05 -0.07 -0.22 -0.34 -0.53 -0.09 -0.39 -0.18 [409] -0.40 -0.14 -0.46 -0.20 -0.31 -0.19 -0.09 -0.41 -0.12 -0.48 -0.19 -0.25 [421] -0.12 -0.24 -1.43 -0.07 -0.40 -0.10 -0.19 -0.17 -0.45 -0.25 -0.62 -0.13 [433] -0.15 -0.07 -0.65 -0.24 -0.43 -0.30 -0.30 -0.82 -0.52 -0.12 -0.24 -0.17 [445] -0.12 -0.12 -0.17 -0.26 -0.80 -0.46 -0.24 -4.04 -4.39 -3.52 -4.09 -4.32 [457] -3.83 -4.06 -4.36 -3.08 -3.08 -3.04 -4.07 -4.17 -3.90 -3.85 -3.57 -3.48 [469] -4.98 -3.57 -4.00 -4.47 -3.92 -6.09 -4.50 -3.54 -3.68 -3.10 -3.80 -3.24 [481] -3.85 -3.10 -4.49 -3.90 -3.96 -3.47 -3.37 -5.20 -3.82 -3.32 -3.06 -3.36 [493] -5.62 -3.64 -3.81 -6.02 -4.04 -4.61 -5.29 -4.01 -3.90 -4.28 -3.94 -3.62 [505] -4.64 -3.33 -3.46 -3.35 -3.22 -3.10 -4.23 -4.08 -3.21 -3.82 -3.21 -4.97 [517] -3.23 -3.47 -3.37 -3.03 -3.36 -3.22 -4.15 -4.10 -3.50 -4.39 -3.48 -4.26 [529] -3.21 -3.71 -4.48 -4.02 -3.74 -3.15 -4.40 -3.77 -3.90 -4.15 -3.37 -3.31 [541] -5.02 -4.43 -3.31 -3.90 -3.44 -3.71 -3.39 -3.86 -3.86 -3.86 -3.53 -5.76 [553] -5.38 -3.04 -4.11 -4.37 -3.58 -3.10 -3.28 -3.62 -3.03 -4.91 -3.04 -4.28 [565] -5.86 -3.91 -3.31 -7.93 -3.57 -3.47 -3.03 -6.08 -4.12 -4.21 -3.96 -3.14 [577] -3.21 -3.66 -3.74 -3.06 -3.19 -3.12 -3.68 -3.41 -3.30 -3.64 -4.99 -4.70 [589] -3.37 -3.21 -3.88 -3.87 -3.21 -4.83 -3.42 -3.42 -3.55 -3.75 -3.38 -3.05 [601] -3.33 > round( attr( logLikBhhh, "gradient" ), 2 ) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] -0.06 -2.09 -0.56 -0.17 -0.40 -0.23 -0.27 [2,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [3,] -0.10 -3.17 -1.49 -0.10 -0.10 -0.40 0.02 [4,] -0.02 -1.11 -0.29 -0.10 -0.12 -0.10 -0.23 [5,] -0.07 -1.44 -0.05 -0.13 -0.39 -0.20 -0.24 [6,] -0.03 -0.85 -0.04 -0.05 -0.13 -0.13 -0.26 [7,] -0.05 -1.18 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.06 -3.18 -0.84 -0.11 -0.22 -0.22 -0.28 [9,] -0.09 -3.02 -1.42 -0.38 -0.09 -0.19 -0.02 [10,] -0.02 -0.41 -0.03 -0.08 -0.08 -0.09 -0.22 [11,] -0.14 -5.15 -2.09 -0.28 -0.97 -0.28 0.58 [12,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [13,] -0.04 -1.82 -0.58 -0.19 -0.23 -0.16 -0.29 [14,] -0.05 -1.09 -0.07 -0.10 -0.25 -0.20 -0.29 [15,] -0.03 -0.88 -0.13 -0.13 -0.16 -0.13 -0.28 [16,] -0.08 -3.04 -1.23 -0.08 -0.41 -0.41 -0.13 [17,] -0.11 -3.91 -1.58 -0.21 -0.42 -0.32 0.10 [18,] -0.04 -0.81 -0.03 -0.11 -0.18 -0.15 -0.29 [19,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.05 -1.38 -0.51 -0.10 -0.05 -0.26 -0.29 [21,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [22,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [23,] -0.05 -1.42 -0.53 -0.21 -0.26 -0.21 -0.28 [24,] -0.04 -1.13 -0.35 -0.11 -0.04 -0.18 -0.29 [25,] -0.04 -1.60 -0.17 -0.09 -0.26 -0.17 -0.29 [26,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [27,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [28,] 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[49,] -0.01 -0.40 -0.02 -0.06 -0.04 -0.07 -0.19 [50,] -0.08 -3.46 -1.23 -0.16 -0.49 -0.33 -0.13 [51,] -0.01 -0.25 -0.01 -0.06 -0.03 -0.06 -0.16 [52,] -0.06 -1.95 -0.43 -0.12 -0.37 -0.24 -0.26 [53,] -0.02 -0.46 -0.07 -0.08 -0.10 -0.08 -0.21 [54,] -0.05 -1.48 -0.55 -0.22 -0.33 -0.22 -0.28 [55,] -0.06 -1.21 -0.22 -0.06 -0.28 -0.28 -0.28 [56,] -0.11 -4.23 -1.71 -0.46 -0.34 -0.11 0.21 [57,] -0.02 -0.46 -0.03 -0.11 -0.08 -0.08 -0.24 [58,] -0.04 -1.34 -0.54 -0.15 -0.04 -0.18 -0.29 [59,] -0.02 -0.65 -0.02 -0.10 -0.12 -0.10 -0.25 [60,] -0.05 -1.56 -0.49 -0.20 -0.29 -0.20 -0.29 [61,] -0.07 -3.45 -1.10 -0.37 -0.51 -0.15 -0.20 [62,] -0.05 -2.00 -0.54 -0.16 -0.33 -0.22 -0.28 [63,] -0.03 -0.73 -0.03 -0.07 -0.17 -0.17 -0.28 [64,] -0.04 -0.99 -0.15 -0.07 -0.15 -0.18 -0.29 [65,] -0.04 -1.23 -0.27 -0.15 -0.23 -0.15 -0.29 [66,] -0.06 -2.36 -0.84 -0.11 -0.17 -0.28 -0.27 [67,] -0.04 -1.59 -0.43 -0.17 -0.26 -0.17 -0.29 [68,] -0.05 -2.13 -0.68 -0.14 -0.27 -0.23 -0.29 [69,] -0.01 -0.32 -0.02 -0.07 -0.07 -0.07 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-0.14 -0.07 -0.27 -0.22 [438,] -0.05 -2.23 -0.80 -0.21 -0.27 -0.21 -0.28 [439,] -0.05 -1.42 -0.37 -0.21 -0.16 -0.16 -0.28 [440,] -0.11 -2.93 -0.76 -0.22 -0.65 -0.22 0.13 [441,] -0.08 -3.32 -1.19 -0.24 -0.24 -0.24 -0.16 [442,] -0.03 -0.71 -0.11 -0.08 -0.08 -0.13 -0.26 [443,] -0.04 -1.19 -0.31 -0.13 -0.04 -0.18 -0.29 [444,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [445,] -0.03 -0.70 -0.10 -0.10 -0.03 -0.10 -0.26 [446,] -0.03 -0.57 -0.10 -0.10 -0.13 -0.13 -0.26 [447,] -0.03 -0.75 -0.05 -0.07 -0.14 -0.17 -0.29 [448,] -0.05 -2.22 -0.71 -0.19 -0.24 -0.19 -0.29 [449,] -0.11 -3.97 -1.07 -0.21 -0.64 -0.21 0.12 [450,] -0.07 -2.70 -1.09 -0.22 -0.36 -0.29 -0.20 [451,] -0.04 -1.20 -0.18 -0.09 -0.04 -0.18 -0.29 [452,] 0.17 4.66 0.26 0.52 0.69 0.69 1.02 [453,] 0.20 5.40 0.80 0.60 0.20 1.00 1.72 [454,] 0.12 4.46 1.81 0.60 0.72 0.24 -0.01 [455,] 0.18 5.66 1.77 0.53 0.88 0.35 1.13 [456,] 0.19 4.28 0.02 0.78 0.97 0.97 1.58 [457,] 0.15 3.37 0.23 0.31 0.15 0.77 0.59 [458,] 0.17 6.44 2.61 0.70 0.87 0.35 1.06 [459,] 0.20 4.36 0.30 0.40 0.59 0.79 1.67 [460,] 0.04 1.47 0.60 0.08 0.24 0.16 -0.89 [461,] 0.04 1.22 0.57 0.15 0.11 0.08 -0.90 [462,] 0.02 0.63 0.26 0.07 0.07 0.03 -0.98 [463,] 0.18 7.36 2.63 0.53 0.18 0.70 1.09 [464,] 0.18 7.70 2.75 0.92 0.73 0.18 1.28 [465,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [466,] 0.16 4.97 2.33 0.47 0.16 0.31 0.64 [467,] 0.13 3.42 0.51 0.13 0.76 0.63 0.09 [468,] 0.12 4.28 1.16 0.23 0.81 0.35 -0.09 [469,] 0.24 6.47 0.96 0.72 1.20 1.20 2.91 [470,] 0.13 5.30 1.89 0.50 0.63 0.63 0.08 [471,] 0.17 7.94 2.54 0.85 0.68 0.85 0.94 [472,] 0.21 5.57 0.82 0.62 1.03 0.82 1.89 [473,] 0.16 4.38 1.14 0.81 0.16 0.65 0.79 [474,] 0.30 8.10 0.45 0.90 1.50 1.20 5.12 [475,] 0.21 5.62 1.46 0.83 1.25 0.42 1.94 [476,] 0.12 5.13 1.83 0.49 0.61 0.49 0.01 [477,] 0.14 3.75 1.39 0.56 0.97 0.42 0.31 [478,] 0.05 1.25 0.07 0.09 0.23 0.09 -0.85 [479,] 0.15 4.84 0.60 0.60 0.91 0.60 0.55 [480,] 0.08 2.13 0.55 0.24 0.08 0.24 -0.57 [481,] 0.16 4.98 1.56 0.62 0.16 0.62 0.65 [482,] 0.05 1.24 0.18 0.09 0.32 0.09 -0.86 [483,] 0.21 3.63 0.16 1.04 0.83 1.04 1.92 [484,] 0.16 5.11 1.60 0.64 0.16 0.80 0.74 [485,] 0.17 5.29 1.16 0.33 0.99 0.66 0.86 [486,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [487,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [488,] 0.25 8.09 2.53 0.51 1.26 1.26 3.35 [489,] 0.15 7.95 2.29 0.31 0.92 0.61 0.59 [490,] 0.09 3.87 1.38 0.09 0.09 0.28 -0.42 [491,] 0.03 1.61 0.47 0.06 0.19 0.09 -0.93 [492,] 0.10 3.63 1.47 0.29 0.59 0.49 -0.34 [493,] 0.28 6.07 1.10 0.83 0.83 1.10 4.18 [494,] 0.13 3.61 0.94 0.13 0.80 0.27 0.22 [495,] 0.15 4.09 0.61 0.45 0.76 0.76 0.56 [496,] 0.30 13.93 4.45 1.19 1.78 1.48 4.97 [497,] 0.17 7.26 2.59 0.69 0.17 0.17 1.03 [498,] 0.22 5.83 0.86 0.65 0.65 0.86 2.17 [499,] 0.26 8.26 1.81 1.03 1.03 1.29 3.53 [500,] 0.17 5.44 0.07 0.51 0.51 0.68 0.96 [501,] 0.16 7.52 2.40 0.80 0.80 0.64 0.74 [502,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [503,] 0.16 3.59 0.65 0.33 0.98 0.65 0.82 [504,] 0.13 3.55 0.53 0.26 0.53 0.66 0.18 [505,] 0.22 11.32 3.26 1.09 0.22 0.65 2.22 [506,] 0.09 2.54 0.38 0.28 0.28 0.28 -0.40 [507,] 0.11 3.05 1.13 0.45 0.11 0.45 -0.13 [508,] 0.10 3.11 0.68 0.29 0.68 0.39 -0.36 [509,] 0.07 2.37 0.52 0.15 0.30 0.07 -0.63 [510,] 0.05 1.04 0.07 0.05 0.14 0.09 -0.85 [511,] 0.19 4.14 0.75 0.56 1.13 0.75 1.41 [512,] 0.18 7.40 2.64 0.70 1.06 0.70 1.11 [513,] 0.07 4.13 1.09 0.07 0.36 0.29 -0.64 [514,] 0.15 4.87 0.61 0.46 0.76 0.30 0.57 [515,] 0.07 1.97 0.29 0.07 0.29 0.29 -0.64 [516,] 0.24 7.64 1.67 0.96 0.24 0.96 2.88 [517,] 0.08 4.41 1.16 0.08 0.31 0.31 -0.59 [518,] 0.11 4.77 1.71 0.45 0.57 0.23 -0.12 [519,] 0.10 3.72 1.00 0.10 0.50 0.30 -0.31 [520,] -0.01 -0.35 -0.12 -0.02 -0.05 -0.01 -1.00 [521,] 0.10 5.15 1.48 0.30 0.40 0.40 -0.33 [522,] 0.07 2.01 0.52 0.22 0.37 0.22 -0.62 [523,] 0.18 5.80 1.27 0.36 0.72 0.36 1.23 [524,] 0.18 3.91 0.71 0.71 0.36 0.89 1.14 [525,] 0.12 3.19 0.83 0.35 0.71 0.47 -0.05 [526,] 0.20 7.42 3.01 0.20 1.00 1.00 1.73 [527,] 0.12 3.69 1.73 0.35 0.12 0.35 -0.10 [528,] 0.19 5.14 1.33 0.38 0.95 0.95 1.47 [529,] 0.07 2.34 0.51 0.22 0.37 0.22 -0.64 [530,] 0.14 4.51 0.21 0.28 0.28 0.56 0.35 [531,] 0.21 8.67 3.10 0.83 0.21 0.41 1.90 [532,] 0.17 5.45 1.70 0.51 0.85 0.68 0.98 [533,] 0.14 5.35 0.58 0.14 0.87 0.43 0.42 [534,] 0.06 1.61 0.24 0.12 0.30 0.18 -0.76 [535,] 0.20 8.43 3.01 0.60 0.80 0.60 1.74 [536,] 0.15 3.98 1.47 0.74 0.88 0.74 0.48 [537,] 0.16 5.93 1.60 0.32 0.96 0.32 0.75 [538,] 0.18 4.90 1.27 0.18 0.54 0.54 1.24 [539,] 0.10 2.69 0.70 0.40 0.10 0.20 -0.32 [540,] 0.09 2.93 0.92 0.18 0.37 0.37 -0.43 [541,] 0.24 4.23 0.18 0.48 0.24 0.73 2.98 [542,] 0.20 6.50 3.05 0.61 1.02 0.81 1.81 [543,] 0.09 2.00 0.64 0.36 0.36 0.27 -0.44 [544,] 0.16 5.13 1.12 0.64 0.96 0.80 0.75 [545,] 0.11 2.95 0.44 0.22 0.66 0.22 -0.19 [546,] 0.14 3.10 0.21 0.71 0.71 0.42 0.35 [547,] 0.10 3.28 1.54 0.31 0.51 0.10 -0.29 [548,] 0.16 6.59 2.35 0.31 0.16 0.31 0.67 [549,] 0.16 6.56 2.34 0.47 0.78 0.62 0.66 [550,] 0.16 5.02 1.57 0.31 0.63 0.31 0.67 [551,] 0.12 3.89 1.82 0.36 0.12 0.12 0.01 [552,] 0.28 16.17 4.25 1.42 1.13 1.42 4.47 [553,] 0.26 12.35 3.94 1.05 1.58 1.05 3.70 [554,] 0.02 0.81 0.29 0.04 0.12 0.06 -0.97 [555,] 0.18 6.59 2.67 0.53 1.07 0.53 1.16 [556,] 0.20 7.36 2.98 0.99 0.99 0.40 1.69 [557,] 0.13 3.45 1.28 0.26 0.77 0.51 0.11 [558,] 0.04 1.65 0.67 0.09 0.22 0.18 -0.86 [559,] 0.09 2.77 1.30 0.09 0.43 0.17 -0.49 [560,] 0.13 4.23 1.32 0.40 0.79 0.40 0.19 [561,] -0.01 -0.24 -0.10 -0.03 -0.03 -0.01 -1.00 [562,] 0.24 6.35 0.35 0.47 1.18 1.18 2.77 [563,] 0.02 0.87 0.28 0.04 0.09 0.04 -0.98 [564,] 0.19 7.09 2.87 0.38 0.96 0.77 1.50 [565,] 0.29 7.79 1.15 0.58 1.44 1.44 4.66 [566,] 0.16 4.36 1.61 0.65 0.16 0.81 0.77 [567,] 0.09 1.99 0.36 0.27 0.09 0.27 -0.45 [568,] 0.38 19.74 2.66 1.52 1.90 1.90 8.80 [569,] 0.13 3.41 0.50 0.13 0.38 0.63 0.08 [570,] 0.11 4.19 1.70 0.23 0.68 0.45 -0.13 [571,] -0.01 -0.22 -0.03 -0.01 -0.02 -0.01 -1.00 [572,] 0.30 5.24 0.22 0.60 0.90 1.50 5.10 [573,] 0.18 5.74 2.69 0.90 0.90 0.72 1.19 [574,] 0.19 4.10 0.75 0.19 0.56 0.93 1.37 [575,] 0.17 5.31 0.66 0.66 0.99 0.66 0.87 [576,] 0.06 1.27 0.09 0.17 0.29 0.12 -0.77 [577,] 0.07 3.04 1.09 0.14 0.36 0.29 -0.64 [578,] 0.14 4.36 0.95 0.55 0.55 0.55 0.26 [579,] 0.14 5.34 2.17 0.43 0.87 0.29 0.42 [580,] 0.03 1.24 0.44 0.09 0.18 0.09 -0.94 [581,] 0.07 1.85 0.27 0.07 0.34 0.27 -0.68 [582,] 0.05 1.89 0.76 0.20 0.36 0.15 -0.82 [583,] 0.14 5.11 2.07 0.41 0.83 0.55 0.30 [584,] 0.11 2.33 0.16 0.21 0.32 0.32 -0.24 [585,] 0.09 2.83 0.35 0.27 0.53 0.18 -0.47 [586,] 0.13 4.31 2.02 0.67 0.81 0.67 0.23 [587,] 0.24 12.48 3.60 0.24 1.20 1.20 2.92 [588,] 0.22 10.43 3.33 0.22 1.33 1.11 2.35 [589,] 0.10 3.22 1.51 0.40 0.40 0.40 -0.31 [590,] 0.07 2.31 1.08 0.22 0.22 0.14 -0.65 [591,] 0.16 4.28 1.11 0.63 0.16 0.32 0.71 [592,] 0.16 6.62 2.36 0.47 0.95 0.32 0.69 [593,] 0.07 3.09 1.11 0.15 0.22 0.15 -0.63 [594,] 0.23 6.22 1.61 0.46 1.15 0.92 2.61 [595,] 0.11 3.44 1.08 0.43 0.43 0.32 -0.21 [596,] 0.11 5.02 1.60 0.32 0.43 0.21 -0.22 [597,] 0.12 2.72 0.19 0.12 0.25 0.62 0.04 [598,] 0.15 4.66 1.46 0.29 0.73 0.58 0.44 [599,] 0.10 3.23 1.01 0.20 0.61 0.50 -0.31 [600,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 [601,] 0.09 3.02 1.41 0.28 0.09 0.47 -0.40 > all.equal( sum( logLikBhhh ), c( logLik( estResultBhhh ) ) ) [1] TRUE > logLikStart <- censReg( affairsFormula, data = Affairs, + start = c( 8.17, -0.18, 0.55, -1.69, 0.33, -2.3, 2.13 ), + logLikOnly = TRUE ) > round( c( logLikStart ), 2 ) [1] -0.33 -0.17 -0.70 -0.08 -0.40 -0.12 -0.31 -0.32 -0.66 -0.08 -1.16 -0.17 [13] -0.20 -0.28 -0.16 -0.54 -0.77 -0.19 -0.18 -0.28 -0.18 -0.18 -0.30 -0.18 [25] -0.23 -0.18 -0.11 -0.23 -0.14 -0.22 -0.46 -0.13 -0.11 -0.22 -0.36 -0.23 [37] -0.32 -0.12 -0.22 -0.09 -0.12 -0.26 -0.06 -0.88 -0.21 -0.17 -0.15 -0.52 [49] -0.06 -0.54 -0.05 -0.36 -0.07 -0.31 -0.32 -0.87 -0.09 -0.18 -0.11 -0.27 [61] -0.46 -0.31 -0.17 -0.19 -0.20 -0.32 -0.23 -0.24 -0.06 -0.25 -0.14 -0.11 [73] -0.16 -0.36 -0.61 -0.14 -0.13 -0.15 -0.13 -0.74 -0.16 -0.12 -0.23 -0.13 [85] -0.06 -0.35 -0.27 -0.39 -0.10 -0.37 -0.36 -0.55 -0.21 -0.20 -0.25 -0.11 [97] -0.77 -0.53 -0.31 -0.25 -0.10 -0.41 -0.07 -0.08 -0.37 -0.20 -0.22 -0.18 [109] -0.76 -0.28 -0.09 -0.25 -0.04 -0.15 -0.30 -0.25 -0.64 -0.20 -0.06 -0.18 [121] -0.22 -0.22 -0.44 -0.65 -0.13 -0.18 -0.15 -0.07 -0.13 -0.16 -0.35 -0.21 [133] -0.16 -0.46 -0.49 -0.52 -0.18 -0.48 -0.10 -0.40 -0.42 -0.32 -0.25 -0.39 [145] -0.35 -0.30 -0.16 -0.19 -0.19 -0.51 -0.26 -0.36 -0.38 -0.53 -0.36 -0.46 [157] -0.42 -0.58 -0.33 -0.17 -0.21 -0.20 -0.18 -0.19 -0.27 -0.23 -0.68 -0.15 [169] -0.36 -0.35 -0.12 -0.07 -0.12 -0.40 -0.16 -0.39 -0.22 -0.42 -0.22 -0.35 [181] -0.50 -0.06 -0.29 -0.09 -0.10 -0.11 -0.10 -0.20 -0.15 -0.61 -0.11 -0.17 [193] -0.11 -0.35 -0.68 -0.46 -0.50 -0.17 -0.50 -0.09 -0.05 -0.15 -0.14 -0.29 [205] -0.87 -0.33 -0.58 -0.19 -0.37 -0.50 -0.51 -0.23 -0.42 -0.14 -0.19 -0.03 [217] -0.10 -0.29 -0.46 -0.16 -0.45 -0.21 -0.16 -0.15 -0.27 -0.37 -0.25 -0.20 [229] -0.23 -0.07 -0.26 -0.12 -0.21 -0.17 -0.09 -0.25 -0.04 -0.10 -0.23 -0.33 [241] -0.34 -0.32 -0.61 -0.14 -0.36 -0.15 -0.31 -0.17 -0.29 -0.28 -0.32 -0.36 [253] -0.12 -0.17 -0.26 -0.21 -0.14 -0.54 -0.15 -0.21 -0.13 -0.50 -0.47 -0.18 [265] -0.24 -0.26 -1.20 -0.31 -0.18 -0.13 -0.25 -0.11 -0.38 -0.66 -0.16 -1.04 [277] -0.16 -0.42 -0.27 -0.21 -0.37 -0.32 -0.19 -0.46 -0.24 -0.37 -0.11 -0.46 [289] -0.51 -0.59 -0.20 -0.18 -0.26 -0.23 -0.12 -0.44 -0.08 -0.20 -0.50 -0.18 [301] -0.74 -0.17 -0.27 -0.12 -0.17 -0.30 -0.15 -0.09 -0.50 -0.11 -0.46 -0.13 [313] -0.12 -0.38 -0.31 -0.05 -0.13 -0.43 -0.22 -0.18 -0.08 -0.18 -0.17 -0.22 [325] -0.13 -0.24 -0.48 -0.32 -0.11 -0.13 -0.19 -0.20 -0.26 -0.10 -0.13 -0.67 [337] -0.06 -0.18 -0.29 -0.28 -0.28 -0.68 -0.22 -0.13 -0.16 -0.06 -0.08 -0.46 [349] -0.26 -0.22 -0.15 -0.11 -0.08 -0.07 -0.07 -0.31 -0.73 -0.16 -0.14 -0.10 [361] -0.24 -0.05 -0.14 -0.18 -0.11 -0.15 -0.09 -0.10 -0.47 -0.21 -0.43 -0.31 [373] -0.23 -0.15 -0.09 -0.15 -0.13 -0.34 -0.09 -0.18 -0.27 -0.14 -0.17 -0.27 [385] -0.65 -0.08 -0.45 -0.26 -0.18 -0.42 -0.09 -0.28 -0.53 -0.81 -0.57 -0.08 [397] -0.26 -0.22 -0.22 -0.41 -0.05 -0.07 -0.22 -0.34 -0.53 -0.09 -0.39 -0.18 [409] -0.40 -0.14 -0.46 -0.20 -0.31 -0.19 -0.10 -0.41 -0.12 -0.48 -0.20 -0.25 [421] -0.12 -0.24 -1.39 -0.07 -0.40 -0.10 -0.19 -0.17 -0.44 -0.25 -0.61 -0.13 [433] -0.15 -0.07 -0.65 -0.24 -0.42 -0.30 -0.30 -0.81 -0.51 -0.12 -0.24 -0.17 [445] -0.12 -0.12 -0.17 -0.26 -0.79 -0.46 -0.24 -4.03 -4.38 -3.54 -4.08 -4.30 [457] -3.83 -4.06 -4.35 -3.11 -3.10 -3.06 -4.08 -4.16 -3.90 -3.85 -3.58 -3.50 [469] -4.95 -3.59 -4.01 -4.45 -3.93 -6.01 -4.48 -3.55 -3.69 -3.12 -3.81 -3.26 [481] -3.86 -3.12 -4.47 -3.91 -3.96 -3.48 -3.39 -5.17 -3.83 -3.34 -3.08 -3.38 [493] -5.56 -3.64 -3.81 -5.96 -4.04 -4.59 -5.26 -4.01 -3.91 -4.27 -3.93 -3.63 [505] -4.63 -3.34 -3.48 -3.37 -3.23 -3.12 -4.22 -4.09 -3.23 -3.81 -3.23 -4.94 [517] -3.26 -3.48 -3.39 -3.05 -3.38 -3.24 -4.13 -4.10 -3.52 -4.39 -3.50 -4.26 [529] -3.23 -3.71 -4.47 -4.02 -3.74 -3.17 -4.39 -3.78 -3.90 -4.14 -3.38 -3.33 [541] -4.98 -4.43 -3.33 -3.91 -3.45 -3.71 -3.40 -3.87 -3.87 -3.86 -3.54 -5.73 [553] -5.34 -3.06 -4.11 -4.36 -3.59 -3.12 -3.30 -3.63 -3.05 -4.88 -3.06 -4.27 [565] -5.79 -3.92 -3.32 -7.80 -3.58 -3.48 -3.05 -6.00 -4.12 -4.20 -3.96 -3.16 [577] -3.23 -3.67 -3.75 -3.08 -3.21 -3.14 -3.69 -3.42 -3.31 -3.66 -4.97 -4.69 [589] -3.39 -3.23 -3.88 -3.88 -3.23 -4.80 -3.44 -3.43 -3.56 -3.76 -3.39 -3.07 [601] -3.35 > round( attr( logLikStart, "gradient" ), 2 ) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] -0.06 -2.04 -0.55 -0.17 -0.39 -0.22 -0.27 [2,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [3,] -0.10 -3.06 -1.44 -0.10 -0.10 -0.38 0.01 [4,] -0.02 -1.10 -0.29 -0.10 -0.12 -0.10 -0.23 [5,] -0.06 -1.42 -0.05 -0.13 -0.39 -0.19 -0.24 [6,] -0.03 -0.85 -0.04 -0.05 -0.13 -0.13 -0.27 [7,] -0.05 -1.16 -0.04 -0.11 -0.05 -0.16 -0.28 [8,] -0.05 -3.09 -0.81 -0.11 -0.22 -0.22 -0.28 [9,] -0.09 -2.93 -1.37 -0.37 -0.09 -0.18 -0.03 [10,] -0.02 -0.42 -0.03 -0.08 -0.08 -0.09 -0.23 [11,] -0.13 -4.97 -2.02 -0.27 -0.94 -0.27 0.55 [12,] -0.03 -0.92 -0.14 -0.14 -0.20 -0.14 -0.29 [13,] -0.04 -1.79 -0.57 -0.19 -0.23 -0.15 -0.29 [14,] -0.05 -1.08 -0.07 -0.10 -0.24 -0.20 -0.29 [15,] -0.03 -0.87 -0.13 -0.13 -0.16 -0.13 -0.28 [16,] -0.08 -2.94 -1.19 -0.08 -0.40 -0.40 -0.14 [17,] -0.10 -3.78 -1.53 -0.20 -0.41 -0.31 0.08 [18,] -0.04 -0.80 -0.03 -0.11 -0.18 -0.15 -0.29 [19,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [20,] -0.05 -1.35 -0.50 -0.10 -0.05 -0.25 -0.29 [21,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [22,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [23,] -0.05 -1.39 -0.52 -0.21 -0.26 -0.21 -0.28 [24,] -0.03 -1.11 -0.35 -0.10 -0.03 -0.17 -0.29 [25,] -0.04 -1.58 -0.17 -0.09 -0.26 -0.17 -0.29 [26,] -0.04 -0.78 -0.05 -0.07 -0.18 -0.18 -0.29 [27,] -0.02 -0.63 -0.16 -0.09 -0.02 -0.12 -0.25 [28,] -0.04 -1.81 -0.65 -0.22 -0.26 -0.17 -0.29 [29,] -0.03 -0.79 -0.12 -0.09 -0.15 -0.15 -0.27 [30,] -0.04 -1.12 -0.17 -0.12 -0.21 -0.17 -0.29 [31,] -0.07 -2.98 -1.07 -0.28 -0.43 -0.21 -0.20 [32,] -0.03 -0.59 -0.04 -0.08 -0.14 -0.14 -0.27 [33,] -0.02 -0.66 -0.01 -0.10 -0.15 -0.10 -0.26 [34,] -0.04 -1.73 -0.62 -0.21 -0.21 -0.16 -0.29 [35,] -0.06 -1.91 -0.24 -0.06 -0.36 -0.24 -0.26 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-0.09 -0.11 -0.40 -0.17 -0.27 [405,] -0.08 -4.47 -1.18 -0.31 -0.24 -0.08 -0.15 [406,] -0.02 -0.79 -0.15 -0.09 -0.11 -0.11 -0.24 [407,] -0.06 -3.28 -0.94 -0.13 -0.31 -0.25 -0.24 [408,] -0.03 -1.63 -0.52 -0.14 -0.21 -0.17 -0.29 [409,] -0.06 -1.72 -0.45 -0.13 -0.32 -0.25 -0.24 [410,] -0.03 -0.78 -0.20 -0.12 -0.15 -0.15 -0.27 [411,] -0.07 -1.55 -0.28 -0.14 -0.21 -0.21 -0.20 [412,] -0.04 -1.44 -0.27 -0.08 -0.23 -0.19 -0.29 [413,] -0.05 -1.45 -0.38 -0.21 -0.21 -0.16 -0.28 [414,] -0.04 -1.56 -0.37 -0.15 -0.22 -0.15 -0.29 [415,] -0.02 -0.47 -0.03 -0.06 -0.02 -0.11 -0.24 [416,] -0.07 -1.44 -0.26 -0.13 -0.07 -0.20 -0.23 [417,] -0.03 -1.48 -0.39 -0.10 -0.16 -0.13 -0.26 [418,] -0.07 -2.70 -1.09 -0.29 -0.29 -0.22 -0.19 [419,] -0.04 -1.02 -0.26 -0.11 -0.19 -0.19 -0.29 [420,] -0.05 -0.80 -0.46 -0.18 -0.18 -0.23 -0.29 [421,] -0.03 -0.56 -0.10 -0.10 -0.13 -0.13 -0.26 [422,] -0.04 -1.18 -0.17 -0.09 -0.04 -0.17 -0.29 [423,] -0.15 -5.61 -2.27 -0.30 -0.76 -0.15 0.87 [424,] -0.02 -0.39 -0.03 -0.09 -0.02 -0.07 -0.22 [425,] -0.06 -1.72 -0.45 -0.13 -0.32 -0.25 -0.24 [426,] -0.02 -0.59 -0.09 -0.09 -0.11 -0.11 -0.24 [427,] -0.04 -0.81 0.00 -0.04 -0.11 -0.18 -0.29 [428,] -0.03 -0.92 -0.24 -0.14 -0.03 -0.14 -0.29 [429,] -0.07 -2.22 -1.04 -0.35 -0.35 -0.21 -0.21 [430,] -0.05 -1.46 -0.46 -0.18 -0.23 -0.18 -0.29 [431,] -0.09 -2.77 -1.30 -0.17 -0.26 -0.35 -0.08 [432,] -0.03 -0.59 -0.04 -0.08 -0.14 -0.14 -0.27 [433,] -0.03 -0.83 -0.12 -0.12 -0.12 -0.12 -0.28 [434,] -0.02 -0.87 -0.25 -0.08 -0.02 -0.08 -0.21 [435,] -0.09 -2.44 -0.63 -0.18 -0.09 -0.18 -0.04 [436,] -0.04 -1.17 -0.30 -0.13 -0.04 -0.17 -0.29 [437,] -0.07 -2.80 -1.00 -0.13 -0.07 -0.27 -0.23 [438,] -0.05 -2.18 -0.78 -0.21 -0.26 -0.21 -0.28 [439,] -0.05 -1.39 -0.36 -0.21 -0.15 -0.15 -0.28 [440,] -0.11 -2.85 -0.74 -0.21 -0.63 -0.21 0.12 [441,] -0.08 -3.22 -1.15 -0.23 -0.23 -0.23 -0.16 [442,] -0.03 -0.71 -0.10 -0.08 -0.08 -0.13 -0.26 [443,] -0.04 -1.17 -0.30 -0.13 -0.04 -0.17 -0.29 [444,] -0.03 -0.74 -0.05 -0.07 -0.13 -0.17 -0.29 [445,] -0.03 -0.70 -0.10 -0.10 -0.03 -0.10 -0.26 [446,] -0.03 -0.56 -0.10 -0.10 -0.13 -0.13 -0.26 [447,] -0.03 -0.74 -0.05 -0.07 -0.13 -0.17 -0.29 [448,] -0.05 -2.16 -0.69 -0.18 -0.23 -0.18 -0.29 [449,] -0.10 -3.85 -1.04 -0.21 -0.62 -0.21 0.11 [450,] -0.07 -2.62 -1.06 -0.21 -0.35 -0.28 -0.20 [451,] -0.04 -1.18 -0.17 -0.09 -0.04 -0.17 -0.29 [452,] 0.17 4.51 0.25 0.50 0.67 0.67 0.97 [453,] 0.19 5.24 0.78 0.58 0.19 0.97 1.66 [454,] 0.12 4.34 1.76 0.59 0.70 0.23 -0.02 [455,] 0.17 5.47 1.71 0.51 0.86 0.34 1.07 [456,] 0.19 4.14 0.02 0.75 0.94 0.94 1.51 [457,] 0.15 3.27 0.22 0.30 0.15 0.74 0.56 [458,] 0.17 6.24 2.53 0.68 0.84 0.34 1.02 [459,] 0.19 4.21 0.29 0.38 0.57 0.77 1.59 [460,] 0.04 1.48 0.60 0.08 0.24 0.16 -0.89 [461,] 0.04 1.22 0.57 0.15 0.11 0.08 -0.90 [462,] 0.02 0.67 0.27 0.07 0.07 0.04 -0.98 [463,] 0.17 7.17 2.56 0.51 0.17 0.68 1.06 [464,] 0.18 7.46 2.66 0.89 0.71 0.18 1.23 [465,] 0.16 5.74 1.55 0.31 0.93 0.31 0.71 [466,] 0.15 4.83 2.26 0.45 0.15 0.30 0.61 [467,] 0.12 3.32 0.49 0.12 0.74 0.61 0.07 [468,] 0.11 4.16 1.12 0.22 0.79 0.34 -0.11 [469,] 0.23 6.26 0.93 0.70 1.16 1.16 2.80 [470,] 0.12 5.19 1.85 0.49 0.62 0.62 0.08 [471,] 0.16 7.75 2.47 0.82 0.66 0.82 0.92 [472,] 0.20 5.38 0.80 0.60 1.00 0.80 1.81 [473,] 0.16 4.26 1.10 0.79 0.16 0.63 0.76 [474,] 0.29 7.81 0.43 0.87 1.45 1.16 4.93 [475,] 0.20 5.42 1.41 0.80 1.20 0.40 1.86 [476,] 0.12 5.01 1.79 0.48 0.60 0.48 0.01 [477,] 0.13 3.64 1.35 0.54 0.94 0.40 0.29 [478,] 0.05 1.22 0.07 0.09 0.23 0.09 -0.86 [479,] 0.15 4.69 0.59 0.59 0.88 0.59 0.52 [480,] 0.08 2.09 0.54 0.23 0.08 0.23 -0.58 [481,] 0.15 4.84 1.51 0.61 0.15 0.61 0.62 [482,] 0.04 1.20 0.18 0.09 0.31 0.09 -0.86 [483,] 0.20 3.51 0.15 1.00 0.80 1.00 1.85 [484,] 0.16 4.98 1.56 0.62 0.16 0.78 0.72 [485,] 0.16 5.12 1.12 0.32 0.96 0.64 0.82 [486,] 0.11 4.10 1.66 0.22 0.66 0.44 -0.13 [487,] 0.10 3.62 0.98 0.10 0.49 0.29 -0.32 [488,] 0.24 7.83 2.45 0.49 1.22 1.22 3.24 [489,] 0.15 7.74 2.23 0.30 0.89 0.60 0.57 [490,] 0.09 3.80 1.36 0.09 0.09 0.27 -0.42 [491,] 0.03 1.64 0.47 0.06 0.19 0.09 -0.93 [492,] 0.10 3.57 1.45 0.29 0.58 0.48 -0.34 [493,] 0.27 5.86 1.07 0.80 0.80 1.07 4.03 [494,] 0.13 3.49 0.90 0.13 0.78 0.26 0.18 [495,] 0.15 3.97 0.59 0.44 0.74 0.74 0.53 [496,] 0.29 13.49 4.30 1.15 1.72 1.43 4.83 [497,] 0.17 7.04 2.51 0.67 0.17 0.17 0.99 [498,] 0.21 5.63 0.83 0.63 0.63 0.83 2.08 [499,] 0.25 7.99 1.75 1.00 1.00 1.25 3.41 [500,] 0.16 5.26 0.07 0.49 0.49 0.66 0.91 [501,] 0.16 7.33 2.34 0.78 0.78 0.62 0.72 [502,] 0.19 6.88 2.79 0.37 0.93 0.74 1.45 [503,] 0.16 3.48 0.63 0.32 0.95 0.63 0.77 [504,] 0.13 3.45 0.51 0.26 0.51 0.64 0.16 [505,] 0.21 10.99 3.17 1.06 0.21 0.63 2.16 [506,] 0.09 2.47 0.37 0.27 0.27 0.27 -0.41 [507,] 0.11 2.98 1.10 0.44 0.11 0.44 -0.14 [508,] 0.09 3.03 0.66 0.28 0.66 0.38 -0.37 [509,] 0.07 2.30 0.50 0.14 0.29 0.07 -0.63 [510,] 0.05 1.01 0.07 0.05 0.14 0.09 -0.85 [511,] 0.18 4.00 0.73 0.55 1.09 0.73 1.34 [512,] 0.17 7.19 2.57 0.68 1.03 0.68 1.07 [513,] 0.07 4.09 1.08 0.07 0.36 0.29 -0.64 [514,] 0.15 4.70 0.59 0.44 0.74 0.29 0.53 [515,] 0.07 1.93 0.29 0.07 0.29 0.29 -0.64 [516,] 0.23 7.40 1.62 0.92 0.23 0.92 2.78 [517,] 0.08 4.35 1.15 0.08 0.31 0.31 -0.59 [518,] 0.11 4.66 1.66 0.44 0.55 0.22 -0.13 [519,] 0.10 3.62 0.98 0.10 0.49 0.29 -0.32 [520,] -0.01 -0.28 -0.10 -0.02 -0.04 -0.01 -1.00 [521,] 0.10 5.06 1.46 0.29 0.39 0.39 -0.33 [522,] 0.07 1.97 0.51 0.22 0.36 0.22 -0.62 [523,] 0.18 5.60 1.23 0.35 0.70 0.35 1.17 [524,] 0.17 3.79 0.69 0.69 0.34 0.86 1.10 [525,] 0.11 3.10 0.80 0.34 0.69 0.46 -0.07 [526,] 0.19 7.20 2.92 0.19 0.97 0.97 1.68 [527,] 0.11 3.61 1.69 0.34 0.11 0.34 -0.10 [528,] 0.18 4.98 1.29 0.37 0.92 0.92 1.41 [529,] 0.07 2.29 0.50 0.21 0.36 0.21 -0.64 [530,] 0.14 4.38 0.21 0.27 0.27 0.55 0.32 [531,] 0.20 8.40 3.00 0.80 0.20 0.40 1.84 [532,] 0.17 5.29 1.65 0.50 0.83 0.66 0.94 [533,] 0.14 5.17 0.56 0.14 0.84 0.42 0.38 [534,] 0.06 1.57 0.23 0.12 0.29 0.17 -0.76 [535,] 0.19 8.18 2.92 0.58 0.78 0.58 1.69 [536,] 0.14 3.87 1.43 0.72 0.86 0.72 0.46 [537,] 0.16 5.74 1.55 0.31 0.93 0.31 0.71 [538,] 0.18 4.74 1.23 0.18 0.53 0.53 1.19 [539,] 0.10 2.62 0.68 0.39 0.10 0.19 -0.33 [540,] 0.09 2.87 0.90 0.18 0.36 0.36 -0.43 [541,] 0.23 4.08 0.17 0.47 0.23 0.70 2.85 [542,] 0.20 6.31 2.96 0.59 0.99 0.79 1.75 [543,] 0.09 1.95 0.62 0.35 0.35 0.27 -0.44 [544,] 0.16 4.98 1.09 0.62 0.93 0.78 0.72 [545,] 0.11 2.86 0.42 0.21 0.63 0.21 -0.21 [546,] 0.14 3.00 0.20 0.68 0.68 0.41 0.32 [547,] 0.10 3.19 1.50 0.30 0.50 0.10 -0.30 [548,] 0.15 6.40 2.29 0.30 0.15 0.30 0.64 [549,] 0.15 6.38 2.28 0.46 0.76 0.61 0.64 [550,] 0.15 4.86 1.52 0.30 0.61 0.30 0.63 [551,] 0.12 3.79 1.78 0.36 0.12 0.12 -0.01 [552,] 0.27 15.67 4.12 1.37 1.10 1.37 4.35 [553,] 0.25 11.96 3.82 1.02 1.53 1.02 3.59 [554,] 0.02 0.85 0.31 0.04 0.12 0.06 -0.97 [555,] 0.17 6.39 2.59 0.52 1.04 0.52 1.11 [556,] 0.19 7.13 2.89 0.96 0.96 0.39 1.63 [557,] 0.12 3.35 1.24 0.25 0.74 0.50 0.09 [558,] 0.04 1.66 0.67 0.09 0.22 0.18 -0.86 [559,] 0.08 2.70 1.27 0.08 0.42 0.17 -0.49 [560,] 0.13 4.10 1.28 0.38 0.77 0.38 0.16 [561,] 0.00 -0.18 -0.07 -0.02 -0.02 0.00 -1.00 [562,] 0.23 6.14 0.34 0.45 1.14 1.14 2.66 [563,] 0.02 0.91 0.29 0.04 0.10 0.04 -0.97 [564,] 0.19 6.88 2.79 0.37 0.93 0.74 1.45 [565,] 0.28 7.52 1.11 0.56 1.39 1.39 4.49 [566,] 0.16 4.24 1.57 0.63 0.16 0.79 0.75 [567,] 0.09 1.94 0.35 0.26 0.09 0.26 -0.45 [568,] 0.37 19.06 2.57 1.47 1.83 1.83 8.51 [569,] 0.12 3.31 0.49 0.12 0.37 0.61 0.07 [570,] 0.11 4.10 1.66 0.22 0.66 0.44 -0.13 [571,] -0.01 -0.19 -0.03 -0.01 -0.02 -0.01 -1.00 [572,] 0.29 5.06 0.22 0.58 0.87 1.44 4.91 [573,] 0.17 5.58 2.61 0.87 0.87 0.70 1.15 [574,] 0.18 3.97 0.72 0.18 0.54 0.90 1.31 [575,] 0.16 5.14 0.64 0.64 0.96 0.64 0.83 [576,] 0.06 1.24 0.08 0.17 0.28 0.11 -0.78 [577,] 0.07 3.01 1.07 0.14 0.36 0.29 -0.64 [578,] 0.13 4.24 0.93 0.53 0.53 0.53 0.24 [579,] 0.14 5.19 2.10 0.42 0.84 0.28 0.39 [580,] 0.03 1.26 0.45 0.09 0.18 0.09 -0.94 [581,] 0.07 1.80 0.27 0.07 0.33 0.27 -0.68 [582,] 0.05 1.88 0.76 0.20 0.35 0.15 -0.82 [583,] 0.13 4.98 2.02 0.40 0.81 0.54 0.28 [584,] 0.10 2.25 0.15 0.20 0.31 0.31 -0.26 [585,] 0.09 2.75 0.34 0.26 0.52 0.17 -0.48 [586,] 0.13 4.21 1.97 0.66 0.79 0.66 0.22 [587,] 0.23 12.10 3.49 0.23 1.16 1.16 2.84 [588,] 0.22 10.12 3.23 0.22 1.29 1.08 2.28 [589,] 0.10 3.15 1.48 0.39 0.39 0.39 -0.31 [590,] 0.07 2.27 1.06 0.21 0.21 0.14 -0.64 [591,] 0.15 4.14 1.07 0.61 0.15 0.31 0.67 [592,] 0.15 6.42 2.29 0.46 0.92 0.31 0.66 [593,] 0.07 3.04 1.09 0.14 0.22 0.14 -0.63 [594,] 0.22 6.01 1.56 0.45 1.11 0.89 2.51 [595,] 0.10 3.36 1.05 0.42 0.42 0.31 -0.22 [596,] 0.10 4.91 1.57 0.31 0.42 0.21 -0.23 [597,] 0.12 2.64 0.18 0.12 0.24 0.60 0.02 [598,] 0.14 4.53 1.42 0.28 0.71 0.57 0.42 [599,] 0.10 3.16 0.99 0.20 0.59 0.49 -0.31 [600,] 0.02 0.51 0.16 0.07 0.14 0.05 -0.96 [601,] 0.09 2.97 1.39 0.28 0.09 0.46 -0.39 > > > proc.time() user system elapsed 7.78 1.53 9.29