R Under development (unstable) (2026-01-18 r89306 ucrt) -- "Unsuffered Consequences" Copyright (C) 2026 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( "sampleSelection" ) 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/ > suppressPackageStartupMessages( library( "lmtest" ) ) > options( digits = 3 ) > > ## loading and preparing data > data( Mroz87 ) > Mroz87$kids <- ( Mroz87$kids5 + Mroz87$kids618 > 0 ) > Mroz87$age30.39 <- Mroz87$age < 40 > Mroz87$age50.60 <- Mroz87$age >= 50 > > ## A simple single MC trial: note probit assumes normal errors > set.seed( 20080225 ) > simDat <- data.frame( x = runif( 100 ) ) > simDat$e <- rnorm( 100 ) > simDat$y <- 2 * simDat$x + simDat$e > probitResult <- probit( (y > 0) ~ x, data = simDat ) > print( probitResult ) Call: probit(formula = (y > 0) ~ x, data = simDat) Coefficients: (Intercept) x 0.32 1.44 > summary( probitResult ) -------------------------------------------- Probit binary choice model/Maximum Likelihood estimation Newton-Raphson maximisation, 5 iterations Return code 1: gradient close to zero (gradtol) Log-Likelihood: -40.7 Model: Y == 'TRUE' in contrary to 'FALSE' 100 observations (16 'negative' and 84 'positive') and 2 free parameters (df = 98) Estimates: Estimate Std. error t value Pr(> t) (Intercept) 0.320 0.300 1.07 0.286 x 1.443 0.584 2.47 0.013 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significance test: chi2(1) = 6.55 (p=0.0105) -------------------------------------------- > coef( probitResult ) (Intercept) x 0.32 1.44 > stdEr( probitResult ) (Intercept) x 0.300 0.584 > vcov( probitResult ) (Intercept) x (Intercept) 0.0902 -0.150 x -0.1496 0.341 > nobs( probitResult ) [1] 100 > nObs( probitResult ) [1] 100 > df.residual( probitResult ) [1] 98 > logLik( probitResult ) 'log Lik.' -40.7 (df=2) > model.frame( probitResult ) (y > 0) x 1 FALSE 0.8578 2 TRUE 0.2095 3 TRUE 0.7252 4 FALSE 0.3852 5 TRUE 0.7642 6 TRUE 0.0670 7 TRUE 0.2279 8 TRUE 0.3479 9 TRUE 0.3748 10 FALSE 0.2885 11 FALSE 0.6666 12 TRUE 0.8908 13 TRUE 0.4169 14 FALSE 0.2805 15 TRUE 0.4289 16 FALSE 0.8123 17 TRUE 0.4342 18 TRUE 0.7219 19 TRUE 0.5354 20 TRUE 0.5088 21 TRUE 0.6134 22 TRUE 0.7772 23 TRUE 0.2925 24 TRUE 0.7707 25 FALSE 0.0248 26 FALSE 0.1183 27 TRUE 0.8597 28 TRUE 0.3348 29 TRUE 0.6604 30 TRUE 0.7982 31 TRUE 0.6112 32 TRUE 0.1323 33 TRUE 0.2583 34 TRUE 0.5948 35 TRUE 0.0476 36 TRUE 0.5141 37 TRUE 0.9643 38 FALSE 0.0373 39 TRUE 0.5541 40 FALSE 0.2473 41 TRUE 0.6956 42 TRUE 0.8097 43 TRUE 0.6226 44 TRUE 0.6547 45 TRUE 0.9633 46 FALSE 0.1897 47 TRUE 0.7765 48 TRUE 0.3945 49 TRUE 0.9595 50 TRUE 0.6937 51 TRUE 0.9279 52 TRUE 0.0729 53 TRUE 0.4328 54 TRUE 0.5375 55 TRUE 0.8613 56 TRUE 0.4234 57 TRUE 0.0586 58 TRUE 0.3954 59 TRUE 0.6814 60 TRUE 0.6555 61 TRUE 0.4966 62 TRUE 0.8711 63 TRUE 0.5787 64 TRUE 0.9142 65 TRUE 0.3148 66 TRUE 0.1275 67 TRUE 0.5557 68 TRUE 0.2103 69 TRUE 0.5995 70 TRUE 0.2221 71 FALSE 0.2999 72 TRUE 0.1047 73 TRUE 0.3790 74 TRUE 0.3345 75 TRUE 0.1792 76 FALSE 0.7925 77 TRUE 0.0577 78 TRUE 0.4821 79 TRUE 0.8295 80 TRUE 0.4637 81 TRUE 0.4191 82 TRUE 0.9267 83 FALSE 0.0201 84 TRUE 0.8520 85 TRUE 0.7059 86 TRUE 0.3428 87 TRUE 0.4517 88 TRUE 0.8549 89 TRUE 0.3842 90 TRUE 0.8773 91 TRUE 0.2642 92 TRUE 0.9297 93 TRUE 0.9710 94 FALSE 0.2346 95 TRUE 0.4273 96 TRUE 0.5949 97 TRUE 0.7867 98 TRUE 0.9336 99 TRUE 0.7844 100 FALSE 0.4848 > model.matrix( probitResult ) (Intercept) x 1 1 0.8578 2 1 0.2095 3 1 0.7252 4 1 0.3852 5 1 0.7642 6 1 0.0670 7 1 0.2279 8 1 0.3479 9 1 0.3748 10 1 0.2885 11 1 0.6666 12 1 0.8908 13 1 0.4169 14 1 0.2805 15 1 0.4289 16 1 0.8123 17 1 0.4342 18 1 0.7219 19 1 0.5354 20 1 0.5088 21 1 0.6134 22 1 0.7772 23 1 0.2925 24 1 0.7707 25 1 0.0248 26 1 0.1183 27 1 0.8597 28 1 0.3348 29 1 0.6604 30 1 0.7982 31 1 0.6112 32 1 0.1323 33 1 0.2583 34 1 0.5948 35 1 0.0476 36 1 0.5141 37 1 0.9643 38 1 0.0373 39 1 0.5541 40 1 0.2473 41 1 0.6956 42 1 0.8097 43 1 0.6226 44 1 0.6547 45 1 0.9633 46 1 0.1897 47 1 0.7765 48 1 0.3945 49 1 0.9595 50 1 0.6937 51 1 0.9279 52 1 0.0729 53 1 0.4328 54 1 0.5375 55 1 0.8613 56 1 0.4234 57 1 0.0586 58 1 0.3954 59 1 0.6814 60 1 0.6555 61 1 0.4966 62 1 0.8711 63 1 0.5787 64 1 0.9142 65 1 0.3148 66 1 0.1275 67 1 0.5557 68 1 0.2103 69 1 0.5995 70 1 0.2221 71 1 0.2999 72 1 0.1047 73 1 0.3790 74 1 0.3345 75 1 0.1792 76 1 0.7925 77 1 0.0577 78 1 0.4821 79 1 0.8295 80 1 0.4637 81 1 0.4191 82 1 0.9267 83 1 0.0201 84 1 0.8520 85 1 0.7059 86 1 0.3428 87 1 0.4517 88 1 0.8549 89 1 0.3842 90 1 0.8773 91 1 0.2642 92 1 0.9297 93 1 0.9710 94 1 0.2346 95 1 0.4273 96 1 0.5949 97 1 0.7867 98 1 0.9336 99 1 0.7844 100 1 0.4848 attr(,"assign") [1] 0 1 > fitted( probitResult ) 1 2 3 4 5 6 7 8 9 10 11 12 13 0.940 0.733 0.914 0.809 0.923 0.662 0.742 0.795 0.805 0.769 0.900 0.946 0.822 14 15 16 17 18 19 20 21 22 23 24 25 26 0.766 0.826 0.932 0.828 0.913 0.863 0.854 0.886 0.925 0.771 0.924 0.639 0.688 27 28 29 30 31 32 33 34 35 36 37 38 39 0.941 0.789 0.899 0.929 0.885 0.695 0.756 0.881 0.651 0.856 0.957 0.646 0.869 40 41 42 43 44 45 46 47 48 49 50 51 52 0.751 0.907 0.932 0.888 0.897 0.956 0.724 0.925 0.813 0.956 0.907 0.951 0.665 53 54 55 56 57 58 59 60 61 62 63 64 65 0.828 0.863 0.941 0.824 0.657 0.813 0.904 0.897 0.850 0.943 0.876 0.949 0.781 66 67 68 69 70 71 72 73 74 75 76 77 78 0.693 0.869 0.734 0.882 0.739 0.774 0.681 0.807 0.789 0.719 0.928 0.657 0.845 79 80 81 82 83 84 85 86 87 88 89 90 91 0.935 0.839 0.822 0.951 0.637 0.939 0.910 0.792 0.834 0.940 0.809 0.944 0.758 92 93 94 95 96 97 98 99 100 0.952 0.957 0.745 0.826 0.881 0.927 0.952 0.927 0.846 > all.equal( fitted( probitResult ), predict( probitResult, type = "response" ) ) [1] TRUE > all.equal( fitted( probitResult )[ 11:22 ], + predict( probitResult, newdata = simDat[ 11:22, ], type = "response" ) ) [1] TRUE > linearPredictors( probitResult ) 1 2 3 4 5 6 7 8 9 10 11 12 13 1.558 0.623 1.367 0.876 1.423 0.417 0.649 0.822 0.861 0.736 1.282 1.606 0.922 14 15 16 17 18 19 20 21 22 23 24 25 26 0.725 0.939 1.492 0.947 1.362 1.093 1.054 1.205 1.442 0.742 1.432 0.356 0.491 27 28 29 30 31 32 33 34 35 36 37 38 39 1.561 0.803 1.273 1.472 1.202 0.511 0.693 1.179 0.389 1.062 1.712 0.374 1.120 40 41 42 43 44 45 46 47 48 49 50 51 52 0.677 1.324 1.489 1.219 1.265 1.710 0.594 1.441 0.890 1.705 1.321 1.659 0.425 53 54 55 56 57 58 59 60 61 62 63 64 65 0.945 1.096 1.563 0.931 0.405 0.891 1.303 1.266 1.037 1.577 1.155 1.639 0.774 66 67 68 69 70 71 72 73 74 75 76 77 78 0.504 1.122 0.624 1.185 0.641 0.753 0.471 0.867 0.803 0.579 1.464 0.403 1.016 79 80 81 82 83 84 85 86 87 88 89 90 91 1.517 0.989 0.925 1.657 0.349 1.550 1.339 0.815 0.972 1.554 0.875 1.586 0.701 92 93 94 95 96 97 98 99 100 1.662 1.721 0.659 0.937 1.179 1.455 1.667 1.452 1.020 > all.equal( linearPredictors( probitResult ), predict( probitResult ) ) [1] TRUE > all.equal( linearPredictors( probitResult )[ 11:22 ], + predict( probitResult, newdata = simDat[ 11:22, ] ) ) [1] TRUE > residuals( probitResult, type = "response" ) 1 2 3 4 5 6 7 8 9 10 -0.9404 0.2668 0.0859 -0.8095 0.0774 0.3384 0.2582 0.2055 0.1946 -0.7693 11 12 13 14 15 16 17 18 19 20 -0.9001 0.0542 0.1783 -0.7658 0.1738 -0.9322 0.1719 0.0866 0.1372 0.1459 21 22 23 24 25 26 27 28 29 30 0.1140 0.0747 0.2290 0.0760 -0.6391 -0.6883 0.0593 0.2109 0.1015 0.0705 31 32 33 34 35 36 37 38 39 40 0.1146 0.3046 0.2442 0.1193 0.3487 0.1441 0.0435 -0.6458 0.1314 -0.7508 41 42 43 44 45 46 47 48 49 50 0.0927 0.0683 0.1115 0.1030 0.0436 -0.7237 0.0748 0.1869 0.0441 0.0932 51 52 53 54 55 56 57 58 59 60 0.0485 0.3353 0.1724 0.1366 0.0590 0.1759 0.3428 0.1865 0.0962 0.1027 61 62 63 64 65 66 67 68 69 70 0.1499 0.0574 0.1240 0.0506 0.2193 0.3071 0.1309 0.2664 0.1179 0.2609 71 72 73 74 75 76 77 78 79 80 -0.7743 0.3187 0.1930 0.2110 0.2814 -0.9284 0.3433 0.1549 0.0646 0.1612 81 82 83 84 85 86 87 88 89 90 0.1775 0.0487 -0.6365 0.0606 0.0903 0.2076 0.1655 0.0601 0.1909 0.0564 91 92 93 94 95 96 97 98 99 100 0.2415 0.0483 0.0426 -0.7450 0.1744 0.1193 0.0728 0.0477 0.0732 -0.8461 > residuals( probitResult, type = "pearson" ) 1 2 3 4 5 6 7 8 9 10 11 -3.972 0.603 0.306 -2.061 0.290 0.715 0.590 0.509 0.492 -1.826 -3.002 12 13 14 15 16 17 18 19 20 21 22 0.239 0.466 -1.808 0.459 -3.708 0.456 0.308 0.399 0.413 0.359 0.284 23 24 25 26 27 28 29 30 31 32 33 0.545 0.287 -1.331 -1.486 0.251 0.517 0.336 0.275 0.360 0.662 0.568 34 35 36 37 38 39 40 41 42 43 44 0.368 0.732 0.410 0.213 -1.350 0.389 -1.736 0.320 0.271 0.354 0.339 45 46 47 48 49 50 51 52 53 54 55 0.214 -1.619 0.284 0.479 0.215 0.321 0.226 0.710 0.456 0.398 0.250 56 57 58 59 60 61 62 63 64 65 66 0.462 0.722 0.479 0.326 0.338 0.420 0.247 0.376 0.231 0.530 0.666 67 68 69 70 71 72 73 74 75 76 77 0.388 0.603 0.366 0.594 -1.852 0.684 0.489 0.517 0.626 -3.600 0.723 78 79 80 81 82 83 84 85 86 87 88 0.428 0.263 0.438 0.465 0.226 -1.323 0.254 0.315 0.512 0.445 0.253 89 90 91 92 93 94 95 96 97 98 99 0.486 0.244 0.564 0.225 0.211 -1.709 0.460 0.368 0.280 0.224 0.281 100 -2.345 > residuals( probitResult, type = "deviance" ) 1 2 3 4 5 6 7 8 9 10 11 -2.375 0.788 0.424 -1.821 0.401 0.909 0.773 0.678 0.658 -1.713 -2.146 12 13 14 15 16 17 18 19 20 21 22 0.334 0.627 -1.704 0.618 -2.320 0.614 0.426 0.543 0.562 0.492 0.394 23 24 25 26 27 28 29 30 31 32 33 0.721 0.398 -1.428 -1.527 0.350 0.688 0.463 0.382 0.493 0.852 0.748 34 35 36 37 38 39 40 41 42 43 44 0.504 0.926 0.558 0.298 -1.441 0.531 -1.667 0.441 0.376 0.486 0.466 45 46 47 48 49 50 51 52 53 54 55 0.299 -1.604 0.394 0.643 0.300 0.442 0.315 0.904 0.615 0.542 0.349 56 57 58 59 60 61 62 63 64 65 66 0.622 0.916 0.643 0.450 0.466 0.570 0.344 0.515 0.322 0.704 0.857 67 68 69 70 71 72 73 74 75 76 77 0.530 0.787 0.501 0.778 -1.725 0.876 0.655 0.689 0.813 -2.296 0.917 78 79 80 81 82 83 84 85 86 87 88 0.580 0.366 0.593 0.625 0.316 -1.423 0.354 0.435 0.682 0.602 0.352 89 90 91 92 93 94 95 96 97 98 99 0.651 0.341 0.744 0.315 0.295 -1.653 0.619 0.504 0.389 0.313 0.390 100 -1.935 > all.equal( residuals( probitResult, type = "deviance" ), + residuals( probitResult ) ) [1] TRUE > all.equal( residuals( probitResult, type = "response" ), + ( simDat$y > 0 ) - fitted( probitResult ) ) [1] TRUE > lrtest( probitResult ) Likelihood ratio test Model 1: (y > 0) ~ x Model 2: (y > 0) ~ 1 #Df LogLik Df Chisq Pr(>Chisq) 1 2 -40.7 2 1 -44.0 -1 6.55 0.01 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > all.equal( lrtest( probitResult, (y > 0) ~ 1 ), lrtest( probitResult ) ) [1] TRUE > probitResult0 <- probit( (y > 0) ~ 1, data = simDat ) > all.equal( lrtest( probitResult, probitResult0 ), lrtest( probitResult ) ) [1] TRUE > > # estimation with glm() > probitResult2 <- glm( (y > 0) ~ x, family = binomial( link = "probit" ), + data = simDat ) > all.equal( coef( probitResult ), coef( probitResult2 ), tol = 1e-4 ) [1] TRUE > all.equal( stdEr( probitResult ), stdEr( probitResult2 ), tol = 1e-1 ) [1] TRUE > all.equal( logLik( probitResult ), logLik( probitResult2 ) ) [1] TRUE > all.equal( model.frame( probitResult ), model.frame( probitResult2 ) ) [1] TRUE > all.equal( model.matrix( probitResult ), model.matrix( probitResult2 ) ) [1] TRUE > all.equal( fitted( probitResult ), fitted( probitResult2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResult, type = "response" ), + predict( probitResult2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResult, newdata = simDat[ 5:55, ], type = "response" ), + predict( probitResult2, newdata = simDat[ 5:55, ], type = "response" ), + tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResult ), predict( probitResult2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResult, newdata = simDat[ 5:55, ] ), + predict( probitResult2, newdata = simDat[ 5:55, ] ), + tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResult, type = "response" ), + residuals( probitResult2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResult, type = "pearson" ), + residuals( probitResult2, type = "pearson" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResult, type = "deviance" ), + residuals( probitResult2, type = "deviance" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResult ), residuals( probitResult2 ), tol = 1e-4 ) [1] TRUE > lrtest( probitResult2 ) Likelihood ratio test Model 1: (y > 0) ~ x Model 2: (y > 0) ~ 1 #Df LogLik Df Chisq Pr(>Chisq) 1 2 -40.7 2 1 -44.0 -1 6.55 0.01 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > all.equal( lrtest( probitResult2 ), lrtest( probitResult ) ) [1] TRUE > > # estimation with equal weights > simDat$we <- rep( 0.5, 100 ) > probitResultWe <- probit( (y > 0) ~ x, weights = simDat$we, data = simDat ) > print( probitResultWe ) Call: probit(formula = (y > 0) ~ x, data = simDat, weights = simDat$we) Coefficients: (Intercept) x 0.32 1.44 > summary( probitResultWe ) -------------------------------------------- Probit binary choice model/Maximum Likelihood estimation Newton-Raphson maximisation, 5 iterations Return code 1: gradient close to zero (gradtol) Log-Likelihood: -20.3 Model: Y == 'TRUE' in contrary to 'FALSE' 100 observations (16 'negative' and 84 'positive') and 2 free parameters (df = 98) Estimates: Estimate Std. error t value Pr(> t) (Intercept) 0.320 0.425 0.75 0.451 x 1.443 0.826 1.75 0.081 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significance test: chi2(1) = 47.2 (p=6.27e-12) -------------------------------------------- > all.equal( coef( probitResult ), coef( probitResultWe ) ) [1] TRUE > all.equal( stdEr( probitResult ), stdEr( probitResultWe ) * sqrt(0.5) ) [1] TRUE > all.equal( vcov( probitResult ), vcov( probitResultWe ) * 0.5 ) [1] TRUE > all.equal( nobs( probitResult ), nobs( probitResultWe ) ) [1] TRUE > all.equal( nObs( probitResult ), nObs( probitResultWe ) ) [1] TRUE > all.equal( df.residual( probitResult ), df.residual( probitResultWe ) ) [1] TRUE > all.equal( logLik( probitResult ) * 0.5, logLik( probitResultWe ) ) [1] TRUE > model.frame( probitResultWe ) (y > 0) x (weights) 1 FALSE 0.8578 0.5 2 TRUE 0.2095 0.5 3 TRUE 0.7252 0.5 4 FALSE 0.3852 0.5 5 TRUE 0.7642 0.5 6 TRUE 0.0670 0.5 7 TRUE 0.2279 0.5 8 TRUE 0.3479 0.5 9 TRUE 0.3748 0.5 10 FALSE 0.2885 0.5 11 FALSE 0.6666 0.5 12 TRUE 0.8908 0.5 13 TRUE 0.4169 0.5 14 FALSE 0.2805 0.5 15 TRUE 0.4289 0.5 16 FALSE 0.8123 0.5 17 TRUE 0.4342 0.5 18 TRUE 0.7219 0.5 19 TRUE 0.5354 0.5 20 TRUE 0.5088 0.5 21 TRUE 0.6134 0.5 22 TRUE 0.7772 0.5 23 TRUE 0.2925 0.5 24 TRUE 0.7707 0.5 25 FALSE 0.0248 0.5 26 FALSE 0.1183 0.5 27 TRUE 0.8597 0.5 28 TRUE 0.3348 0.5 29 TRUE 0.6604 0.5 30 TRUE 0.7982 0.5 31 TRUE 0.6112 0.5 32 TRUE 0.1323 0.5 33 TRUE 0.2583 0.5 34 TRUE 0.5948 0.5 35 TRUE 0.0476 0.5 36 TRUE 0.5141 0.5 37 TRUE 0.9643 0.5 38 FALSE 0.0373 0.5 39 TRUE 0.5541 0.5 40 FALSE 0.2473 0.5 41 TRUE 0.6956 0.5 42 TRUE 0.8097 0.5 43 TRUE 0.6226 0.5 44 TRUE 0.6547 0.5 45 TRUE 0.9633 0.5 46 FALSE 0.1897 0.5 47 TRUE 0.7765 0.5 48 TRUE 0.3945 0.5 49 TRUE 0.9595 0.5 50 TRUE 0.6937 0.5 51 TRUE 0.9279 0.5 52 TRUE 0.0729 0.5 53 TRUE 0.4328 0.5 54 TRUE 0.5375 0.5 55 TRUE 0.8613 0.5 56 TRUE 0.4234 0.5 57 TRUE 0.0586 0.5 58 TRUE 0.3954 0.5 59 TRUE 0.6814 0.5 60 TRUE 0.6555 0.5 61 TRUE 0.4966 0.5 62 TRUE 0.8711 0.5 63 TRUE 0.5787 0.5 64 TRUE 0.9142 0.5 65 TRUE 0.3148 0.5 66 TRUE 0.1275 0.5 67 TRUE 0.5557 0.5 68 TRUE 0.2103 0.5 69 TRUE 0.5995 0.5 70 TRUE 0.2221 0.5 71 FALSE 0.2999 0.5 72 TRUE 0.1047 0.5 73 TRUE 0.3790 0.5 74 TRUE 0.3345 0.5 75 TRUE 0.1792 0.5 76 FALSE 0.7925 0.5 77 TRUE 0.0577 0.5 78 TRUE 0.4821 0.5 79 TRUE 0.8295 0.5 80 TRUE 0.4637 0.5 81 TRUE 0.4191 0.5 82 TRUE 0.9267 0.5 83 FALSE 0.0201 0.5 84 TRUE 0.8520 0.5 85 TRUE 0.7059 0.5 86 TRUE 0.3428 0.5 87 TRUE 0.4517 0.5 88 TRUE 0.8549 0.5 89 TRUE 0.3842 0.5 90 TRUE 0.8773 0.5 91 TRUE 0.2642 0.5 92 TRUE 0.9297 0.5 93 TRUE 0.9710 0.5 94 FALSE 0.2346 0.5 95 TRUE 0.4273 0.5 96 TRUE 0.5949 0.5 97 TRUE 0.7867 0.5 98 TRUE 0.9336 0.5 99 TRUE 0.7844 0.5 100 FALSE 0.4848 0.5 > all.equal( model.matrix( probitResult ), model.matrix( probitResultWe ) ) [1] TRUE > all.equal( fitted( probitResult ), fitted( probitResultWe ) ) [1] TRUE > all.equal( predict( probitResult, type = "response" ), + predict( probitResultWe, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResult, newdata = simDat[ 5:55, ], type = "response" ), + predict( probitResultWe, newdata = simDat[ 5:55, ], type = "response" ), + tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResult ), predict( probitResultWe ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResult, newdata = simDat[ 5:55, ] ), + predict( probitResultWe, newdata = simDat[ 5:55, ] ), + tol = 1e-4 ) [1] TRUE > all.equal( linearPredictors( probitResult ), linearPredictors( probitResultWe ) ) [1] TRUE > all.equal( residuals( probitResult, type = "response" ), + residuals( probitResultWe, type = "response" ) ) [1] TRUE > all.equal( residuals( probitResult, type = "pearson" ), + residuals( probitResultWe, type = "pearson" ) / sqrt( 0.5 ) ) [1] TRUE > all.equal( residuals( probitResult, type = "deviance" ), + residuals( probitResultWe, type = "deviance" ) / sqrt( 0.5 ) ) [1] TRUE > all.equal( residuals( probitResultWe, type = "response" ), + ( simDat$y > 0 ) - fitted( probitResultWe ) ) [1] TRUE > all.equal( coef( probitResult ), coef( probitResultWe ), tol = 1e-4 ) [1] TRUE > all.equal( logLik( probitResult ) * 0.5, logLik( probitResultWe ) * 1 ) [1] TRUE > lrtest( probitResultWe ) Likelihood ratio test Model 1: (y > 0) ~ x Model 2: (y > 0) ~ 1 #Df LogLik Df Chisq Pr(>Chisq) 1 2 -20.4 2 1 -22.0 -1 3.28 0.07 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > # estimation with equal weights with glm() > probitResultWe2 <- glm( (y > 0) ~ x, family = binomial( link = "probit" ), + weights = simDat$we, data = simDat ) Warning message: In eval(family$initialize) : non-integer #successes in a binomial glm! > all.equal( coef( probitResultWe ), coef( probitResultWe2 ), tol = 1e-4 ) [1] TRUE > all.equal( stdEr( probitResultWe ), stdEr( probitResultWe2 ), tol = 1e-1 ) [1] TRUE > logLik( probitResultWe2 ) 'log Lik.' 0 (df=2) > all.equal( model.frame( probitResultWe ), model.frame( probitResultWe2 ) ) [1] TRUE > all.equal( model.matrix( probitResultWe ), model.matrix( probitResultWe2 ) ) [1] TRUE > all.equal( fitted( probitResultWe ), fitted( probitResultWe2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResultWe, type = "response" ), + predict( probitResultWe2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResultWe, newdata = simDat[ 5:55, ], type = "response" ), + predict( probitResultWe2, newdata = simDat[ 5:55, ], type = "response" ), + tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResultWe ), predict( probitResultWe2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResultWe, newdata = simDat[ 5:55, ] ), + predict( probitResultWe2, newdata = simDat[ 5:55, ] ), + tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResultWe, type = "response" ), + residuals( probitResultWe2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResultWe, type = "pearson" ), + residuals( probitResultWe2, type = "pearson" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResultWe, type = "deviance" ), + residuals( probitResultWe2, type = "deviance" ), tol = 1e-4 ) [1] TRUE > > # estimation with weights to account for stratified sampling > # proportion in the "population" > yProbPop <- sum( simDat$y > 0 ) / nrow( simDat ) > yProbPop [1] 0.84 > # stratified sample with all observations with y = 0 > simDat$sampStrat <- simDat$y <= 0 | rnorm( nrow( simDat ) ) > 0.25 > sum( simDat$sampStrat ) [1] 54 > # stratified sample of y and x > simDatSamp <- simDat[ simDat$sampStrat, ] > # proportion in the "sample" > yProbSamp <- sum( simDatSamp$y > 0 ) / nrow( simDatSamp ) > yProbSamp [1] 0.704 > # unweighted estimation (ignoring the stratification) > probitResultStrat <- probit( (y > 0) ~ x, data = simDatSamp ) > summary( probitResultStrat ) -------------------------------------------- Probit binary choice model/Maximum Likelihood estimation Newton-Raphson maximisation, 4 iterations Return code 1: gradient close to zero (gradtol) Log-Likelihood: -31.6 Model: Y == 'TRUE' in contrary to 'FALSE' 54 observations (16 'negative' and 38 'positive') and 2 free parameters (df = 52) Estimates: Estimate Std. error t value Pr(> t) (Intercept) 0.0621 0.3483 0.18 0.86 x 1.1074 0.7089 1.56 0.12 Significance test: chi2(1) = 2.53 (p=0.112) -------------------------------------------- > nobs( probitResultStrat ) [1] 54 > nObs( probitResultStrat ) [1] 54 > df.residual( probitResultStrat ) [1] 52 > logLik( probitResultStrat ) 'log Lik.' -31.6 (df=2) > lrtest( probitResultStrat ) Likelihood ratio test Model 1: (y > 0) ~ x Model 2: (y > 0) ~ 1 #Df LogLik Df Chisq Pr(>Chisq) 1 2 -31.6 2 1 -32.8 -1 2.53 0.11 > # weights > simDatSamp$wStrat <- ifelse( simDatSamp$y > 0, yProbPop / yProbSamp, + ( 1 - yProbPop ) / ( 1 - yProbSamp ) ) > probitResultStratW <- probit( (y > 0) ~ x, weights = simDatSamp$wStrat, + data = simDatSamp ) > print( probitResultStratW ) Call: probit(formula = (y > 0) ~ x, data = simDatSamp, weights = simDatSamp$wStrat) Coefficients: (Intercept) x 0.542 1.054 > summary( probitResultStratW ) -------------------------------------------- Probit binary choice model/Maximum Likelihood estimation Newton-Raphson maximisation, 4 iterations Return code 1: gradient close to zero (gradtol) Log-Likelihood: -22.9 Model: Y == 'TRUE' in contrary to 'FALSE' 54 observations (16 'negative' and 38 'positive') and 2 free parameters (df = 52) Estimates: Estimate Std. error t value Pr(> t) (Intercept) 0.542 0.401 1.35 0.18 x 1.054 0.831 1.27 0.20 Significance test: chi2(1) = 19.8 (p=8.46e-06) -------------------------------------------- > coef( probitResultStratW ) (Intercept) x 0.542 1.054 > stdEr( probitResultStratW ) (Intercept) x 0.401 0.831 > vcov( probitResultStratW ) (Intercept) x (Intercept) 0.161 -0.285 x -0.285 0.691 > nobs( probitResultStratW ) [1] 54 > nObs( probitResultStratW ) [1] 54 > df.residual( probitResultStratW ) [1] 52 > logLik( probitResultStratW ) 'log Lik.' -22.9 (df=2) > model.frame( probitResultStratW ) (y > 0) x (weights) 1 FALSE 0.8578 0.54 2 TRUE 0.2095 1.19 3 TRUE 0.7252 1.19 4 FALSE 0.3852 0.54 8 TRUE 0.3479 1.19 9 TRUE 0.3748 1.19 10 FALSE 0.2885 0.54 11 FALSE 0.6666 0.54 12 TRUE 0.8908 1.19 13 TRUE 0.4169 1.19 14 FALSE 0.2805 0.54 15 TRUE 0.4289 1.19 16 FALSE 0.8123 0.54 18 TRUE 0.7219 1.19 19 TRUE 0.5354 1.19 20 TRUE 0.5088 1.19 22 TRUE 0.7772 1.19 23 TRUE 0.2925 1.19 25 FALSE 0.0248 0.54 26 FALSE 0.1183 0.54 28 TRUE 0.3348 1.19 31 TRUE 0.6112 1.19 32 TRUE 0.1323 1.19 33 TRUE 0.2583 1.19 38 FALSE 0.0373 0.54 39 TRUE 0.5541 1.19 40 FALSE 0.2473 0.54 43 TRUE 0.6226 1.19 46 FALSE 0.1897 0.54 47 TRUE 0.7765 1.19 49 TRUE 0.9595 1.19 51 TRUE 0.9279 1.19 52 TRUE 0.0729 1.19 58 TRUE 0.3954 1.19 61 TRUE 0.4966 1.19 65 TRUE 0.3148 1.19 67 TRUE 0.5557 1.19 68 TRUE 0.2103 1.19 71 FALSE 0.2999 0.54 72 TRUE 0.1047 1.19 73 TRUE 0.3790 1.19 75 TRUE 0.1792 1.19 76 FALSE 0.7925 0.54 77 TRUE 0.0577 1.19 80 TRUE 0.4637 1.19 83 FALSE 0.0201 0.54 87 TRUE 0.4517 1.19 89 TRUE 0.3842 1.19 90 TRUE 0.8773 1.19 94 FALSE 0.2346 0.54 95 TRUE 0.4273 1.19 97 TRUE 0.7867 1.19 99 TRUE 0.7844 1.19 100 FALSE 0.4848 0.54 > model.matrix( probitResultStratW ) (Intercept) x 1 1 0.8578 2 1 0.2095 3 1 0.7252 4 1 0.3852 8 1 0.3479 9 1 0.3748 10 1 0.2885 11 1 0.6666 12 1 0.8908 13 1 0.4169 14 1 0.2805 15 1 0.4289 16 1 0.8123 18 1 0.7219 19 1 0.5354 20 1 0.5088 22 1 0.7772 23 1 0.2925 25 1 0.0248 26 1 0.1183 28 1 0.3348 31 1 0.6112 32 1 0.1323 33 1 0.2583 38 1 0.0373 39 1 0.5541 40 1 0.2473 43 1 0.6226 46 1 0.1897 47 1 0.7765 49 1 0.9595 51 1 0.9279 52 1 0.0729 58 1 0.3954 61 1 0.4966 65 1 0.3148 67 1 0.5557 68 1 0.2103 71 1 0.2999 72 1 0.1047 73 1 0.3790 75 1 0.1792 76 1 0.7925 77 1 0.0577 80 1 0.4637 83 1 0.0201 87 1 0.4517 89 1 0.3842 90 1 0.8773 94 1 0.2346 95 1 0.4273 97 1 0.7867 99 1 0.7844 100 1 0.4848 attr(,"assign") [1] 0 1 > fitted( probitResultStratW ) 1 2 3 4 8 9 10 11 12 13 14 15 16 0.926 0.777 0.904 0.828 0.818 0.825 0.801 0.893 0.931 0.837 0.799 0.840 0.919 18 19 20 22 23 25 26 28 31 32 33 38 39 0.904 0.866 0.859 0.913 0.802 0.715 0.747 0.814 0.882 0.752 0.792 0.719 0.870 40 43 46 47 49 51 52 58 61 65 67 68 71 0.789 0.884 0.771 0.913 0.940 0.936 0.732 0.831 0.857 0.809 0.870 0.777 0.804 72 73 75 76 77 80 83 87 89 90 94 95 97 0.743 0.827 0.767 0.916 0.727 0.849 0.713 0.846 0.828 0.929 0.785 0.839 0.915 99 100 0.914 0.854 > all.equal( fitted( probitResultStratW ), predict( probitResultStratW, + type = "response" ) ) [1] TRUE > all.equal( fitted( probitResultStratW )[ 11:22 ], + predict( probitResultStratW, newdata = simDatSamp[ 11:22, ], + type = "response" ) ) [1] TRUE > linearPredictors( probitResultStratW ) 1 2 3 4 8 9 10 11 12 13 14 15 16 1.446 0.762 1.306 0.947 0.908 0.937 0.846 1.244 1.480 0.981 0.837 0.994 1.398 18 19 20 22 23 25 26 28 31 32 33 38 39 1.302 1.106 1.078 1.361 0.850 0.568 0.666 0.894 1.186 0.681 0.814 0.581 1.125 40 43 46 47 49 51 52 58 61 65 67 68 71 0.802 1.198 0.741 1.360 1.553 1.519 0.618 0.958 1.065 0.873 1.127 0.763 0.858 72 73 75 76 77 80 83 87 89 90 94 95 97 0.652 0.941 0.730 1.377 0.602 1.030 0.563 1.018 0.946 1.466 0.789 0.992 1.371 99 100 1.368 1.052 > all.equal( linearPredictors( probitResultStratW ), + predict( probitResultStratW ) ) [1] TRUE > all.equal( linearPredictors( probitResultStratW )[ 11:22 ], + predict( probitResultStratW, newdata = simDatSamp[ 11:22, ] ) ) [1] TRUE > residuals( probitResultStratW, type = "response" ) 1 2 3 4 8 9 10 11 12 13 -0.9258 0.2229 0.0958 -0.8283 0.1819 0.1745 -0.8011 -0.8933 0.0694 0.1633 14 15 16 18 19 20 22 23 25 26 -0.7988 0.1602 -0.9189 0.0964 0.1344 0.1406 0.0868 0.1977 -0.7149 -0.7474 28 31 32 33 38 39 40 43 46 47 0.1856 0.1179 0.2479 0.2079 -0.7193 0.1302 -0.7888 0.1155 -0.7708 0.0869 49 51 52 58 61 65 67 68 71 72 0.0602 0.0643 0.2682 0.1690 0.1435 0.1913 0.1298 0.2227 -0.8045 0.2572 73 75 76 77 80 83 87 89 90 94 0.1734 0.2326 -0.9157 0.2735 0.1514 -0.7132 0.1545 0.1720 0.0713 -0.7849 95 97 99 100 0.1606 0.0853 0.0856 -0.8537 > residuals( probitResultStratW, type = "pearson" ) 1 2 3 4 8 9 10 11 12 13 14 -2.597 0.585 0.356 -1.614 0.515 0.502 -1.475 -2.126 0.298 0.483 -1.464 15 16 18 19 20 22 23 25 26 28 31 0.477 -2.473 0.357 0.431 0.442 0.337 0.542 -1.164 -1.264 0.522 0.399 32 33 38 39 40 43 46 47 49 51 52 0.627 0.560 -1.176 0.423 -1.420 0.395 -1.348 0.337 0.277 0.286 0.661 58 61 65 67 68 71 72 73 75 76 77 0.493 0.447 0.531 0.422 0.585 -1.490 0.643 0.500 0.601 -2.422 0.670 80 83 87 89 90 94 95 97 99 100 0.462 -1.159 0.467 0.498 0.303 -1.404 0.478 0.334 0.334 -1.775 > residuals( probitResultStratW, type = "deviance" ) 1 2 3 4 8 9 10 11 12 13 14 -1.676 0.776 0.490 -1.379 0.692 0.677 -1.321 -1.554 0.414 0.652 -1.316 15 16 18 19 20 22 23 25 26 28 31 0.646 -1.647 0.492 0.587 0.601 0.466 0.725 -1.164 -1.219 0.700 0.547 32 33 38 39 40 43 46 47 49 51 52 0.825 0.746 -1.171 0.577 -1.296 0.541 -1.261 0.466 0.385 0.398 0.863 58 61 65 67 68 71 72 73 75 76 77 0.665 0.608 0.712 0.576 0.775 -1.328 0.843 0.674 0.795 -1.634 0.873 80 83 87 89 90 94 95 97 99 100 0.626 -1.161 0.633 0.671 0.420 -1.288 0.647 0.461 0.462 -1.441 > all.equal( residuals( probitResultStratW, type = "response" ), + ( simDatSamp$y > 0 ) - fitted( probitResultStratW ) ) [1] TRUE > lrtest( probitResultStratW ) Likelihood ratio test Model 1: (y > 0) ~ x Model 2: (y > 0) ~ 1 #Df LogLik Df Chisq Pr(>Chisq) 1 2 -22.9 2 1 -23.7 -1 1.68 0.19 > > # estimation with weights to account for stratified sampling with glm() > probitResultStratW2 <- glm( (y > 0) ~ x, + family = binomial( link = "probit" ), weights = simDatSamp$wStrat, + data = simDatSamp ) Warning message: In eval(family$initialize) : non-integer #successes in a binomial glm! > all.equal( coef( probitResultStratW ), coef( probitResultStratW2 ), tol = 1e-4 ) [1] TRUE > all.equal( stdEr( probitResultStratW ), stdEr( probitResultStratW2 ), tol = 1e-1 ) [1] TRUE > logLik( probitResultStratW2 ) 'log Lik.' -34.6 (df=2) > all.equal( model.frame( probitResultStratW ), model.frame( probitResultStratW2 ) ) [1] TRUE > all.equal( model.matrix( probitResultStratW ), model.matrix( probitResultStratW2 ) ) [1] TRUE > all.equal( fitted( probitResultStratW ), fitted( probitResultStratW2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResultStratW, type = "response" ), + predict( probitResultStratW2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( + predict( probitResultStratW, newdata = simDat[ 5:55, ], type = "response" ), + predict( probitResultStratW2, newdata = simDat[ 5:55, ], type = "response" ), + tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResultStratW ), predict( probitResultStratW2 ), + tol = 1e-4 ) [1] TRUE > all.equal( predict( probitResultStratW, newdata = simDatSamp[ 5:44, ] ), + predict( probitResultStratW2, newdata = simDatSamp[ 5:44, ] ), + tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResultStratW, type = "response" ), + residuals( probitResultStratW2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResultStratW, type = "pearson" ), + residuals( probitResultStratW2, type = "pearson" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( probitResultStratW, type = "deviance" ), + residuals( probitResultStratW2, type = "deviance" ), tol = 1e-4 ) [1] TRUE > > > ## female labour force participation probability > lfpResult <- probit( lfp ~ kids + age30.39 + age50.60 + educ + hushrs + + huseduc + huswage + mtr + motheduc, data = Mroz87 ) > print( lfpResult ) Call: probit(formula = lfp ~ kids + age30.39 + age50.60 + educ + hushrs + huseduc + huswage + mtr + motheduc, data = Mroz87) Coefficients: (Intercept) kidsTRUE age30.39TRUE age50.60TRUE educ hushrs 9.75e+00 -2.31e-01 1.78e-01 -5.14e-01 1.22e-01 -7.35e-04 huseduc huswage mtr motheduc -3.10e-02 -2.14e-01 -1.05e+01 -5.84e-03 > summary( lfpResult ) -------------------------------------------- Probit binary choice model/Maximum Likelihood estimation Newton-Raphson maximisation, 4 iterations Return code 8: successive function values within relative tolerance limit (reltol) Log-Likelihood: -435 Model: Y == '1' in contrary to '0' 753 observations (325 'negative' and 428 'positive') and 10 free parameters (df = 743) Estimates: Estimate Std. error t value Pr(> t) (Intercept) 9.75e+00 1.14e+00 8.55 < 2e-16 *** kidsTRUE -2.31e-01 1.35e-01 -1.71 0.08817 . age30.39TRUE 1.78e-01 1.17e-01 1.52 0.12774 age50.60TRUE -5.14e-01 1.49e-01 -3.45 0.00056 *** educ 1.22e-01 3.01e-02 4.04 5.3e-05 *** hushrs -7.35e-04 1.04e-04 -7.08 1.5e-12 *** huseduc -3.10e-02 2.25e-02 -1.38 0.16776 huswage -2.14e-01 2.40e-02 -8.93 < 2e-16 *** mtr -1.05e+01 1.15e+00 -9.10 < 2e-16 *** motheduc -5.84e-03 1.66e-02 -0.35 0.72575 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significance test: chi2(9) = 159 (p=9.51e-30) -------------------------------------------- > coef( lfpResult ) (Intercept) kidsTRUE age30.39TRUE age50.60TRUE educ hushrs 9.75e+00 -2.31e-01 1.78e-01 -5.14e-01 1.22e-01 -7.35e-04 huseduc huswage mtr motheduc -3.10e-02 -2.14e-01 -1.05e+01 -5.84e-03 > stdEr( lfpResult ) (Intercept) kidsTRUE age30.39TRUE age50.60TRUE educ hushrs 1.139889 0.135233 0.117024 0.148982 0.030062 0.000104 huseduc huswage mtr motheduc 0.022452 0.024015 1.152889 0.016638 > vcov( lfpResult ) (Intercept) kidsTRUE age30.39TRUE age50.60TRUE educ (Intercept) 1.30e+00 2.97e-03 9.45e-03 -2.60e-02 -7.48e-03 kidsTRUE 2.97e-03 1.83e-02 -3.21e-03 8.99e-03 -9.58e-05 age30.39TRUE 9.45e-03 -3.21e-03 1.37e-02 4.40e-03 1.47e-04 age50.60TRUE -2.60e-02 8.99e-03 4.40e-03 2.22e-02 -8.70e-05 educ -7.48e-03 -9.58e-05 1.47e-04 -8.70e-05 9.04e-04 hushrs -7.26e-05 -1.29e-06 -1.07e-06 1.37e-07 -1.34e-08 huseduc -1.34e-03 5.77e-05 -2.52e-04 1.96e-04 -3.21e-04 huswage -2.09e-02 -3.95e-04 -2.51e-05 9.71e-05 2.93e-06 mtr -1.25e+00 -1.37e-02 -1.15e-02 1.31e-02 2.85e-03 motheduc -1.47e-03 -1.15e-04 -1.99e-04 1.59e-04 -1.55e-04 hushrs huseduc huswage mtr motheduc (Intercept) -7.26e-05 -1.34e-03 -2.09e-02 -1.25e+00 -1.47e-03 kidsTRUE -1.29e-06 5.77e-05 -3.95e-04 -1.37e-02 -1.15e-04 age30.39TRUE -1.07e-06 -2.52e-04 -2.51e-05 -1.15e-02 -1.99e-04 age50.60TRUE 1.37e-07 1.96e-04 9.71e-05 1.31e-02 1.59e-04 educ -1.34e-08 -3.21e-04 2.93e-06 2.85e-03 -1.55e-04 hushrs 1.08e-08 -3.25e-07 1.44e-06 6.16e-05 9.26e-08 huseduc -3.25e-07 5.04e-04 -1.02e-04 1.00e-03 -2.28e-05 huswage 1.44e-06 -1.02e-04 5.77e-04 2.14e-02 2.44e-05 mtr 6.16e-05 1.00e-03 2.14e-02 1.33e+00 1.20e-03 motheduc 9.26e-08 -2.28e-05 2.44e-05 1.20e-03 2.77e-04 > nobs( lfpResult ) [1] 753 > nObs( lfpResult ) [1] 753 > df.residual( lfpResult ) [1] 743 > logLik( lfpResult ) 'log Lik.' -435 (df=10) > lrtest( lfpResult ) Likelihood ratio test Model 1: lfp ~ kids + age30.39 + age50.60 + educ + hushrs + huseduc + huswage + mtr + motheduc Model 2: lfp ~ 1 #Df LogLik Df Chisq Pr(>Chisq) 1 10 -435 2 1 -515 -9 159 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > lrtest( lfpResult, lfp ~ age50.60 + educ + hushrs + huswage + mtr ) Likelihood ratio test Model 1: lfp ~ kids + age30.39 + age50.60 + educ + hushrs + huseduc + huswage + mtr + motheduc Model 2: lfp ~ age50.60 + educ + hushrs + huswage + mtr #Df LogLik Df Chisq Pr(>Chisq) 1 10 -435 2 6 -438 -4 6.1 0.19 > model.frame( lfpResult ) lfp kids age30.39 age50.60 educ hushrs huseduc huswage mtr motheduc 1 1 TRUE TRUE FALSE 12 2708 12 4.029 0.722 12 2 1 TRUE TRUE FALSE 12 2310 9 8.442 0.661 7 3 1 TRUE TRUE FALSE 12 3072 12 3.581 0.692 12 4 1 TRUE TRUE FALSE 12 1920 10 3.542 0.781 7 5 1 TRUE TRUE FALSE 14 2000 12 10.000 0.622 12 6 1 FALSE FALSE TRUE 12 1040 11 6.711 0.692 14 7 1 TRUE TRUE FALSE 16 2670 12 3.428 0.692 14 8 1 FALSE FALSE TRUE 12 4120 8 2.549 0.692 3 9 1 TRUE FALSE FALSE 12 1995 4 4.221 0.751 7 10 1 TRUE TRUE FALSE 12 2100 12 5.714 0.692 7 11 1 TRUE TRUE FALSE 12 2450 12 9.796 0.582 12 12 1 TRUE FALSE FALSE 11 2375 14 8.000 0.622 14 13 1 TRUE TRUE FALSE 12 2830 16 5.300 0.722 16 14 1 TRUE FALSE FALSE 12 3317 12 4.341 0.722 10 15 1 TRUE FALSE FALSE 10 2024 17 10.870 0.582 7 16 1 TRUE TRUE FALSE 11 1694 12 9.150 0.722 16 17 1 TRUE FALSE FALSE 12 2156 12 6.122 0.692 10 18 1 TRUE TRUE FALSE 12 2250 12 6.150 0.722 12 19 1 FALSE FALSE FALSE 12 2024 11 6.917 0.692 7 20 1 TRUE TRUE FALSE 12 2123 10 4.710 0.692 12 21 1 TRUE FALSE FALSE 16 4160 16 3.131 0.622 10 22 1 FALSE TRUE FALSE 12 2000 12 4.000 0.722 12 23 1 FALSE FALSE FALSE 13 2420 17 7.223 0.661 7 24 1 FALSE FALSE FALSE 12 1150 17 7.965 0.722 7 25 1 TRUE TRUE FALSE 12 2024 12 4.088 0.751 12 26 1 TRUE FALSE FALSE 17 1904 17 14.181 0.582 16 27 1 FALSE FALSE TRUE 12 2448 16 6.536 0.692 3 28 1 TRUE TRUE FALSE 12 2000 13 8.500 0.661 3 29 1 TRUE TRUE FALSE 17 2390 17 6.276 0.622 12 30 1 FALSE FALSE FALSE 12 1920 10 5.208 0.722 12 31 1 FALSE FALSE TRUE 11 1840 10 2.782 0.751 7 32 1 FALSE FALSE TRUE 16 3360 17 4.911 0.692 3 33 1 TRUE TRUE FALSE 13 2284 13 5.867 0.661 12 34 1 TRUE FALSE TRUE 12 1875 8 7.520 0.692 7 35 1 TRUE FALSE FALSE 16 2140 17 7.545 0.661 12 36 1 TRUE FALSE FALSE 11 1896 8 5.538 0.751 10 37 1 FALSE FALSE FALSE 12 1040 16 6.923 0.692 3 38 1 TRUE FALSE FALSE 10 2200 12 5.000 0.661 10 39 1 FALSE FALSE TRUE 14 1952 12 7.306 0.622 7 40 1 FALSE TRUE FALSE 17 1560 17 11.218 0.580 14 41 1 TRUE TRUE FALSE 12 4030 16 3.846 0.722 12 42 1 FALSE FALSE TRUE 12 2570 12 5.837 0.622 9 43 1 TRUE TRUE FALSE 16 1530 16 13.725 0.661 14 44 1 TRUE FALSE FALSE 12 3149 8 6.349 0.661 3 45 1 TRUE TRUE FALSE 12 2690 12 5.253 0.722 12 46 1 TRUE TRUE FALSE 12 3096 12 1.308 0.781 12 47 1 TRUE FALSE FALSE 16 2552 16 2.800 0.722 14 48 1 FALSE FALSE FALSE 12 2040 11 2.696 0.722 10 49 1 FALSE FALSE TRUE 12 2180 13 7.569 0.661 7 50 1 TRUE TRUE FALSE 12 1864 12 3.408 0.781 12 51 1 TRUE FALSE FALSE 12 2068 12 6.540 0.692 7 52 1 FALSE FALSE TRUE 12 2010 12 7.214 0.692 7 53 1 TRUE TRUE FALSE 12 2152 10 6.273 0.751 12 54 1 FALSE FALSE TRUE 8 1496 11 5.882 0.751 7 55 1 FALSE FALSE TRUE 10 2100 4 3.809 0.751 7 56 1 FALSE TRUE FALSE 16 1960 14 6.378 0.692 12 57 1 FALSE FALSE TRUE 14 1985 15 6.045 0.640 7 58 1 FALSE TRUE FALSE 17 2020 17 8.812 0.580 17 59 1 FALSE FALSE TRUE 14 2178 16 8.877 0.582 17 60 1 TRUE TRUE FALSE 12 3684 12 3.342 0.751 12 61 1 TRUE FALSE FALSE 14 5010 13 3.184 0.551 14 62 1 TRUE FALSE FALSE 12 1880 12 6.915 0.692 12 63 1 FALSE FALSE FALSE 8 1904 8 5.515 0.722 7 64 1 FALSE FALSE FALSE 12 2083 12 5.281 0.722 7 65 1 TRUE FALSE FALSE 12 2125 12 3.200 0.751 7 66 1 FALSE TRUE FALSE 8 1985 12 5.879 0.692 12 67 1 TRUE FALSE FALSE 17 2640 17 6.250 0.692 12 68 1 FALSE TRUE FALSE 12 2070 8 7.488 0.722 12 69 1 TRUE FALSE FALSE 12 2107 8 6.977 0.692 7 70 1 TRUE FALSE TRUE 12 2250 10 8.000 0.692 12 71 1 FALSE TRUE FALSE 12 2880 16 4.132 0.692 12 72 1 FALSE FALSE TRUE 12 1848 12 14.476 0.551 10 73 1 FALSE FALSE TRUE 9 1927 7 5.734 0.661 7 74 1 TRUE TRUE FALSE 10 1304 9 3.374 0.791 0 75 1 TRUE TRUE FALSE 12 3000 12 1.833 0.722 7 76 1 TRUE FALSE FALSE 12 1892 12 5.603 0.661 12 77 1 TRUE TRUE FALSE 12 3644 12 4.298 0.722 7 78 1 FALSE FALSE FALSE 17 1430 17 3.916 0.751 3 79 1 TRUE TRUE FALSE 15 2350 14 4.879 0.751 10 80 1 FALSE FALSE TRUE 12 1948 16 9.240 0.661 7 81 1 FALSE FALSE TRUE 6 1804 12 6.652 0.722 12 82 1 FALSE FALSE TRUE 14 2326 12 4.622 0.751 12 83 1 TRUE FALSE TRUE 12 1739 11 9.974 0.661 7 84 1 FALSE FALSE TRUE 14 1176 17 20.918 0.622 7 85 1 FALSE FALSE TRUE 9 1100 8 1.940 0.791 7 86 1 TRUE FALSE TRUE 17 1528 17 8.835 0.582 7 87 1 FALSE FALSE TRUE 13 2250 15 4.667 0.692 7 88 1 TRUE FALSE FALSE 9 1927 10 5.189 0.751 7 89 1 TRUE FALSE FALSE 15 2414 16 18.724 0.442 7 90 1 TRUE TRUE FALSE 12 768 8 10.417 0.771 10 91 1 TRUE FALSE FALSE 12 1984 14 8.846 0.622 7 92 1 TRUE TRUE FALSE 12 2246 12 7.569 0.722 12 93 1 TRUE TRUE FALSE 12 3024 17 4.451 0.622 10 94 1 TRUE FALSE FALSE 12 2921 12 9.320 0.622 12 95 1 TRUE TRUE FALSE 12 2045 17 9.169 0.692 7 96 1 TRUE FALSE FALSE 12 1928 12 6.483 0.722 7 97 1 TRUE FALSE FALSE 12 1920 10 7.812 0.692 7 98 1 TRUE TRUE FALSE 13 2280 17 11.404 0.582 14 99 1 FALSE FALSE FALSE 12 2300 17 5.087 0.692 7 100 1 TRUE TRUE FALSE 13 2480 13 6.976 0.622 12 101 1 TRUE TRUE FALSE 12 1135 10 5.286 0.771 12 102 1 TRUE TRUE FALSE 12 1384 12 11.562 0.661 7 103 1 TRUE FALSE FALSE 12 1848 14 8.606 0.692 7 104 1 TRUE FALSE FALSE 16 2499 17 12.805 0.442 14 105 1 TRUE FALSE FALSE 12 2390 12 6.695 0.692 12 106 1 TRUE TRUE FALSE 13 2400 14 8.333 0.692 10 107 1 FALSE FALSE FALSE 11 1920 8 4.167 0.692 7 108 1 TRUE FALSE FALSE 12 2301 13 5.476 0.722 7 109 1 TRUE FALSE FALSE 12 1944 10 5.144 0.692 7 110 1 TRUE TRUE FALSE 12 2100 17 11.667 0.661 7 111 1 TRUE TRUE FALSE 17 1920 16 7.292 0.722 12 112 1 FALSE FALSE TRUE 14 2880 14 4.861 0.661 7 113 1 TRUE FALSE FALSE 16 1932 16 12.164 0.622 12 114 1 FALSE FALSE FALSE 17 3234 17 10.823 0.462 10 115 1 TRUE FALSE FALSE 12 2805 12 12.478 0.462 10 116 1 FALSE FALSE FALSE 11 2272 9 6.162 0.722 7 117 1 FALSE FALSE FALSE 12 2227 14 7.185 0.661 7 118 1 TRUE FALSE FALSE 12 1720 6 7.093 0.622 7 119 1 TRUE TRUE FALSE 17 2300 13 17.826 0.442 12 120 1 FALSE FALSE FALSE 10 3410 12 6.393 0.582 7 121 1 TRUE FALSE TRUE 13 2304 16 11.719 0.551 7 122 1 FALSE FALSE FALSE 11 1984 12 4.788 0.722 12 123 1 TRUE FALSE FALSE 12 1890 14 4.233 0.751 14 124 1 TRUE TRUE FALSE 16 1970 12 7.107 0.661 12 125 1 FALSE FALSE FALSE 17 2400 12 8.333 0.610 7 126 1 TRUE TRUE FALSE 12 2504 12 6.989 0.692 10 127 1 TRUE FALSE FALSE 16 2398 17 9.591 0.661 7 128 1 TRUE TRUE FALSE 12 1960 12 6.020 0.722 7 129 1 TRUE FALSE TRUE 16 2550 12 2.969 0.722 12 130 1 TRUE TRUE FALSE 8 2500 12 7.000 0.692 10 131 1 TRUE TRUE FALSE 12 2164 10 7.163 0.722 7 132 1 FALSE FALSE TRUE 12 2640 12 4.736 0.692 7 133 1 TRUE TRUE FALSE 12 1936 12 8.781 0.692 12 134 1 TRUE TRUE FALSE 13 2136 12 9.832 0.580 10 135 1 TRUE FALSE FALSE 11 1955 10 6.650 0.722 7 136 1 TRUE FALSE FALSE 12 1980 10 9.848 0.661 12 137 1 TRUE TRUE FALSE 12 2550 11 9.804 0.622 7 138 1 TRUE TRUE FALSE 14 2058 13 8.260 0.661 7 139 1 TRUE TRUE FALSE 12 2263 14 9.068 0.692 7 140 1 FALSE FALSE TRUE 12 1763 13 8.508 0.622 7 141 1 FALSE FALSE TRUE 12 2096 14 7.157 0.622 3 142 1 FALSE FALSE FALSE 17 2059 13 7.042 0.520 12 143 1 TRUE FALSE FALSE 14 1820 17 6.593 0.722 16 144 1 TRUE FALSE TRUE 12 2832 8 4.346 0.692 7 145 1 FALSE FALSE TRUE 9 1990 10 7.236 0.722 3 146 1 TRUE TRUE FALSE 12 2000 7 1.740 0.801 12 147 1 TRUE FALSE FALSE 12 1885 12 6.897 0.722 7 148 1 TRUE TRUE FALSE 12 2860 12 5.045 0.551 12 149 1 TRUE FALSE TRUE 14 1913 16 17.250 0.491 12 150 1 TRUE FALSE FALSE 16 1800 16 8.333 0.622 16 151 1 TRUE FALSE FALSE 17 2880 16 9.375 0.582 12 152 1 TRUE TRUE FALSE 15 1993 16 8.279 0.692 12 153 1 TRUE FALSE FALSE 12 2250 14 4.500 0.692 7 154 1 TRUE TRUE FALSE 16 2286 16 4.199 0.722 14 155 1 FALSE FALSE FALSE 17 1880 16 16.064 0.491 7 156 1 FALSE FALSE TRUE 17 2350 17 11.277 0.521 10 157 1 TRUE TRUE FALSE 12 3640 11 0.549 0.942 7 158 1 FALSE FALSE FALSE 16 1770 14 9.548 0.622 14 159 1 TRUE FALSE FALSE 13 1875 13 5.333 0.692 7 160 1 TRUE TRUE FALSE 12 2200 9 5.455 0.722 7 161 1 TRUE TRUE FALSE 11 2033 12 5.903 0.751 12 162 1 TRUE TRUE FALSE 16 2739 17 9.858 0.582 12 163 1 TRUE TRUE FALSE 14 1626 14 11.685 0.722 17 164 1 TRUE FALSE FALSE 16 2248 17 6.228 0.722 7 165 1 FALSE FALSE FALSE 12 2140 12 9.175 0.622 7 166 1 TRUE TRUE FALSE 9 1985 12 6.297 0.751 3 167 1 FALSE FALSE FALSE 17 1528 17 10.471 0.582 12 168 1 FALSE FALSE FALSE 14 1920 16 14.583 0.491 7 169 1 TRUE FALSE FALSE 12 1918 10 6.214 0.722 7 170 1 FALSE TRUE FALSE 12 2112 12 6.629 0.692 7 171 1 TRUE FALSE FALSE 11 2144 9 3.825 0.722 3 172 1 TRUE FALSE TRUE 12 1920 15 7.812 0.692 7 173 1 TRUE TRUE FALSE 12 2241 9 3.481 0.722 10 174 1 FALSE FALSE FALSE 10 880 11 5.550 0.751 10 175 1 TRUE TRUE FALSE 12 2070 16 11.594 0.622 12 176 1 FALSE FALSE FALSE 5 1050 5 9.524 0.722 7 177 1 TRUE FALSE FALSE 17 2635 17 7.280 0.622 14 178 1 FALSE TRUE FALSE 11 3000 8 3.000 0.722 10 179 1 TRUE FALSE TRUE 12 2500 11 10.400 0.622 7 180 1 TRUE FALSE FALSE 12 1990 12 10.050 0.661 7 181 1 TRUE TRUE FALSE 14 2390 13 8.787 0.661 10 182 1 TRUE TRUE FALSE 11 1430 12 10.140 0.692 12 183 1 TRUE FALSE FALSE 12 1800 8 5.000 0.692 12 184 1 TRUE TRUE FALSE 14 2103 17 11.888 0.582 7 185 1 TRUE FALSE FALSE 12 1350 12 13.333 0.661 7 186 1 TRUE TRUE FALSE 10 2880 17 8.681 0.582 7 187 1 FALSE TRUE FALSE 16 2400 17 7.292 0.610 12 188 1 TRUE FALSE FALSE 13 1135 12 3.524 0.722 7 189 1 TRUE TRUE FALSE 12 2750 17 6.182 0.722 12 190 1 FALSE TRUE FALSE 12 2085 16 2.975 0.751 12 191 1 FALSE FALSE TRUE 12 2600 8 4.577 0.722 12 192 1 TRUE TRUE FALSE 11 3542 12 3.106 0.751 10 193 1 TRUE FALSE FALSE 12 1975 12 5.823 0.751 12 194 1 TRUE TRUE FALSE 9 2400 12 7.500 0.622 10 195 1 TRUE TRUE FALSE 13 3000 14 8.333 0.622 12 196 1 TRUE FALSE FALSE 12 1960 14 9.235 0.661 12 197 1 TRUE TRUE FALSE 12 2000 9 5.500 0.661 12 198 1 FALSE FALSE TRUE 12 3000 12 6.000 0.661 7 199 1 TRUE FALSE FALSE 13 2400 12 6.667 0.622 12 200 1 TRUE FALSE FALSE 16 2450 14 13.061 0.521 12 201 1 TRUE FALSE FALSE 12 2423 8 12.794 0.521 12 202 1 TRUE TRUE FALSE 16 2000 16 12.000 0.580 12 203 1 FALSE TRUE FALSE 17 2526 16 6.730 0.582 16 204 1 FALSE FALSE TRUE 12 2695 12 6.679 0.692 7 205 1 FALSE FALSE TRUE 12 2048 12 4.394 0.692 16 206 1 TRUE FALSE FALSE 9 1920 11 4.688 0.751 7 207 1 TRUE FALSE FALSE 12 2338 9 7.699 0.661 7 208 1 TRUE TRUE FALSE 12 2945 8 4.329 0.692 10 209 1 FALSE FALSE FALSE 13 2047 12 6.742 0.661 12 210 1 FALSE TRUE FALSE 12 1668 7 9.352 0.622 10 211 1 FALSE FALSE TRUE 12 175 10 9.143 0.751 0 212 1 FALSE FALSE TRUE 12 1798 12 2.642 0.751 7 213 1 TRUE FALSE FALSE 12 1222 12 7.365 0.722 12 214 1 FALSE FALSE TRUE 10 1820 7 1.407 0.771 12 215 1 FALSE FALSE TRUE 12 1560 8 1.519 0.771 10 216 1 FALSE FALSE FALSE 16 2210 12 3.344 0.751 12 217 1 TRUE FALSE FALSE 12 2874 16 2.911 0.722 3 218 1 TRUE TRUE FALSE 11 2499 8 4.383 0.722 7 219 1 FALSE FALSE FALSE 12 3088 12 4.210 0.722 12 220 1 TRUE TRUE FALSE 10 2020 8 3.713 0.781 10 221 1 TRUE FALSE FALSE 12 1980 10 10.606 0.551 7 222 1 TRUE FALSE FALSE 12 1968 11 8.638 0.661 7 223 1 FALSE FALSE FALSE 12 2100 12 3.837 0.751 7 224 1 TRUE TRUE FALSE 12 2651 17 7.167 0.692 7 225 1 TRUE FALSE FALSE 16 1918 14 8.863 0.622 12 226 1 TRUE FALSE FALSE 17 2585 16 7.737 0.661 12 227 1 TRUE FALSE FALSE 12 2250 17 9.170 0.622 7 228 1 TRUE TRUE FALSE 17 2480 17 11.290 0.551 12 229 1 FALSE FALSE TRUE 12 2924 12 6.327 0.582 7 230 1 TRUE TRUE FALSE 12 1896 9 4.747 0.751 10 231 1 TRUE TRUE FALSE 12 2332 12 4.888 0.692 10 232 1 FALSE FALSE TRUE 8 3482 7 2.484 0.722 7 233 1 TRUE TRUE FALSE 12 2106 14 8.547 0.622 12 234 1 TRUE TRUE FALSE 13 1160 12 6.638 0.751 17 235 1 TRUE TRUE FALSE 12 2040 12 3.186 0.722 7 236 1 TRUE FALSE FALSE 12 2856 16 4.547 0.582 7 237 1 TRUE FALSE FALSE 8 950 8 12.789 0.751 7 238 1 FALSE FALSE TRUE 12 2068 10 6.528 0.661 7 239 1 TRUE TRUE FALSE 17 1896 17 16.403 0.551 12 240 1 FALSE FALSE TRUE 17 2000 16 7.000 0.610 14 241 1 FALSE FALSE FALSE 12 288 11 13.542 0.751 7 242 1 TRUE TRUE FALSE 13 2160 12 6.713 0.692 12 243 1 TRUE TRUE FALSE 12 3120 17 5.449 0.661 7 244 1 TRUE TRUE FALSE 12 1944 17 6.687 0.722 7 245 1 TRUE FALSE FALSE 12 2046 12 9.094 0.692 16 246 1 TRUE FALSE FALSE 12 2005 9 6.534 0.751 7 247 1 TRUE TRUE FALSE 9 2070 10 4.686 0.751 10 248 1 TRUE FALSE FALSE 10 3000 12 8.000 0.521 12 249 1 FALSE FALSE TRUE 12 2640 12 4.053 0.692 7 250 1 TRUE TRUE FALSE 16 2450 17 8.163 0.551 16 251 1 FALSE FALSE TRUE 13 1000 15 16.500 0.462 10 252 1 TRUE TRUE FALSE 8 2080 8 3.462 0.781 3 253 1 FALSE FALSE TRUE 16 2413 16 11.361 0.551 16 254 1 TRUE TRUE FALSE 13 2570 12 3.891 0.722 7 255 1 TRUE TRUE FALSE 12 2030 12 9.606 0.692 12 256 1 FALSE FALSE FALSE 11 4684 17 2.669 0.722 7 257 1 TRUE TRUE FALSE 13 2802 17 8.886 0.661 7 258 1 TRUE FALSE TRUE 12 2090 12 6.364 0.722 7 259 1 TRUE TRUE FALSE 12 2053 16 17.728 0.551 12 260 1 TRUE TRUE FALSE 10 1984 10 5.292 0.722 12 261 1 FALSE FALSE TRUE 12 2040 16 15.686 0.582 7 262 1 TRUE FALSE FALSE 17 2794 8 5.242 0.551 10 263 1 TRUE FALSE FALSE 15 3290 14 3.039 0.722 14 264 1 TRUE FALSE FALSE 16 1911 16 16.745 0.491 16 265 1 FALSE FALSE TRUE 10 2000 12 3.750 0.692 7 266 1 TRUE TRUE FALSE 11 2580 12 5.814 0.722 10 267 1 TRUE FALSE FALSE 12 2400 17 10.833 0.582 7 268 1 TRUE FALSE FALSE 12 1740 12 9.003 0.722 14 269 1 TRUE FALSE FALSE 14 2500 12 6.540 0.610 14 270 1 TRUE TRUE FALSE 16 1840 17 5.978 0.751 12 271 1 FALSE FALSE TRUE 14 2036 14 8.625 0.582 7 272 1 TRUE FALSE FALSE 8 3536 11 4.833 0.622 7 273 1 TRUE FALSE FALSE 7 880 11 10.909 0.751 3 274 1 TRUE TRUE FALSE 12 2007 12 3.886 0.751 7 275 1 FALSE FALSE FALSE 12 2632 13 8.457 0.551 7 276 1 FALSE FALSE FALSE 14 2600 12 5.000 0.692 7 277 1 TRUE TRUE FALSE 12 2156 13 7.421 0.661 12 278 1 TRUE TRUE FALSE 12 3625 12 4.494 0.692 10 279 1 FALSE TRUE FALSE 12 2420 12 7.025 0.622 7 280 1 TRUE FALSE FALSE 14 2080 9 3.279 0.751 3 281 1 FALSE FALSE TRUE 16 3443 17 1.286 0.722 12 282 1 TRUE TRUE FALSE 12 2250 12 5.556 0.722 7 283 1 TRUE TRUE FALSE 12 2535 16 7.495 0.582 12 284 1 FALSE FALSE FALSE 12 2352 15 22.109 0.442 7 285 1 TRUE FALSE FALSE 13 3036 6 9.058 0.442 10 286 1 FALSE FALSE TRUE 13 2600 14 11.538 0.521 7 287 1 FALSE TRUE FALSE 10 2223 7 6.047 0.692 0 288 1 TRUE TRUE FALSE 12 2666 10 1.365 0.751 7 289 1 TRUE FALSE FALSE 12 2006 17 16.550 0.551 10 290 1 TRUE FALSE FALSE 12 1710 17 8.772 0.640 9 291 1 FALSE TRUE FALSE 12 1920 14 9.635 0.661 12 292 1 FALSE TRUE FALSE 14 1647 14 6.679 0.722 12 293 1 TRUE TRUE FALSE 17 3080 15 7.573 0.442 12 294 1 FALSE FALSE FALSE 10 1920 10 7.812 0.661 3 295 1 TRUE TRUE FALSE 9 2420 4 1.984 0.751 9 296 1 FALSE FALSE FALSE 12 2205 15 9.524 0.622 12 297 1 TRUE TRUE FALSE 12 3035 14 6.425 0.661 12 298 1 FALSE FALSE TRUE 16 2185 15 10.984 0.622 14 299 1 FALSE FALSE TRUE 12 1880 12 12.766 0.521 7 300 1 TRUE TRUE FALSE 17 1863 17 9.555 0.610 12 301 1 FALSE FALSE FALSE 12 2456 16 5.497 0.661 12 302 1 TRUE FALSE FALSE 17 1847 17 9.762 0.692 12 303 1 TRUE TRUE FALSE 11 2000 6 5.000 0.751 7 304 1 TRUE TRUE FALSE 16 1856 14 6.000 0.722 12 305 1 TRUE TRUE FALSE 11 1880 14 6.915 0.692 10 306 1 TRUE TRUE FALSE 13 3020 12 4.967 0.722 12 307 1 TRUE TRUE FALSE 11 2646 8 2.390 0.692 7 308 1 TRUE FALSE FALSE 8 1640 9 4.512 0.751 7 309 1 FALSE FALSE TRUE 11 1950 11 9.231 0.661 3 310 1 TRUE FALSE FALSE 12 1920 12 11.458 0.582 12 311 1 TRUE TRUE FALSE 10 2025 12 7.699 0.661 7 312 1 FALSE FALSE TRUE 17 2470 17 4.183 0.661 16 313 1 TRUE TRUE FALSE 12 1800 12 8.333 0.622 12 314 1 FALSE FALSE TRUE 12 1920 8 5.104 0.692 12 315 1 TRUE FALSE FALSE 17 2039 17 8.227 0.640 7 316 1 TRUE FALSE FALSE 14 2570 16 5.663 0.661 14 317 1 FALSE FALSE FALSE 12 1914 12 5.222 0.722 7 318 1 TRUE FALSE FALSE 12 1516 10 7.190 0.751 7 319 1 FALSE FALSE FALSE 12 2520 16 13.907 0.491 12 320 1 TRUE FALSE FALSE 12 2327 12 6.016 0.722 10 321 1 FALSE FALSE FALSE 12 2188 12 7.678 0.622 7 322 1 FALSE FALSE FALSE 12 1864 12 5.311 0.751 3 323 1 TRUE TRUE FALSE 9 2183 10 4.123 0.751 7 324 1 FALSE FALSE FALSE 10 1920 12 7.812 0.622 7 325 1 TRUE TRUE FALSE 12 1824 12 4.605 0.722 10 326 1 TRUE FALSE TRUE 12 2878 12 3.975 0.661 7 327 1 TRUE FALSE TRUE 12 2390 16 15.063 0.521 7 328 1 TRUE TRUE FALSE 12 3120 13 4.506 0.622 12 329 1 TRUE TRUE FALSE 12 2040 12 5.882 0.661 12 330 1 TRUE TRUE FALSE 17 2151 15 10.228 0.582 12 331 1 TRUE TRUE FALSE 12 1976 14 6.579 0.661 10 332 1 FALSE FALSE FALSE 17 2286 16 8.815 0.582 14 333 1 FALSE FALSE TRUE 12 2032 10 2.953 0.751 7 334 1 TRUE FALSE FALSE 10 1680 4 4.517 0.751 7 335 1 TRUE FALSE FALSE 12 1560 14 5.128 0.751 14 336 1 TRUE TRUE FALSE 12 2895 12 4.145 0.722 10 337 1 FALSE FALSE FALSE 12 1820 8 8.791 0.661 10 338 1 TRUE FALSE FALSE 12 2450 12 7.306 0.692 7 339 1 TRUE TRUE FALSE 12 1748 8 6.007 0.692 7 340 1 TRUE FALSE FALSE 12 1020 12 8.333 0.751 7 341 1 TRUE FALSE FALSE 16 2342 17 11.529 0.582 14 342 1 FALSE FALSE TRUE 13 2250 12 6.667 0.661 7 343 1 TRUE TRUE FALSE 13 2880 16 8.681 0.551 12 344 1 TRUE FALSE FALSE 12 2032 12 3.468 0.722 14 345 1 TRUE TRUE FALSE 16 3120 16 3.032 0.722 14 346 1 FALSE FALSE TRUE 17 1760 16 13.807 0.582 14 347 1 FALSE FALSE FALSE 12 1725 12 6.217 0.661 0 348 1 TRUE FALSE FALSE 14 2080 13 4.231 0.751 16 349 1 TRUE FALSE TRUE 12 2040 10 11.470 0.661 7 350 1 FALSE TRUE FALSE 17 2940 17 12.245 0.500 12 351 1 FALSE FALSE FALSE 12 2280 12 9.210 0.582 7 352 1 TRUE TRUE FALSE 14 2164 16 6.331 0.722 10 353 1 TRUE FALSE FALSE 12 1999 12 7.804 0.692 7 354 1 TRUE TRUE FALSE 12 1824 13 7.675 0.722 10 355 1 TRUE FALSE FALSE 17 2182 16 15.582 0.551 10 356 1 TRUE TRUE FALSE 16 2385 16 9.644 0.661 14 357 1 FALSE FALSE FALSE 16 2460 17 12.886 0.551 7 358 1 TRUE TRUE FALSE 12 2595 16 6.551 0.692 12 359 1 FALSE FALSE TRUE 9 2400 10 8.250 0.661 7 360 1 TRUE TRUE FALSE 12 3120 8 1.379 0.791 7 361 1 TRUE FALSE FALSE 12 2850 12 5.263 0.692 12 362 1 FALSE FALSE TRUE 16 760 12 10.526 0.720 14 363 1 TRUE TRUE FALSE 14 2500 17 10.000 0.622 12 364 1 TRUE TRUE FALSE 12 2630 14 12.548 0.521 7 365 1 TRUE FALSE FALSE 12 2597 8 4.621 0.722 7 366 1 FALSE FALSE TRUE 11 2760 11 1.854 0.692 7 367 1 TRUE FALSE FALSE 12 2070 8 7.427 0.722 12 368 1 TRUE FALSE FALSE 16 2256 16 13.298 0.582 16 369 1 TRUE FALSE FALSE 17 1505 16 26.578 0.500 3 370 1 TRUE FALSE TRUE 17 2364 17 13.959 0.491 16 371 1 TRUE TRUE FALSE 14 2895 14 13.126 0.622 7 372 1 TRUE FALSE FALSE 12 2041 12 7.888 0.661 16 373 1 TRUE TRUE FALSE 14 2195 17 8.656 0.661 7 374 1 TRUE TRUE FALSE 12 1935 12 3.411 0.722 10 375 1 TRUE TRUE FALSE 10 1950 10 4.626 0.751 10 376 1 TRUE TRUE FALSE 12 2375 14 4.210 0.751 10 377 1 TRUE FALSE FALSE 13 1920 8 4.271 0.771 10 378 1 TRUE TRUE FALSE 16 3300 12 3.342 0.692 12 379 1 TRUE TRUE FALSE 12 3680 16 8.636 0.491 10 380 1 TRUE FALSE FALSE 7 1968 12 6.758 0.692 7 381 1 TRUE TRUE FALSE 16 2504 10 0.584 0.771 16 382 1 TRUE FALSE FALSE 14 2000 14 5.335 0.692 7 383 1 FALSE TRUE FALSE 12 1656 12 4.710 0.722 7 384 1 TRUE TRUE FALSE 10 1968 7 4.980 0.722 7 385 1 TRUE FALSE FALSE 12 2016 12 2.976 0.722 7 386 1 FALSE FALSE FALSE 16 2602 17 12.744 0.442 7 387 1 TRUE TRUE FALSE 10 1560 9 3.846 0.751 10 388 1 TRUE TRUE FALSE 12 1827 10 7.223 0.751 10 389 1 FALSE TRUE FALSE 14 2080 14 8.808 0.661 12 390 1 FALSE FALSE FALSE 12 3390 12 5.386 0.622 7 391 1 FALSE FALSE TRUE 6 2524 6 3.685 0.722 7 392 1 TRUE TRUE FALSE 15 2777 12 1.844 0.722 7 393 1 FALSE FALSE TRUE 12 3120 13 1.301 0.722 7 394 1 TRUE TRUE FALSE 17 2700 17 7.222 0.692 14 395 1 FALSE FALSE TRUE 14 1904 12 7.353 0.661 14 396 1 TRUE TRUE FALSE 13 2360 14 11.864 0.622 7 397 1 FALSE FALSE TRUE 6 1960 8 5.333 0.722 7 398 1 FALSE TRUE FALSE 16 2000 16 5.500 0.661 7 399 1 TRUE FALSE FALSE 14 2600 16 7.231 0.521 16 400 1 TRUE TRUE FALSE 15 2000 9 7.000 0.640 12 401 1 TRUE FALSE FALSE 14 2218 14 7.214 0.692 7 402 1 TRUE FALSE FALSE 8 2000 5 4.500 0.751 7 403 1 FALSE FALSE TRUE 14 2595 16 8.863 0.622 12 404 1 TRUE FALSE FALSE 12 2400 12 9.333 0.661 12 405 1 TRUE FALSE FALSE 12 2856 12 3.826 0.661 12 406 1 TRUE TRUE FALSE 12 2601 14 5.445 0.661 10 407 1 TRUE FALSE FALSE 12 2054 9 8.763 0.622 3 408 1 TRUE TRUE FALSE 12 2500 12 8.400 0.661 12 409 1 FALSE TRUE FALSE 12 1960 12 5.102 0.722 12 410 1 FALSE FALSE FALSE 8 2058 8 7.410 0.692 12 411 1 TRUE TRUE FALSE 12 2410 10 5.353 0.692 7 412 1 TRUE FALSE FALSE 17 1278 17 19.562 0.661 16 413 1 TRUE TRUE FALSE 12 2875 12 5.078 0.692 12 414 1 FALSE FALSE FALSE 12 2340 12 0.513 0.801 10 415 1 TRUE TRUE FALSE 14 3060 17 5.402 0.582 10 416 1 TRUE TRUE FALSE 13 1920 12 7.865 0.722 12 417 1 TRUE TRUE FALSE 17 3390 12 6.053 0.551 7 418 1 TRUE TRUE FALSE 8 2400 12 5.000 0.751 7 419 1 TRUE TRUE FALSE 12 1640 11 7.683 0.751 7 420 1 TRUE TRUE FALSE 11 1656 8 6.039 0.722 7 421 1 TRUE FALSE FALSE 12 1920 16 5.851 0.722 7 422 1 TRUE FALSE FALSE 12 1780 10 6.742 0.722 7 423 1 TRUE TRUE FALSE 17 1850 17 8.108 0.722 7 424 1 TRUE TRUE FALSE 10 3430 12 5.306 0.722 7 425 1 TRUE FALSE FALSE 12 2008 8 7.271 0.622 7 426 1 TRUE FALSE FALSE 13 2140 11 8.178 0.582 7 427 1 TRUE TRUE FALSE 12 3380 12 7.101 0.582 12 428 1 TRUE TRUE FALSE 12 2430 11 6.584 0.692 12 429 0 TRUE FALSE FALSE 12 2550 15 7.853 0.661 14 430 0 TRUE TRUE FALSE 16 1928 16 11.929 0.661 14 431 0 TRUE TRUE FALSE 12 1100 17 18.000 0.661 12 432 0 TRUE FALSE FALSE 12 3193 16 10.022 0.582 7 433 0 TRUE FALSE FALSE 12 2250 16 9.333 0.661 7 434 0 TRUE FALSE FALSE 12 2012 13 6.085 0.751 7 435 0 TRUE FALSE FALSE 13 3856 15 5.705 0.661 10 436 0 FALSE FALSE TRUE 12 1645 12 9.118 0.722 7 437 0 FALSE FALSE TRUE 12 1554 12 7.207 0.722 9 438 0 TRUE TRUE FALSE 10 2352 8 5.315 0.751 12 439 0 FALSE FALSE TRUE 12 1980 12 8.283 0.692 12 440 0 TRUE TRUE FALSE 12 2352 7 7.058 0.722 10 441 0 TRUE FALSE FALSE 7 1784 6 6.166 0.751 7 442 0 FALSE FALSE TRUE 12 2500 12 2.700 0.722 7 443 0 TRUE TRUE FALSE 9 2088 14 7.663 0.722 7 444 0 TRUE FALSE FALSE 12 4640 12 1.849 0.751 9 445 0 TRUE FALSE FALSE 10 3900 12 3.846 0.722 7 446 0 TRUE TRUE FALSE 14 1988 17 9.054 0.722 3 447 0 TRUE FALSE FALSE 14 1920 10 9.375 0.722 10 448 0 TRUE FALSE FALSE 12 2400 12 8.333 0.661 12 449 0 TRUE TRUE FALSE 12 1867 12 9.373 0.692 12 450 0 TRUE TRUE FALSE 17 3570 17 4.482 0.722 12 451 0 TRUE TRUE FALSE 8 2805 12 5.348 0.722 7 452 0 TRUE TRUE FALSE 12 1110 15 3.203 0.781 7 453 0 TRUE TRUE FALSE 17 2695 16 12.801 0.551 10 454 0 FALSE FALSE TRUE 12 1950 16 6.910 0.622 10 455 0 TRUE TRUE FALSE 12 2128 12 4.229 0.771 7 456 0 TRUE TRUE FALSE 12 3260 12 4.448 0.722 12 457 0 TRUE FALSE TRUE 9 1987 12 8.052 0.722 7 458 0 FALSE TRUE FALSE 11 2185 11 7.506 0.692 10 459 0 TRUE TRUE FALSE 12 2475 12 7.475 0.692 10 460 0 FALSE FALSE TRUE 12 2610 12 6.322 0.692 16 461 0 TRUE TRUE FALSE 9 1920 12 2.135 0.801 7 462 0 TRUE TRUE FALSE 11 2352 14 4.209 0.771 7 463 0 TRUE TRUE FALSE 12 3160 12 0.759 0.942 12 464 0 TRUE TRUE FALSE 9 1040 13 4.808 0.791 12 465 0 TRUE FALSE TRUE 12 3120 12 12.788 0.521 10 466 0 TRUE TRUE FALSE 17 2240 16 7.143 0.722 12 467 0 TRUE TRUE FALSE 12 1980 16 15.152 0.582 10 468 0 FALSE FALSE TRUE 14 1960 14 7.908 0.722 12 469 0 TRUE TRUE FALSE 12 2940 17 6.973 0.692 12 470 0 FALSE FALSE TRUE 12 2467 11 4.918 0.582 7 471 0 TRUE FALSE TRUE 10 2256 12 8.311 0.692 10 472 0 TRUE FALSE FALSE 12 1680 12 7.143 0.722 7 473 0 FALSE FALSE TRUE 12 2250 12 5.689 0.722 7 474 0 TRUE FALSE FALSE 10 2400 9 7.083 0.722 3 475 0 TRUE TRUE FALSE 12 2196 11 5.692 0.722 10 476 0 TRUE TRUE FALSE 13 2400 12 11.400 0.622 7 477 0 TRUE FALSE FALSE 12 3825 11 1.415 0.801 3 478 0 TRUE TRUE FALSE 8 2860 9 2.751 0.781 12 479 0 TRUE TRUE FALSE 12 2750 12 6.618 0.722 12 480 0 FALSE FALSE FALSE 13 2103 16 9.273 0.692 7 481 0 FALSE FALSE FALSE 12 1880 12 12.287 0.622 12 482 0 TRUE TRUE FALSE 12 3185 13 2.855 0.751 7 483 0 TRUE TRUE FALSE 13 2677 17 7.971 0.521 12 484 0 TRUE TRUE FALSE 13 3600 17 19.444 0.442 12 485 0 FALSE FALSE TRUE 8 4334 8 1.481 0.751 12 486 0 FALSE FALSE FALSE 12 2874 8 1.892 0.751 7 487 0 TRUE FALSE TRUE 8 1936 9 6.921 0.722 7 488 0 FALSE FALSE FALSE 14 1964 8 10.692 0.661 12 489 0 FALSE FALSE FALSE 9 1900 8 9.947 0.692 7 490 0 TRUE TRUE FALSE 16 2500 17 19.000 0.462 7 491 0 TRUE TRUE FALSE 12 3173 14 7.488 0.661 14 492 0 TRUE TRUE FALSE 16 2916 17 9.259 0.622 16 493 0 FALSE FALSE FALSE 12 2208 12 11.322 0.622 12 494 0 TRUE FALSE FALSE 12 2094 10 6.447 0.751 12 495 0 TRUE TRUE FALSE 12 2250 12 6.578 0.722 7 496 0 TRUE FALSE FALSE 12 2000 12 6.100 0.751 12 497 0 TRUE FALSE FALSE 11 2600 10 4.231 0.781 10 498 0 FALSE FALSE TRUE 12 4368 8 2.217 0.722 7 499 0 TRUE TRUE FALSE 13 3068 16 6.519 0.692 12 500 0 TRUE FALSE TRUE 12 2218 11 6.492 0.722 7 501 0 FALSE FALSE TRUE 12 1848 12 11.364 0.661 12 502 0 FALSE TRUE FALSE 16 2430 17 9.877 0.661 12 503 0 TRUE FALSE FALSE 16 2640 17 7.658 0.692 7 504 0 TRUE FALSE FALSE 12 2108 16 8.302 0.692 7 505 0 TRUE FALSE FALSE 12 1998 12 14.114 0.622 14 506 0 FALSE FALSE TRUE 14 2500 16 16.000 0.521 7 507 0 FALSE FALSE TRUE 14 1665 17 11.291 0.692 12 508 0 TRUE FALSE FALSE 12 2990 12 2.340 0.722 12 509 0 TRUE TRUE FALSE 13 1795 16 7.799 0.722 12 510 0 FALSE FALSE TRUE 12 2500 12 6.000 0.722 7 511 0 TRUE TRUE FALSE 11 2205 11 6.689 0.751 10 512 0 TRUE FALSE FALSE 12 2460 14 8.537 0.692 12 513 0 TRUE FALSE TRUE 15 1880 16 13.830 0.622 0 514 0 FALSE FALSE TRUE 7 3481 9 1.480 0.751 7 515 0 TRUE FALSE FALSE 12 2450 12 8.571 0.661 7 516 0 TRUE TRUE FALSE 12 2062 14 8.230 0.722 7 517 0 FALSE FALSE TRUE 12 2146 14 8.388 0.692 7 518 0 FALSE FALSE TRUE 12 1575 7 7.365 0.722 10 519 0 TRUE TRUE FALSE 13 3096 12 3.442 0.722 7 520 0 FALSE FALSE TRUE 12 3280 11 3.354 0.751 7 521 0 FALSE FALSE TRUE 10 1680 8 2.976 0.781 0 522 0 TRUE TRUE FALSE 12 2625 11 5.181 0.751 14 523 0 TRUE TRUE FALSE 14 1846 16 10.293 0.692 12 524 0 TRUE FALSE FALSE 12 2178 10 13.243 0.582 10 525 0 TRUE TRUE FALSE 10 960 9 3.646 0.942 7 526 0 FALSE FALSE TRUE 11 2210 12 10.407 0.661 7 527 0 TRUE FALSE FALSE 12 2192 12 8.212 0.692 7 528 0 TRUE TRUE FALSE 12 1960 16 8.674 0.722 7 529 0 FALSE FALSE TRUE 12 1920 10 8.321 0.692 7 530 0 TRUE TRUE FALSE 8 2286 12 4.287 0.751 0 531 0 TRUE FALSE TRUE 7 2000 7 10.500 0.521 3 532 0 TRUE TRUE FALSE 16 2256 12 7.979 0.692 12 533 0 TRUE TRUE FALSE 14 2370 17 11.814 0.622 12 534 0 TRUE TRUE FALSE 12 1800 13 6.111 0.751 12 535 0 TRUE TRUE FALSE 16 2250 16 12.578 0.622 12 536 0 TRUE FALSE FALSE 12 1080 12 11.244 0.751 7 537 0 TRUE TRUE FALSE 10 2840 12 2.741 0.781 7 538 0 FALSE FALSE TRUE 7 2250 11 4.444 0.751 0 539 0 TRUE TRUE FALSE 12 2746 12 5.827 0.722 12 540 0 TRUE FALSE TRUE 10 2300 8 2.348 0.781 12 541 0 FALSE FALSE TRUE 8 2860 8 4.476 0.722 0 542 0 TRUE FALSE FALSE 11 1765 12 8.499 0.722 7 543 0 TRUE FALSE FALSE 15 2520 16 7.183 0.722 14 544 0 TRUE FALSE TRUE 12 2208 12 10.870 0.661 12 545 0 TRUE FALSE FALSE 12 2119 8 7.122 0.722 10 546 0 TRUE FALSE FALSE 13 2580 17 10.659 0.582 12 547 0 TRUE TRUE FALSE 9 1984 12 3.276 0.791 3 548 0 FALSE FALSE TRUE 12 1880 8 7.752 0.722 10 549 0 TRUE FALSE TRUE 12 2185 12 11.945 0.622 3 550 0 TRUE FALSE FALSE 12 2080 12 4.615 0.751 7 551 0 TRUE FALSE TRUE 12 1920 12 13.021 0.622 12 552 0 TRUE TRUE FALSE 6 3000 5 2.667 0.751 7 553 0 TRUE TRUE FALSE 12 2100 12 8.833 0.582 12 554 0 FALSE FALSE FALSE 12 1690 12 2.367 0.791 16 555 0 TRUE FALSE TRUE 12 2600 16 5.000 0.751 7 556 0 TRUE FALSE FALSE 12 1984 16 7.460 0.722 10 557 0 FALSE FALSE FALSE 12 2064 12 7.752 0.722 7 558 0 TRUE FALSE FALSE 12 2553 16 5.405 0.751 12 559 0 FALSE FALSE TRUE 8 2776 9 6.124 0.692 9 560 0 TRUE TRUE FALSE 12 2315 13 8.337 0.692 7 561 0 TRUE FALSE TRUE 12 1880 12 7.128 0.722 7 562 0 TRUE FALSE FALSE 7 2160 7 6.120 0.692 0 563 0 FALSE FALSE TRUE 15 900 12 3.111 0.751 7 564 0 TRUE FALSE FALSE 12 2467 16 12.161 0.582 7 565 0 FALSE FALSE FALSE 6 1820 12 5.385 0.751 7 566 0 TRUE FALSE FALSE 12 2223 10 3.984 0.751 10 567 0 TRUE FALSE TRUE 12 2142 12 7.470 0.722 12 568 0 FALSE FALSE FALSE 12 1928 11 11.411 0.661 7 569 0 TRUE FALSE FALSE 12 2783 17 12.936 0.551 12 570 0 TRUE TRUE FALSE 12 1960 12 5.612 0.751 12 571 0 TRUE FALSE FALSE 12 1920 12 6.810 0.751 7 572 0 TRUE TRUE FALSE 12 1587 12 8.507 0.722 7 573 0 TRUE TRUE FALSE 12 2496 10 5.508 0.661 7 574 0 TRUE TRUE FALSE 17 2280 12 7.229 0.692 12 575 0 TRUE TRUE FALSE 16 2750 17 9.454 0.622 16 576 0 FALSE FALSE TRUE 12 2115 12 8.983 0.692 7 577 0 TRUE FALSE FALSE 11 2590 12 5.985 0.722 12 578 0 TRUE TRUE FALSE 12 2372 12 3.963 0.751 12 579 0 FALSE FALSE FALSE 10 2295 12 7.058 0.582 7 580 0 TRUE TRUE FALSE 10 2096 12 7.157 0.722 10 581 0 TRUE TRUE FALSE 12 3315 17 4.014 0.661 12 582 0 TRUE TRUE FALSE 14 1777 16 3.883 0.722 10 583 0 TRUE FALSE FALSE 10 1880 12 7.979 0.722 7 584 0 FALSE FALSE FALSE 12 2184 10 5.037 0.751 12 585 0 TRUE TRUE FALSE 16 3250 16 7.077 0.661 12 586 0 FALSE FALSE TRUE 5 1520 5 3.618 0.771 7 587 0 TRUE TRUE FALSE 12 3119 13 13.466 0.491 10 588 0 FALSE TRUE FALSE 12 640 17 14.531 0.722 12 589 0 TRUE FALSE FALSE 12 2250 12 6.800 0.722 12 590 0 FALSE FALSE TRUE 13 3000 13 6.667 0.661 7 591 0 FALSE FALSE TRUE 8 2028 8 5.917 0.722 7 592 0 TRUE TRUE FALSE 12 2412 14 10.240 0.661 12 593 0 FALSE FALSE TRUE 8 2452 12 8.157 0.692 7 594 0 TRUE FALSE FALSE 8 2304 12 6.678 0.722 3 595 0 TRUE FALSE FALSE 12 3120 12 0.962 0.942 7 596 0 TRUE FALSE TRUE 8 1670 8 16.907 0.442 7 597 0 TRUE FALSE FALSE 12 2137 15 13.570 0.622 10 598 0 FALSE FALSE TRUE 11 2071 11 8.933 0.692 7 599 0 TRUE FALSE FALSE 13 1960 7 4.737 0.722 17 600 0 TRUE TRUE FALSE 8 2068 12 8.511 0.722 7 601 0 TRUE FALSE FALSE 12 2190 12 5.251 0.751 7 602 0 TRUE TRUE FALSE 15 2295 13 21.786 0.442 12 603 0 TRUE TRUE FALSE 12 2970 13 7.912 0.661 12 604 0 TRUE TRUE FALSE 10 2068 8 7.108 0.751 7 605 0 TRUE TRUE FALSE 13 2419 14 5.043 0.771 7 606 0 TRUE TRUE FALSE 12 2150 11 2.244 0.781 12 607 0 TRUE FALSE FALSE 11 1152 10 8.507 0.751 7 608 0 TRUE FALSE FALSE 12 2640 12 7.576 0.692 7 609 0 TRUE FALSE FALSE 11 2550 11 5.882 0.751 7 610 0 FALSE FALSE FALSE 13 1360 16 40.441 0.442 9 611 0 TRUE TRUE FALSE 12 2420 12 6.234 0.722 12 612 0 TRUE FALSE FALSE 11 2205 14 9.297 0.692 7 613 0 TRUE FALSE FALSE 12 3268 12 0.704 0.942 12 614 0 FALSE FALSE TRUE 12 3672 12 2.542 0.692 3 615 0 TRUE FALSE FALSE 12 1800 12 5.083 0.751 12 616 0 TRUE FALSE FALSE 10 1926 14 7.684 0.722 7 617 0 TRUE TRUE FALSE 7 1920 7 4.062 0.781 7 618 0 TRUE FALSE FALSE 12 2080 12 6.511 0.661 12 619 0 TRUE FALSE FALSE 12 2856 12 2.951 0.722 7 620 0 TRUE FALSE FALSE 12 2115 10 5.674 0.751 12 621 0 TRUE FALSE FALSE 12 1880 12 12.766 0.661 12 622 0 FALSE FALSE TRUE 11 2000 12 6.000 0.751 7 623 0 TRUE TRUE FALSE 12 2044 15 9.296 0.692 7 624 0 TRUE FALSE FALSE 10 1677 11 5.132 0.751 7 625 0 TRUE TRUE FALSE 11 2184 8 9.066 0.692 7 626 0 FALSE FALSE TRUE 16 3185 13 11.931 0.442 7 627 0 FALSE FALSE FALSE 10 2680 10 6.716 0.692 10 628 0 TRUE FALSE FALSE 14 3615 14 4.585 0.692 14 629 0 FALSE FALSE TRUE 11 2139 12 9.257 0.692 7 630 0 TRUE FALSE FALSE 12 3080 12 8.566 0.622 10 631 0 FALSE FALSE TRUE 5 1261 6 6.741 0.751 3 632 0 TRUE FALSE FALSE 10 2227 10 9.879 0.692 7 633 0 TRUE FALSE TRUE 16 1920 16 9.375 0.661 12 634 0 FALSE FALSE TRUE 12 2350 12 6.277 0.722 7 635 0 TRUE TRUE FALSE 11 1785 10 6.424 0.751 7 636 0 FALSE FALSE FALSE 12 2598 12 8.372 0.661 7 637 0 FALSE FALSE TRUE 12 2455 17 12.220 0.582 7 638 0 TRUE FALSE TRUE 12 2450 13 3.561 0.722 10 639 0 TRUE TRUE FALSE 12 1218 8 8.867 0.751 7 640 0 TRUE FALSE TRUE 6 2040 4 1.851 0.942 7 641 0 TRUE TRUE FALSE 14 2600 14 8.077 0.551 16 642 0 TRUE TRUE FALSE 12 2450 16 8.980 0.661 12 643 0 FALSE FALSE TRUE 12 2717 12 6.662 0.661 7 644 0 FALSE FALSE TRUE 16 2701 17 11.107 0.582 16 645 0 TRUE TRUE FALSE 12 2600 17 14.077 0.551 7 646 0 FALSE FALSE FALSE 12 3640 10 0.412 0.942 7 647 0 TRUE TRUE FALSE 17 2940 16 23.810 0.442 12 648 0 FALSE FALSE FALSE 12 1880 12 9.575 0.692 7 649 0 FALSE FALSE FALSE 12 3500 12 1.929 0.722 7 650 0 FALSE FALSE TRUE 9 3320 8 2.578 0.722 7 651 0 FALSE FALSE TRUE 12 1605 12 7.227 0.722 7 652 0 TRUE TRUE FALSE 12 2500 14 8.800 0.661 12 653 0 TRUE FALSE FALSE 12 2400 9 7.750 0.661 7 654 0 TRUE TRUE FALSE 12 1634 17 14.688 0.661 12 655 0 TRUE TRUE FALSE 12 2260 12 10.487 0.661 7 656 0 FALSE FALSE TRUE 12 3478 12 4.025 0.722 12 657 0 TRUE FALSE FALSE 14 2550 16 11.765 0.622 14 658 0 TRUE FALSE TRUE 10 840 8 9.524 0.751 7 659 0 FALSE FALSE FALSE 12 1520 12 6.908 0.722 7 660 0 TRUE FALSE FALSE 9 1920 9 5.151 0.751 12 661 0 FALSE FALSE TRUE 14 2703 17 20.496 0.442 16 662 0 TRUE TRUE FALSE 16 1896 16 13.555 0.661 12 663 0 FALSE FALSE TRUE 11 1960 13 14.796 0.582 7 664 0 TRUE TRUE FALSE 12 3060 8 1.690 0.781 10 665 0 TRUE TRUE FALSE 12 2805 12 7.230 0.521 7 666 0 FALSE FALSE TRUE 12 1944 7 4.115 0.751 3 667 0 FALSE FALSE FALSE 12 1960 12 2.999 0.771 3 668 0 TRUE FALSE FALSE 12 2112 8 8.049 0.692 7 669 0 TRUE TRUE FALSE 11 2544 12 8.577 0.622 12 670 0 TRUE FALSE TRUE 12 1700 17 9.323 0.582 10 671 0 TRUE TRUE FALSE 12 2550 12 4.706 0.751 3 672 0 TRUE FALSE FALSE 17 2080 17 9.135 0.661 16 673 0 TRUE FALSE FALSE 10 2060 12 7.767 0.722 10 674 0 TRUE FALSE FALSE 11 1955 8 4.127 0.751 7 675 0 TRUE FALSE FALSE 14 2500 17 10.000 0.661 17 676 0 FALSE FALSE TRUE 12 2750 10 4.073 0.751 7 677 0 FALSE FALSE TRUE 8 2040 5 7.853 0.582 3 678 0 TRUE TRUE FALSE 13 3275 15 7.817 0.661 12 679 0 TRUE TRUE FALSE 12 2400 12 9.167 0.661 7 680 0 FALSE FALSE TRUE 16 2024 17 29.644 0.442 12 681 0 TRUE FALSE FALSE 8 1840 6 4.076 0.791 3 682 0 TRUE TRUE FALSE 9 2033 17 8.362 0.722 10 683 0 TRUE FALSE FALSE 16 1946 17 12.062 0.442 16 684 0 TRUE TRUE FALSE 12 3660 12 2.325 0.751 10 685 0 FALSE FALSE TRUE 12 2088 11 10.010 0.661 7 686 0 TRUE TRUE FALSE 12 2048 12 5.273 0.751 7 687 0 FALSE FALSE TRUE 15 1920 15 10.417 0.692 14 688 0 FALSE FALSE FALSE 12 2000 12 7.500 0.722 7 689 0 TRUE FALSE FALSE 9 2204 8 6.352 0.722 10 690 0 TRUE FALSE FALSE 9 3157 10 3.807 0.722 7 691 0 TRUE FALSE FALSE 12 1665 14 9.910 0.722 3 692 0 FALSE FALSE TRUE 16 2304 11 2.670 0.722 12 693 0 FALSE FALSE FALSE 9 2275 8 4.396 0.751 7 694 0 TRUE TRUE FALSE 15 2760 17 7.971 0.661 12 695 0 TRUE FALSE FALSE 12 1750 14 15.429 0.582 7 696 0 TRUE TRUE FALSE 12 3366 13 1.985 0.722 7 697 0 TRUE FALSE FALSE 15 2205 16 7.256 0.722 10 698 0 TRUE FALSE FALSE 12 1990 9 7.035 0.722 3 699 0 TRUE FALSE FALSE 17 1930 16 8.135 0.692 14 700 0 FALSE FALSE TRUE 12 1350 12 11.981 0.551 12 701 0 TRUE FALSE TRUE 12 3340 12 5.992 0.661 7 702 0 TRUE FALSE TRUE 10 960 10 4.146 0.751 14 703 0 TRUE FALSE FALSE 13 2732 13 8.510 0.622 12 704 0 FALSE FALSE TRUE 12 1624 8 2.963 0.751 0 705 0 TRUE TRUE FALSE 11 1804 12 8.815 0.622 7 706 0 TRUE TRUE FALSE 8 2805 10 2.495 0.781 7 707 0 FALSE FALSE TRUE 12 2160 8 4.884 0.751 10 708 0 TRUE FALSE FALSE 16 2052 15 10.200 0.661 12 709 0 TRUE TRUE FALSE 12 2250 16 10.000 0.661 12 710 0 TRUE TRUE FALSE 12 1120 12 5.357 0.801 12 711 0 TRUE TRUE FALSE 12 2450 14 9.796 0.661 14 712 0 FALSE FALSE TRUE 10 3432 11 1.374 0.771 7 713 0 TRUE FALSE FALSE 12 2700 12 3.463 0.722 7 714 0 TRUE FALSE TRUE 12 2817 13 5.041 0.722 3 715 0 TRUE TRUE FALSE 15 3000 17 16.667 0.442 7 716 0 FALSE FALSE FALSE 10 2125 11 4.329 0.751 7 717 0 TRUE FALSE FALSE 14 1864 16 10.193 0.692 7 718 0 TRUE FALSE FALSE 12 2400 12 7.583 0.722 7 719 0 FALSE FALSE FALSE 8 2160 7 4.120 0.751 3 720 0 TRUE TRUE FALSE 8 1040 7 4.808 0.942 3 721 0 TRUE TRUE FALSE 12 2450 16 3.153 0.751 7 722 0 TRUE TRUE FALSE 12 2500 16 19.000 0.462 7 723 0 TRUE FALSE FALSE 16 2131 17 6.053 0.622 12 724 0 TRUE FALSE FALSE 12 2165 7 3.788 0.771 7 725 0 TRUE FALSE FALSE 5 2230 3 2.929 0.781 0 726 0 FALSE FALSE TRUE 8 1995 12 9.297 0.692 3 727 0 FALSE FALSE TRUE 13 2025 16 9.383 0.661 7 728 0 TRUE FALSE FALSE 12 2450 15 26.531 0.442 7 729 0 TRUE FALSE FALSE 12 2160 12 40.509 0.442 10 730 0 TRUE TRUE FALSE 14 1715 17 15.160 0.622 12 731 0 TRUE FALSE FALSE 12 3018 16 18.964 0.442 7 732 0 TRUE TRUE FALSE 12 2216 16 5.415 0.751 10 733 0 TRUE FALSE TRUE 12 2499 12 1.999 0.751 12 734 0 FALSE TRUE FALSE 12 2250 14 8.000 0.692 10 735 0 TRUE FALSE FALSE 14 2116 17 11.153 0.661 12 736 0 TRUE TRUE FALSE 12 2016 14 5.952 0.751 12 737 0 FALSE FALSE TRUE 12 2470 10 2.119 0.751 10 738 0 FALSE FALSE FALSE 9 1640 12 3.838 0.771 7 739 0 TRUE TRUE FALSE 14 2016 16 17.361 0.491 16 740 0 FALSE FALSE FALSE 11 2185 12 8.009 0.692 7 741 0 TRUE TRUE FALSE 12 800 14 3.000 0.801 12 742 0 TRUE FALSE FALSE 12 3022 12 10.589 0.582 7 743 0 TRUE FALSE FALSE 11 1512 14 10.913 0.722 10 744 0 TRUE FALSE TRUE 12 2677 12 5.603 0.722 0 745 0 TRUE FALSE FALSE 10 3150 12 7.936 0.661 3 746 0 TRUE TRUE FALSE 12 1430 12 2.948 0.942 7 747 0 TRUE TRUE FALSE 10 3307 4 2.056 0.791 7 748 0 TRUE TRUE FALSE 12 3120 12 1.301 0.791 7 749 0 TRUE FALSE FALSE 13 3020 16 9.271 0.622 10 750 0 TRUE TRUE FALSE 12 2056 12 4.864 0.771 12 751 0 FALSE FALSE FALSE 12 2383 12 1.090 0.751 10 752 0 FALSE FALSE TRUE 12 1705 8 12.440 0.622 12 753 0 TRUE TRUE FALSE 9 3120 12 6.090 0.692 7 > model.matrix( lfpResult ) (Intercept) kidsTRUE age30.39TRUE age50.60TRUE educ hushrs huseduc huswage 1 1 1 1 0 12 2708 12 4.029 2 1 1 1 0 12 2310 9 8.442 3 1 1 1 0 12 3072 12 3.581 4 1 1 1 0 12 1920 10 3.542 5 1 1 1 0 14 2000 12 10.000 6 1 0 0 1 12 1040 11 6.711 7 1 1 1 0 16 2670 12 3.428 8 1 0 0 1 12 4120 8 2.549 9 1 1 0 0 12 1995 4 4.221 10 1 1 1 0 12 2100 12 5.714 11 1 1 1 0 12 2450 12 9.796 12 1 1 0 0 11 2375 14 8.000 13 1 1 1 0 12 2830 16 5.300 14 1 1 0 0 12 3317 12 4.341 15 1 1 0 0 10 2024 17 10.870 16 1 1 1 0 11 1694 12 9.150 17 1 1 0 0 12 2156 12 6.122 18 1 1 1 0 12 2250 12 6.150 19 1 0 0 0 12 2024 11 6.917 20 1 1 1 0 12 2123 10 4.710 21 1 1 0 0 16 4160 16 3.131 22 1 0 1 0 12 2000 12 4.000 23 1 0 0 0 13 2420 17 7.223 24 1 0 0 0 12 1150 17 7.965 25 1 1 1 0 12 2024 12 4.088 26 1 1 0 0 17 1904 17 14.181 27 1 0 0 1 12 2448 16 6.536 28 1 1 1 0 12 2000 13 8.500 29 1 1 1 0 17 2390 17 6.276 30 1 0 0 0 12 1920 10 5.208 31 1 0 0 1 11 1840 10 2.782 32 1 0 0 1 16 3360 17 4.911 33 1 1 1 0 13 2284 13 5.867 34 1 1 0 1 12 1875 8 7.520 35 1 1 0 0 16 2140 17 7.545 36 1 1 0 0 11 1896 8 5.538 37 1 0 0 0 12 1040 16 6.923 38 1 1 0 0 10 2200 12 5.000 39 1 0 0 1 14 1952 12 7.306 40 1 0 1 0 17 1560 17 11.218 41 1 1 1 0 12 4030 16 3.846 42 1 0 0 1 12 2570 12 5.837 43 1 1 1 0 16 1530 16 13.725 44 1 1 0 0 12 3149 8 6.349 45 1 1 1 0 12 2690 12 5.253 46 1 1 1 0 12 3096 12 1.308 47 1 1 0 0 16 2552 16 2.800 48 1 0 0 0 12 2040 11 2.696 49 1 0 0 1 12 2180 13 7.569 50 1 1 1 0 12 1864 12 3.408 51 1 1 0 0 12 2068 12 6.540 52 1 0 0 1 12 2010 12 7.214 53 1 1 1 0 12 2152 10 6.273 54 1 0 0 1 8 1496 11 5.882 55 1 0 0 1 10 2100 4 3.809 56 1 0 1 0 16 1960 14 6.378 57 1 0 0 1 14 1985 15 6.045 58 1 0 1 0 17 2020 17 8.812 59 1 0 0 1 14 2178 16 8.877 60 1 1 1 0 12 3684 12 3.342 61 1 1 0 0 14 5010 13 3.184 62 1 1 0 0 12 1880 12 6.915 63 1 0 0 0 8 1904 8 5.515 64 1 0 0 0 12 2083 12 5.281 65 1 1 0 0 12 2125 12 3.200 66 1 0 1 0 8 1985 12 5.879 67 1 1 0 0 17 2640 17 6.250 68 1 0 1 0 12 2070 8 7.488 69 1 1 0 0 12 2107 8 6.977 70 1 1 0 1 12 2250 10 8.000 71 1 0 1 0 12 2880 16 4.132 72 1 0 0 1 12 1848 12 14.476 73 1 0 0 1 9 1927 7 5.734 74 1 1 1 0 10 1304 9 3.374 75 1 1 1 0 12 3000 12 1.833 76 1 1 0 0 12 1892 12 5.603 77 1 1 1 0 12 3644 12 4.298 78 1 0 0 0 17 1430 17 3.916 79 1 1 1 0 15 2350 14 4.879 80 1 0 0 1 12 1948 16 9.240 81 1 0 0 1 6 1804 12 6.652 82 1 0 0 1 14 2326 12 4.622 83 1 1 0 1 12 1739 11 9.974 84 1 0 0 1 14 1176 17 20.918 85 1 0 0 1 9 1100 8 1.940 86 1 1 0 1 17 1528 17 8.835 87 1 0 0 1 13 2250 15 4.667 88 1 1 0 0 9 1927 10 5.189 89 1 1 0 0 15 2414 16 18.724 90 1 1 1 0 12 768 8 10.417 91 1 1 0 0 12 1984 14 8.846 92 1 1 1 0 12 2246 12 7.569 93 1 1 1 0 12 3024 17 4.451 94 1 1 0 0 12 2921 12 9.320 95 1 1 1 0 12 2045 17 9.169 96 1 1 0 0 12 1928 12 6.483 97 1 1 0 0 12 1920 10 7.812 98 1 1 1 0 13 2280 17 11.404 99 1 0 0 0 12 2300 17 5.087 100 1 1 1 0 13 2480 13 6.976 101 1 1 1 0 12 1135 10 5.286 102 1 1 1 0 12 1384 12 11.562 103 1 1 0 0 12 1848 14 8.606 104 1 1 0 0 16 2499 17 12.805 105 1 1 0 0 12 2390 12 6.695 106 1 1 1 0 13 2400 14 8.333 107 1 0 0 0 11 1920 8 4.167 108 1 1 0 0 12 2301 13 5.476 109 1 1 0 0 12 1944 10 5.144 110 1 1 1 0 12 2100 17 11.667 111 1 1 1 0 17 1920 16 7.292 112 1 0 0 1 14 2880 14 4.861 113 1 1 0 0 16 1932 16 12.164 114 1 0 0 0 17 3234 17 10.823 115 1 1 0 0 12 2805 12 12.478 116 1 0 0 0 11 2272 9 6.162 117 1 0 0 0 12 2227 14 7.185 118 1 1 0 0 12 1720 6 7.093 119 1 1 1 0 17 2300 13 17.826 120 1 0 0 0 10 3410 12 6.393 121 1 1 0 1 13 2304 16 11.719 122 1 0 0 0 11 1984 12 4.788 123 1 1 0 0 12 1890 14 4.233 124 1 1 1 0 16 1970 12 7.107 125 1 0 0 0 17 2400 12 8.333 126 1 1 1 0 12 2504 12 6.989 127 1 1 0 0 16 2398 17 9.591 128 1 1 1 0 12 1960 12 6.020 129 1 1 0 1 16 2550 12 2.969 130 1 1 1 0 8 2500 12 7.000 131 1 1 1 0 12 2164 10 7.163 132 1 0 0 1 12 2640 12 4.736 133 1 1 1 0 12 1936 12 8.781 134 1 1 1 0 13 2136 12 9.832 135 1 1 0 0 11 1955 10 6.650 136 1 1 0 0 12 1980 10 9.848 137 1 1 1 0 12 2550 11 9.804 138 1 1 1 0 14 2058 13 8.260 139 1 1 1 0 12 2263 14 9.068 140 1 0 0 1 12 1763 13 8.508 141 1 0 0 1 12 2096 14 7.157 142 1 0 0 0 17 2059 13 7.042 143 1 1 0 0 14 1820 17 6.593 144 1 1 0 1 12 2832 8 4.346 145 1 0 0 1 9 1990 10 7.236 146 1 1 1 0 12 2000 7 1.740 147 1 1 0 0 12 1885 12 6.897 148 1 1 1 0 12 2860 12 5.045 149 1 1 0 1 14 1913 16 17.250 150 1 1 0 0 16 1800 16 8.333 151 1 1 0 0 17 2880 16 9.375 152 1 1 1 0 15 1993 16 8.279 153 1 1 0 0 12 2250 14 4.500 154 1 1 1 0 16 2286 16 4.199 155 1 0 0 0 17 1880 16 16.064 156 1 0 0 1 17 2350 17 11.277 157 1 1 1 0 12 3640 11 0.549 158 1 0 0 0 16 1770 14 9.548 159 1 1 0 0 13 1875 13 5.333 160 1 1 1 0 12 2200 9 5.455 161 1 1 1 0 11 2033 12 5.903 162 1 1 1 0 16 2739 17 9.858 163 1 1 1 0 14 1626 14 11.685 164 1 1 0 0 16 2248 17 6.228 165 1 0 0 0 12 2140 12 9.175 166 1 1 1 0 9 1985 12 6.297 167 1 0 0 0 17 1528 17 10.471 168 1 0 0 0 14 1920 16 14.583 169 1 1 0 0 12 1918 10 6.214 170 1 0 1 0 12 2112 12 6.629 171 1 1 0 0 11 2144 9 3.825 172 1 1 0 1 12 1920 15 7.812 173 1 1 1 0 12 2241 9 3.481 174 1 0 0 0 10 880 11 5.550 175 1 1 1 0 12 2070 16 11.594 176 1 0 0 0 5 1050 5 9.524 177 1 1 0 0 17 2635 17 7.280 178 1 0 1 0 11 3000 8 3.000 179 1 1 0 1 12 2500 11 10.400 180 1 1 0 0 12 1990 12 10.050 181 1 1 1 0 14 2390 13 8.787 182 1 1 1 0 11 1430 12 10.140 183 1 1 0 0 12 1800 8 5.000 184 1 1 1 0 14 2103 17 11.888 185 1 1 0 0 12 1350 12 13.333 186 1 1 1 0 10 2880 17 8.681 187 1 0 1 0 16 2400 17 7.292 188 1 1 0 0 13 1135 12 3.524 189 1 1 1 0 12 2750 17 6.182 190 1 0 1 0 12 2085 16 2.975 191 1 0 0 1 12 2600 8 4.577 192 1 1 1 0 11 3542 12 3.106 193 1 1 0 0 12 1975 12 5.823 194 1 1 1 0 9 2400 12 7.500 195 1 1 1 0 13 3000 14 8.333 196 1 1 0 0 12 1960 14 9.235 197 1 1 1 0 12 2000 9 5.500 198 1 0 0 1 12 3000 12 6.000 199 1 1 0 0 13 2400 12 6.667 200 1 1 0 0 16 2450 14 13.061 201 1 1 0 0 12 2423 8 12.794 202 1 1 1 0 16 2000 16 12.000 203 1 0 1 0 17 2526 16 6.730 204 1 0 0 1 12 2695 12 6.679 205 1 0 0 1 12 2048 12 4.394 206 1 1 0 0 9 1920 11 4.688 207 1 1 0 0 12 2338 9 7.699 208 1 1 1 0 12 2945 8 4.329 209 1 0 0 0 13 2047 12 6.742 210 1 0 1 0 12 1668 7 9.352 211 1 0 0 1 12 175 10 9.143 212 1 0 0 1 12 1798 12 2.642 213 1 1 0 0 12 1222 12 7.365 214 1 0 0 1 10 1820 7 1.407 215 1 0 0 1 12 1560 8 1.519 216 1 0 0 0 16 2210 12 3.344 217 1 1 0 0 12 2874 16 2.911 218 1 1 1 0 11 2499 8 4.383 219 1 0 0 0 12 3088 12 4.210 220 1 1 1 0 10 2020 8 3.713 221 1 1 0 0 12 1980 10 10.606 222 1 1 0 0 12 1968 11 8.638 223 1 0 0 0 12 2100 12 3.837 224 1 1 1 0 12 2651 17 7.167 225 1 1 0 0 16 1918 14 8.863 226 1 1 0 0 17 2585 16 7.737 227 1 1 0 0 12 2250 17 9.170 228 1 1 1 0 17 2480 17 11.290 229 1 0 0 1 12 2924 12 6.327 230 1 1 1 0 12 1896 9 4.747 231 1 1 1 0 12 2332 12 4.888 232 1 0 0 1 8 3482 7 2.484 233 1 1 1 0 12 2106 14 8.547 234 1 1 1 0 13 1160 12 6.638 235 1 1 1 0 12 2040 12 3.186 236 1 1 0 0 12 2856 16 4.547 237 1 1 0 0 8 950 8 12.789 238 1 0 0 1 12 2068 10 6.528 239 1 1 1 0 17 1896 17 16.403 240 1 0 0 1 17 2000 16 7.000 241 1 0 0 0 12 288 11 13.542 242 1 1 1 0 13 2160 12 6.713 243 1 1 1 0 12 3120 17 5.449 244 1 1 1 0 12 1944 17 6.687 245 1 1 0 0 12 2046 12 9.094 246 1 1 0 0 12 2005 9 6.534 247 1 1 1 0 9 2070 10 4.686 248 1 1 0 0 10 3000 12 8.000 249 1 0 0 1 12 2640 12 4.053 250 1 1 1 0 16 2450 17 8.163 251 1 0 0 1 13 1000 15 16.500 252 1 1 1 0 8 2080 8 3.462 253 1 0 0 1 16 2413 16 11.361 254 1 1 1 0 13 2570 12 3.891 255 1 1 1 0 12 2030 12 9.606 256 1 0 0 0 11 4684 17 2.669 257 1 1 1 0 13 2802 17 8.886 258 1 1 0 1 12 2090 12 6.364 259 1 1 1 0 12 2053 16 17.728 260 1 1 1 0 10 1984 10 5.292 261 1 0 0 1 12 2040 16 15.686 262 1 1 0 0 17 2794 8 5.242 263 1 1 0 0 15 3290 14 3.039 264 1 1 0 0 16 1911 16 16.745 265 1 0 0 1 10 2000 12 3.750 266 1 1 1 0 11 2580 12 5.814 267 1 1 0 0 12 2400 17 10.833 268 1 1 0 0 12 1740 12 9.003 269 1 1 0 0 14 2500 12 6.540 270 1 1 1 0 16 1840 17 5.978 271 1 0 0 1 14 2036 14 8.625 272 1 1 0 0 8 3536 11 4.833 273 1 1 0 0 7 880 11 10.909 274 1 1 1 0 12 2007 12 3.886 275 1 0 0 0 12 2632 13 8.457 276 1 0 0 0 14 2600 12 5.000 277 1 1 1 0 12 2156 13 7.421 278 1 1 1 0 12 3625 12 4.494 279 1 0 1 0 12 2420 12 7.025 280 1 1 0 0 14 2080 9 3.279 281 1 0 0 1 16 3443 17 1.286 282 1 1 1 0 12 2250 12 5.556 283 1 1 1 0 12 2535 16 7.495 284 1 0 0 0 12 2352 15 22.109 285 1 1 0 0 13 3036 6 9.058 286 1 0 0 1 13 2600 14 11.538 287 1 0 1 0 10 2223 7 6.047 288 1 1 1 0 12 2666 10 1.365 289 1 1 0 0 12 2006 17 16.550 290 1 1 0 0 12 1710 17 8.772 291 1 0 1 0 12 1920 14 9.635 292 1 0 1 0 14 1647 14 6.679 293 1 1 1 0 17 3080 15 7.573 294 1 0 0 0 10 1920 10 7.812 295 1 1 1 0 9 2420 4 1.984 296 1 0 0 0 12 2205 15 9.524 297 1 1 1 0 12 3035 14 6.425 298 1 0 0 1 16 2185 15 10.984 299 1 0 0 1 12 1880 12 12.766 300 1 1 1 0 17 1863 17 9.555 301 1 0 0 0 12 2456 16 5.497 302 1 1 0 0 17 1847 17 9.762 303 1 1 1 0 11 2000 6 5.000 304 1 1 1 0 16 1856 14 6.000 305 1 1 1 0 11 1880 14 6.915 306 1 1 1 0 13 3020 12 4.967 307 1 1 1 0 11 2646 8 2.390 308 1 1 0 0 8 1640 9 4.512 309 1 0 0 1 11 1950 11 9.231 310 1 1 0 0 12 1920 12 11.458 311 1 1 1 0 10 2025 12 7.699 312 1 0 0 1 17 2470 17 4.183 313 1 1 1 0 12 1800 12 8.333 314 1 0 0 1 12 1920 8 5.104 315 1 1 0 0 17 2039 17 8.227 316 1 1 0 0 14 2570 16 5.663 317 1 0 0 0 12 1914 12 5.222 318 1 1 0 0 12 1516 10 7.190 319 1 0 0 0 12 2520 16 13.907 320 1 1 0 0 12 2327 12 6.016 321 1 0 0 0 12 2188 12 7.678 322 1 0 0 0 12 1864 12 5.311 323 1 1 1 0 9 2183 10 4.123 324 1 0 0 0 10 1920 12 7.812 325 1 1 1 0 12 1824 12 4.605 326 1 1 0 1 12 2878 12 3.975 327 1 1 0 1 12 2390 16 15.063 328 1 1 1 0 12 3120 13 4.506 329 1 1 1 0 12 2040 12 5.882 330 1 1 1 0 17 2151 15 10.228 331 1 1 1 0 12 1976 14 6.579 332 1 0 0 0 17 2286 16 8.815 333 1 0 0 1 12 2032 10 2.953 334 1 1 0 0 10 1680 4 4.517 335 1 1 0 0 12 1560 14 5.128 336 1 1 1 0 12 2895 12 4.145 337 1 0 0 0 12 1820 8 8.791 338 1 1 0 0 12 2450 12 7.306 339 1 1 1 0 12 1748 8 6.007 340 1 1 0 0 12 1020 12 8.333 341 1 1 0 0 16 2342 17 11.529 342 1 0 0 1 13 2250 12 6.667 343 1 1 1 0 13 2880 16 8.681 344 1 1 0 0 12 2032 12 3.468 345 1 1 1 0 16 3120 16 3.032 346 1 0 0 1 17 1760 16 13.807 347 1 0 0 0 12 1725 12 6.217 348 1 1 0 0 14 2080 13 4.231 349 1 1 0 1 12 2040 10 11.470 350 1 0 1 0 17 2940 17 12.245 351 1 0 0 0 12 2280 12 9.210 352 1 1 1 0 14 2164 16 6.331 353 1 1 0 0 12 1999 12 7.804 354 1 1 1 0 12 1824 13 7.675 355 1 1 0 0 17 2182 16 15.582 356 1 1 1 0 16 2385 16 9.644 357 1 0 0 0 16 2460 17 12.886 358 1 1 1 0 12 2595 16 6.551 359 1 0 0 1 9 2400 10 8.250 360 1 1 1 0 12 3120 8 1.379 361 1 1 0 0 12 2850 12 5.263 362 1 0 0 1 16 760 12 10.526 363 1 1 1 0 14 2500 17 10.000 364 1 1 1 0 12 2630 14 12.548 365 1 1 0 0 12 2597 8 4.621 366 1 0 0 1 11 2760 11 1.854 367 1 1 0 0 12 2070 8 7.427 368 1 1 0 0 16 2256 16 13.298 369 1 1 0 0 17 1505 16 26.578 370 1 1 0 1 17 2364 17 13.959 371 1 1 1 0 14 2895 14 13.126 372 1 1 0 0 12 2041 12 7.888 373 1 1 1 0 14 2195 17 8.656 374 1 1 1 0 12 1935 12 3.411 375 1 1 1 0 10 1950 10 4.626 376 1 1 1 0 12 2375 14 4.210 377 1 1 0 0 13 1920 8 4.271 378 1 1 1 0 16 3300 12 3.342 379 1 1 1 0 12 3680 16 8.636 380 1 1 0 0 7 1968 12 6.758 381 1 1 1 0 16 2504 10 0.584 382 1 1 0 0 14 2000 14 5.335 383 1 0 1 0 12 1656 12 4.710 384 1 1 1 0 10 1968 7 4.980 385 1 1 0 0 12 2016 12 2.976 386 1 0 0 0 16 2602 17 12.744 387 1 1 1 0 10 1560 9 3.846 388 1 1 1 0 12 1827 10 7.223 389 1 0 1 0 14 2080 14 8.808 390 1 0 0 0 12 3390 12 5.386 391 1 0 0 1 6 2524 6 3.685 392 1 1 1 0 15 2777 12 1.844 393 1 0 0 1 12 3120 13 1.301 394 1 1 1 0 17 2700 17 7.222 395 1 0 0 1 14 1904 12 7.353 396 1 1 1 0 13 2360 14 11.864 397 1 0 0 1 6 1960 8 5.333 398 1 0 1 0 16 2000 16 5.500 399 1 1 0 0 14 2600 16 7.231 400 1 1 1 0 15 2000 9 7.000 401 1 1 0 0 14 2218 14 7.214 402 1 1 0 0 8 2000 5 4.500 403 1 0 0 1 14 2595 16 8.863 404 1 1 0 0 12 2400 12 9.333 405 1 1 0 0 12 2856 12 3.826 406 1 1 1 0 12 2601 14 5.445 407 1 1 0 0 12 2054 9 8.763 408 1 1 1 0 12 2500 12 8.400 409 1 0 1 0 12 1960 12 5.102 410 1 0 0 0 8 2058 8 7.410 411 1 1 1 0 12 2410 10 5.353 412 1 1 0 0 17 1278 17 19.562 413 1 1 1 0 12 2875 12 5.078 414 1 0 0 0 12 2340 12 0.513 415 1 1 1 0 14 3060 17 5.402 416 1 1 1 0 13 1920 12 7.865 417 1 1 1 0 17 3390 12 6.053 418 1 1 1 0 8 2400 12 5.000 419 1 1 1 0 12 1640 11 7.683 420 1 1 1 0 11 1656 8 6.039 421 1 1 0 0 12 1920 16 5.851 422 1 1 0 0 12 1780 10 6.742 423 1 1 1 0 17 1850 17 8.108 424 1 1 1 0 10 3430 12 5.306 425 1 1 0 0 12 2008 8 7.271 426 1 1 0 0 13 2140 11 8.178 427 1 1 1 0 12 3380 12 7.101 428 1 1 1 0 12 2430 11 6.584 429 1 1 0 0 12 2550 15 7.853 430 1 1 1 0 16 1928 16 11.929 431 1 1 1 0 12 1100 17 18.000 432 1 1 0 0 12 3193 16 10.022 433 1 1 0 0 12 2250 16 9.333 434 1 1 0 0 12 2012 13 6.085 435 1 1 0 0 13 3856 15 5.705 436 1 0 0 1 12 1645 12 9.118 437 1 0 0 1 12 1554 12 7.207 438 1 1 1 0 10 2352 8 5.315 439 1 0 0 1 12 1980 12 8.283 440 1 1 1 0 12 2352 7 7.058 441 1 1 0 0 7 1784 6 6.166 442 1 0 0 1 12 2500 12 2.700 443 1 1 1 0 9 2088 14 7.663 444 1 1 0 0 12 4640 12 1.849 445 1 1 0 0 10 3900 12 3.846 446 1 1 1 0 14 1988 17 9.054 447 1 1 0 0 14 1920 10 9.375 448 1 1 0 0 12 2400 12 8.333 449 1 1 1 0 12 1867 12 9.373 450 1 1 1 0 17 3570 17 4.482 451 1 1 1 0 8 2805 12 5.348 452 1 1 1 0 12 1110 15 3.203 453 1 1 1 0 17 2695 16 12.801 454 1 0 0 1 12 1950 16 6.910 455 1 1 1 0 12 2128 12 4.229 456 1 1 1 0 12 3260 12 4.448 457 1 1 0 1 9 1987 12 8.052 458 1 0 1 0 11 2185 11 7.506 459 1 1 1 0 12 2475 12 7.475 460 1 0 0 1 12 2610 12 6.322 461 1 1 1 0 9 1920 12 2.135 462 1 1 1 0 11 2352 14 4.209 463 1 1 1 0 12 3160 12 0.759 464 1 1 1 0 9 1040 13 4.808 465 1 1 0 1 12 3120 12 12.788 466 1 1 1 0 17 2240 16 7.143 467 1 1 1 0 12 1980 16 15.152 468 1 0 0 1 14 1960 14 7.908 469 1 1 1 0 12 2940 17 6.973 470 1 0 0 1 12 2467 11 4.918 471 1 1 0 1 10 2256 12 8.311 472 1 1 0 0 12 1680 12 7.143 473 1 0 0 1 12 2250 12 5.689 474 1 1 0 0 10 2400 9 7.083 475 1 1 1 0 12 2196 11 5.692 476 1 1 1 0 13 2400 12 11.400 477 1 1 0 0 12 3825 11 1.415 478 1 1 1 0 8 2860 9 2.751 479 1 1 1 0 12 2750 12 6.618 480 1 0 0 0 13 2103 16 9.273 481 1 0 0 0 12 1880 12 12.287 482 1 1 1 0 12 3185 13 2.855 483 1 1 1 0 13 2677 17 7.971 484 1 1 1 0 13 3600 17 19.444 485 1 0 0 1 8 4334 8 1.481 486 1 0 0 0 12 2874 8 1.892 487 1 1 0 1 8 1936 9 6.921 488 1 0 0 0 14 1964 8 10.692 489 1 0 0 0 9 1900 8 9.947 490 1 1 1 0 16 2500 17 19.000 491 1 1 1 0 12 3173 14 7.488 492 1 1 1 0 16 2916 17 9.259 493 1 0 0 0 12 2208 12 11.322 494 1 1 0 0 12 2094 10 6.447 495 1 1 1 0 12 2250 12 6.578 496 1 1 0 0 12 2000 12 6.100 497 1 1 0 0 11 2600 10 4.231 498 1 0 0 1 12 4368 8 2.217 499 1 1 1 0 13 3068 16 6.519 500 1 1 0 1 12 2218 11 6.492 501 1 0 0 1 12 1848 12 11.364 502 1 0 1 0 16 2430 17 9.877 503 1 1 0 0 16 2640 17 7.658 504 1 1 0 0 12 2108 16 8.302 505 1 1 0 0 12 1998 12 14.114 506 1 0 0 1 14 2500 16 16.000 507 1 0 0 1 14 1665 17 11.291 508 1 1 0 0 12 2990 12 2.340 509 1 1 1 0 13 1795 16 7.799 510 1 0 0 1 12 2500 12 6.000 511 1 1 1 0 11 2205 11 6.689 512 1 1 0 0 12 2460 14 8.537 513 1 1 0 1 15 1880 16 13.830 514 1 0 0 1 7 3481 9 1.480 515 1 1 0 0 12 2450 12 8.571 516 1 1 1 0 12 2062 14 8.230 517 1 0 0 1 12 2146 14 8.388 518 1 0 0 1 12 1575 7 7.365 519 1 1 1 0 13 3096 12 3.442 520 1 0 0 1 12 3280 11 3.354 521 1 0 0 1 10 1680 8 2.976 522 1 1 1 0 12 2625 11 5.181 523 1 1 1 0 14 1846 16 10.293 524 1 1 0 0 12 2178 10 13.243 525 1 1 1 0 10 960 9 3.646 526 1 0 0 1 11 2210 12 10.407 527 1 1 0 0 12 2192 12 8.212 528 1 1 1 0 12 1960 16 8.674 529 1 0 0 1 12 1920 10 8.321 530 1 1 1 0 8 2286 12 4.287 531 1 1 0 1 7 2000 7 10.500 532 1 1 1 0 16 2256 12 7.979 533 1 1 1 0 14 2370 17 11.814 534 1 1 1 0 12 1800 13 6.111 535 1 1 1 0 16 2250 16 12.578 536 1 1 0 0 12 1080 12 11.244 537 1 1 1 0 10 2840 12 2.741 538 1 0 0 1 7 2250 11 4.444 539 1 1 1 0 12 2746 12 5.827 540 1 1 0 1 10 2300 8 2.348 541 1 0 0 1 8 2860 8 4.476 542 1 1 0 0 11 1765 12 8.499 543 1 1 0 0 15 2520 16 7.183 544 1 1 0 1 12 2208 12 10.870 545 1 1 0 0 12 2119 8 7.122 546 1 1 0 0 13 2580 17 10.659 547 1 1 1 0 9 1984 12 3.276 548 1 0 0 1 12 1880 8 7.752 549 1 1 0 1 12 2185 12 11.945 550 1 1 0 0 12 2080 12 4.615 551 1 1 0 1 12 1920 12 13.021 552 1 1 1 0 6 3000 5 2.667 553 1 1 1 0 12 2100 12 8.833 554 1 0 0 0 12 1690 12 2.367 555 1 1 0 1 12 2600 16 5.000 556 1 1 0 0 12 1984 16 7.460 557 1 0 0 0 12 2064 12 7.752 558 1 1 0 0 12 2553 16 5.405 559 1 0 0 1 8 2776 9 6.124 560 1 1 1 0 12 2315 13 8.337 561 1 1 0 1 12 1880 12 7.128 562 1 1 0 0 7 2160 7 6.120 563 1 0 0 1 15 900 12 3.111 564 1 1 0 0 12 2467 16 12.161 565 1 0 0 0 6 1820 12 5.385 566 1 1 0 0 12 2223 10 3.984 567 1 1 0 1 12 2142 12 7.470 568 1 0 0 0 12 1928 11 11.411 569 1 1 0 0 12 2783 17 12.936 570 1 1 1 0 12 1960 12 5.612 571 1 1 0 0 12 1920 12 6.810 572 1 1 1 0 12 1587 12 8.507 573 1 1 1 0 12 2496 10 5.508 574 1 1 1 0 17 2280 12 7.229 575 1 1 1 0 16 2750 17 9.454 576 1 0 0 1 12 2115 12 8.983 577 1 1 0 0 11 2590 12 5.985 578 1 1 1 0 12 2372 12 3.963 579 1 0 0 0 10 2295 12 7.058 580 1 1 1 0 10 2096 12 7.157 581 1 1 1 0 12 3315 17 4.014 582 1 1 1 0 14 1777 16 3.883 583 1 1 0 0 10 1880 12 7.979 584 1 0 0 0 12 2184 10 5.037 585 1 1 1 0 16 3250 16 7.077 586 1 0 0 1 5 1520 5 3.618 587 1 1 1 0 12 3119 13 13.466 588 1 0 1 0 12 640 17 14.531 589 1 1 0 0 12 2250 12 6.800 590 1 0 0 1 13 3000 13 6.667 591 1 0 0 1 8 2028 8 5.917 592 1 1 1 0 12 2412 14 10.240 593 1 0 0 1 8 2452 12 8.157 594 1 1 0 0 8 2304 12 6.678 595 1 1 0 0 12 3120 12 0.962 596 1 1 0 1 8 1670 8 16.907 597 1 1 0 0 12 2137 15 13.570 598 1 0 0 1 11 2071 11 8.933 599 1 1 0 0 13 1960 7 4.737 600 1 1 1 0 8 2068 12 8.511 601 1 1 0 0 12 2190 12 5.251 602 1 1 1 0 15 2295 13 21.786 603 1 1 1 0 12 2970 13 7.912 604 1 1 1 0 10 2068 8 7.108 605 1 1 1 0 13 2419 14 5.043 606 1 1 1 0 12 2150 11 2.244 607 1 1 0 0 11 1152 10 8.507 608 1 1 0 0 12 2640 12 7.576 609 1 1 0 0 11 2550 11 5.882 610 1 0 0 0 13 1360 16 40.441 611 1 1 1 0 12 2420 12 6.234 612 1 1 0 0 11 2205 14 9.297 613 1 1 0 0 12 3268 12 0.704 614 1 0 0 1 12 3672 12 2.542 615 1 1 0 0 12 1800 12 5.083 616 1 1 0 0 10 1926 14 7.684 617 1 1 1 0 7 1920 7 4.062 618 1 1 0 0 12 2080 12 6.511 619 1 1 0 0 12 2856 12 2.951 620 1 1 0 0 12 2115 10 5.674 621 1 1 0 0 12 1880 12 12.766 622 1 0 0 1 11 2000 12 6.000 623 1 1 1 0 12 2044 15 9.296 624 1 1 0 0 10 1677 11 5.132 625 1 1 1 0 11 2184 8 9.066 626 1 0 0 1 16 3185 13 11.931 627 1 0 0 0 10 2680 10 6.716 628 1 1 0 0 14 3615 14 4.585 629 1 0 0 1 11 2139 12 9.257 630 1 1 0 0 12 3080 12 8.566 631 1 0 0 1 5 1261 6 6.741 632 1 1 0 0 10 2227 10 9.879 633 1 1 0 1 16 1920 16 9.375 634 1 0 0 1 12 2350 12 6.277 635 1 1 1 0 11 1785 10 6.424 636 1 0 0 0 12 2598 12 8.372 637 1 0 0 1 12 2455 17 12.220 638 1 1 0 1 12 2450 13 3.561 639 1 1 1 0 12 1218 8 8.867 640 1 1 0 1 6 2040 4 1.851 641 1 1 1 0 14 2600 14 8.077 642 1 1 1 0 12 2450 16 8.980 643 1 0 0 1 12 2717 12 6.662 644 1 0 0 1 16 2701 17 11.107 645 1 1 1 0 12 2600 17 14.077 646 1 0 0 0 12 3640 10 0.412 647 1 1 1 0 17 2940 16 23.810 648 1 0 0 0 12 1880 12 9.575 649 1 0 0 0 12 3500 12 1.929 650 1 0 0 1 9 3320 8 2.578 651 1 0 0 1 12 1605 12 7.227 652 1 1 1 0 12 2500 14 8.800 653 1 1 0 0 12 2400 9 7.750 654 1 1 1 0 12 1634 17 14.688 655 1 1 1 0 12 2260 12 10.487 656 1 0 0 1 12 3478 12 4.025 657 1 1 0 0 14 2550 16 11.765 658 1 1 0 1 10 840 8 9.524 659 1 0 0 0 12 1520 12 6.908 660 1 1 0 0 9 1920 9 5.151 661 1 0 0 1 14 2703 17 20.496 662 1 1 1 0 16 1896 16 13.555 663 1 0 0 1 11 1960 13 14.796 664 1 1 1 0 12 3060 8 1.690 665 1 1 1 0 12 2805 12 7.230 666 1 0 0 1 12 1944 7 4.115 667 1 0 0 0 12 1960 12 2.999 668 1 1 0 0 12 2112 8 8.049 669 1 1 1 0 11 2544 12 8.577 670 1 1 0 1 12 1700 17 9.323 671 1 1 1 0 12 2550 12 4.706 672 1 1 0 0 17 2080 17 9.135 673 1 1 0 0 10 2060 12 7.767 674 1 1 0 0 11 1955 8 4.127 675 1 1 0 0 14 2500 17 10.000 676 1 0 0 1 12 2750 10 4.073 677 1 0 0 1 8 2040 5 7.853 678 1 1 1 0 13 3275 15 7.817 679 1 1 1 0 12 2400 12 9.167 680 1 0 0 1 16 2024 17 29.644 681 1 1 0 0 8 1840 6 4.076 682 1 1 1 0 9 2033 17 8.362 683 1 1 0 0 16 1946 17 12.062 684 1 1 1 0 12 3660 12 2.325 685 1 0 0 1 12 2088 11 10.010 686 1 1 1 0 12 2048 12 5.273 687 1 0 0 1 15 1920 15 10.417 688 1 0 0 0 12 2000 12 7.500 689 1 1 0 0 9 2204 8 6.352 690 1 1 0 0 9 3157 10 3.807 691 1 1 0 0 12 1665 14 9.910 692 1 0 0 1 16 2304 11 2.670 693 1 0 0 0 9 2275 8 4.396 694 1 1 1 0 15 2760 17 7.971 695 1 1 0 0 12 1750 14 15.429 696 1 1 1 0 12 3366 13 1.985 697 1 1 0 0 15 2205 16 7.256 698 1 1 0 0 12 1990 9 7.035 699 1 1 0 0 17 1930 16 8.135 700 1 0 0 1 12 1350 12 11.981 701 1 1 0 1 12 3340 12 5.992 702 1 1 0 1 10 960 10 4.146 703 1 1 0 0 13 2732 13 8.510 704 1 0 0 1 12 1624 8 2.963 705 1 1 1 0 11 1804 12 8.815 706 1 1 1 0 8 2805 10 2.495 707 1 0 0 1 12 2160 8 4.884 708 1 1 0 0 16 2052 15 10.200 709 1 1 1 0 12 2250 16 10.000 710 1 1 1 0 12 1120 12 5.357 711 1 1 1 0 12 2450 14 9.796 712 1 0 0 1 10 3432 11 1.374 713 1 1 0 0 12 2700 12 3.463 714 1 1 0 1 12 2817 13 5.041 715 1 1 1 0 15 3000 17 16.667 716 1 0 0 0 10 2125 11 4.329 717 1 1 0 0 14 1864 16 10.193 718 1 1 0 0 12 2400 12 7.583 719 1 0 0 0 8 2160 7 4.120 720 1 1 1 0 8 1040 7 4.808 721 1 1 1 0 12 2450 16 3.153 722 1 1 1 0 12 2500 16 19.000 723 1 1 0 0 16 2131 17 6.053 724 1 1 0 0 12 2165 7 3.788 725 1 1 0 0 5 2230 3 2.929 726 1 0 0 1 8 1995 12 9.297 727 1 0 0 1 13 2025 16 9.383 728 1 1 0 0 12 2450 15 26.531 729 1 1 0 0 12 2160 12 40.509 730 1 1 1 0 14 1715 17 15.160 731 1 1 0 0 12 3018 16 18.964 732 1 1 1 0 12 2216 16 5.415 733 1 1 0 1 12 2499 12 1.999 734 1 0 1 0 12 2250 14 8.000 735 1 1 0 0 14 2116 17 11.153 736 1 1 1 0 12 2016 14 5.952 737 1 0 0 1 12 2470 10 2.119 738 1 0 0 0 9 1640 12 3.838 739 1 1 1 0 14 2016 16 17.361 740 1 0 0 0 11 2185 12 8.009 741 1 1 1 0 12 800 14 3.000 742 1 1 0 0 12 3022 12 10.589 743 1 1 0 0 11 1512 14 10.913 744 1 1 0 1 12 2677 12 5.603 745 1 1 0 0 10 3150 12 7.936 746 1 1 1 0 12 1430 12 2.948 747 1 1 1 0 10 3307 4 2.056 748 1 1 1 0 12 3120 12 1.301 749 1 1 0 0 13 3020 16 9.271 750 1 1 1 0 12 2056 12 4.864 751 1 0 0 0 12 2383 12 1.090 752 1 0 0 1 12 1705 8 12.440 753 1 1 1 0 9 3120 12 6.090 mtr motheduc 1 0.722 12 2 0.661 7 3 0.692 12 4 0.781 7 5 0.622 12 6 0.692 14 7 0.692 14 8 0.692 3 9 0.751 7 10 0.692 7 11 0.582 12 12 0.622 14 13 0.722 16 14 0.722 10 15 0.582 7 16 0.722 16 17 0.692 10 18 0.722 12 19 0.692 7 20 0.692 12 21 0.622 10 22 0.722 12 23 0.661 7 24 0.722 7 25 0.751 12 26 0.582 16 27 0.692 3 28 0.661 3 29 0.622 12 30 0.722 12 31 0.751 7 32 0.692 3 33 0.661 12 34 0.692 7 35 0.661 12 36 0.751 10 37 0.692 3 38 0.661 10 39 0.622 7 40 0.580 14 41 0.722 12 42 0.622 9 43 0.661 14 44 0.661 3 45 0.722 12 46 0.781 12 47 0.722 14 48 0.722 10 49 0.661 7 50 0.781 12 51 0.692 7 52 0.692 7 53 0.751 12 54 0.751 7 55 0.751 7 56 0.692 12 57 0.640 7 58 0.580 17 59 0.582 17 60 0.751 12 61 0.551 14 62 0.692 12 63 0.722 7 64 0.722 7 65 0.751 7 66 0.692 12 67 0.692 12 68 0.722 12 69 0.692 7 70 0.692 12 71 0.692 12 72 0.551 10 73 0.661 7 74 0.791 0 75 0.722 7 76 0.661 12 77 0.722 7 78 0.751 3 79 0.751 10 80 0.661 7 81 0.722 12 82 0.751 12 83 0.661 7 84 0.622 7 85 0.791 7 86 0.582 7 87 0.692 7 88 0.751 7 89 0.442 7 90 0.771 10 91 0.622 7 92 0.722 12 93 0.622 10 94 0.622 12 95 0.692 7 96 0.722 7 97 0.692 7 98 0.582 14 99 0.692 7 100 0.622 12 101 0.771 12 102 0.661 7 103 0.692 7 104 0.442 14 105 0.692 12 106 0.692 10 107 0.692 7 108 0.722 7 109 0.692 7 110 0.661 7 111 0.722 12 112 0.661 7 113 0.622 12 114 0.462 10 115 0.462 10 116 0.722 7 117 0.661 7 118 0.622 7 119 0.442 12 120 0.582 7 121 0.551 7 122 0.722 12 123 0.751 14 124 0.661 12 125 0.610 7 126 0.692 10 127 0.661 7 128 0.722 7 129 0.722 12 130 0.692 10 131 0.722 7 132 0.692 7 133 0.692 12 134 0.580 10 135 0.722 7 136 0.661 12 137 0.622 7 138 0.661 7 139 0.692 7 140 0.622 7 141 0.622 3 142 0.520 12 143 0.722 16 144 0.692 7 145 0.722 3 146 0.801 12 147 0.722 7 148 0.551 12 149 0.491 12 150 0.622 16 151 0.582 12 152 0.692 12 153 0.692 7 154 0.722 14 155 0.491 7 156 0.521 10 157 0.942 7 158 0.622 14 159 0.692 7 160 0.722 7 161 0.751 12 162 0.582 12 163 0.722 17 164 0.722 7 165 0.622 7 166 0.751 3 167 0.582 12 168 0.491 7 169 0.722 7 170 0.692 7 171 0.722 3 172 0.692 7 173 0.722 10 174 0.751 10 175 0.622 12 176 0.722 7 177 0.622 14 178 0.722 10 179 0.622 7 180 0.661 7 181 0.661 10 182 0.692 12 183 0.692 12 184 0.582 7 185 0.661 7 186 0.582 7 187 0.610 12 188 0.722 7 189 0.722 12 190 0.751 12 191 0.722 12 192 0.751 10 193 0.751 12 194 0.622 10 195 0.622 12 196 0.661 12 197 0.661 12 198 0.661 7 199 0.622 12 200 0.521 12 201 0.521 12 202 0.580 12 203 0.582 16 204 0.692 7 205 0.692 16 206 0.751 7 207 0.661 7 208 0.692 10 209 0.661 12 210 0.622 10 211 0.751 0 212 0.751 7 213 0.722 12 214 0.771 12 215 0.771 10 216 0.751 12 217 0.722 3 218 0.722 7 219 0.722 12 220 0.781 10 221 0.551 7 222 0.661 7 223 0.751 7 224 0.692 7 225 0.622 12 226 0.661 12 227 0.622 7 228 0.551 12 229 0.582 7 230 0.751 10 231 0.692 10 232 0.722 7 233 0.622 12 234 0.751 17 235 0.722 7 236 0.582 7 237 0.751 7 238 0.661 7 239 0.551 12 240 0.610 14 241 0.751 7 242 0.692 12 243 0.661 7 244 0.722 7 245 0.692 16 246 0.751 7 247 0.751 10 248 0.521 12 249 0.692 7 250 0.551 16 251 0.462 10 252 0.781 3 253 0.551 16 254 0.722 7 255 0.692 12 256 0.722 7 257 0.661 7 258 0.722 7 259 0.551 12 260 0.722 12 261 0.582 7 262 0.551 10 263 0.722 14 264 0.491 16 265 0.692 7 266 0.722 10 267 0.582 7 268 0.722 14 269 0.610 14 270 0.751 12 271 0.582 7 272 0.622 7 273 0.751 3 274 0.751 7 275 0.551 7 276 0.692 7 277 0.661 12 278 0.692 10 279 0.622 7 280 0.751 3 281 0.722 12 282 0.722 7 283 0.582 12 284 0.442 7 285 0.442 10 286 0.521 7 287 0.692 0 288 0.751 7 289 0.551 10 290 0.640 9 291 0.661 12 292 0.722 12 293 0.442 12 294 0.661 3 295 0.751 9 296 0.622 12 297 0.661 12 298 0.622 14 299 0.521 7 300 0.610 12 301 0.661 12 302 0.692 12 303 0.751 7 304 0.722 12 305 0.692 10 306 0.722 12 307 0.692 7 308 0.751 7 309 0.661 3 310 0.582 12 311 0.661 7 312 0.661 16 313 0.622 12 314 0.692 12 315 0.640 7 316 0.661 14 317 0.722 7 318 0.751 7 319 0.491 12 320 0.722 10 321 0.622 7 322 0.751 3 323 0.751 7 324 0.622 7 325 0.722 10 326 0.661 7 327 0.521 7 328 0.622 12 329 0.661 12 330 0.582 12 331 0.661 10 332 0.582 14 333 0.751 7 334 0.751 7 335 0.751 14 336 0.722 10 337 0.661 10 338 0.692 7 339 0.692 7 340 0.751 7 341 0.582 14 342 0.661 7 343 0.551 12 344 0.722 14 345 0.722 14 346 0.582 14 347 0.661 0 348 0.751 16 349 0.661 7 350 0.500 12 351 0.582 7 352 0.722 10 353 0.692 7 354 0.722 10 355 0.551 10 356 0.661 14 357 0.551 7 358 0.692 12 359 0.661 7 360 0.791 7 361 0.692 12 362 0.720 14 363 0.622 12 364 0.521 7 365 0.722 7 366 0.692 7 367 0.722 12 368 0.582 16 369 0.500 3 370 0.491 16 371 0.622 7 372 0.661 16 373 0.661 7 374 0.722 10 375 0.751 10 376 0.751 10 377 0.771 10 378 0.692 12 379 0.491 10 380 0.692 7 381 0.771 16 382 0.692 7 383 0.722 7 384 0.722 7 385 0.722 7 386 0.442 7 387 0.751 10 388 0.751 10 389 0.661 12 390 0.622 7 391 0.722 7 392 0.722 7 393 0.722 7 394 0.692 14 395 0.661 14 396 0.622 7 397 0.722 7 398 0.661 7 399 0.521 16 400 0.640 12 401 0.692 7 402 0.751 7 403 0.622 12 404 0.661 12 405 0.661 12 406 0.661 10 407 0.622 3 408 0.661 12 409 0.722 12 410 0.692 12 411 0.692 7 412 0.661 16 413 0.692 12 414 0.801 10 415 0.582 10 416 0.722 12 417 0.551 7 418 0.751 7 419 0.751 7 420 0.722 7 421 0.722 7 422 0.722 7 423 0.722 7 424 0.722 7 425 0.622 7 426 0.582 7 427 0.582 12 428 0.692 12 429 0.661 14 430 0.661 14 431 0.661 12 432 0.582 7 433 0.661 7 434 0.751 7 435 0.661 10 436 0.722 7 437 0.722 9 438 0.751 12 439 0.692 12 440 0.722 10 441 0.751 7 442 0.722 7 443 0.722 7 444 0.751 9 445 0.722 7 446 0.722 3 447 0.722 10 448 0.661 12 449 0.692 12 450 0.722 12 451 0.722 7 452 0.781 7 453 0.551 10 454 0.622 10 455 0.771 7 456 0.722 12 457 0.722 7 458 0.692 10 459 0.692 10 460 0.692 16 461 0.801 7 462 0.771 7 463 0.942 12 464 0.791 12 465 0.521 10 466 0.722 12 467 0.582 10 468 0.722 12 469 0.692 12 470 0.582 7 471 0.692 10 472 0.722 7 473 0.722 7 474 0.722 3 475 0.722 10 476 0.622 7 477 0.801 3 478 0.781 12 479 0.722 12 480 0.692 7 481 0.622 12 482 0.751 7 483 0.521 12 484 0.442 12 485 0.751 12 486 0.751 7 487 0.722 7 488 0.661 12 489 0.692 7 490 0.462 7 491 0.661 14 492 0.622 16 493 0.622 12 494 0.751 12 495 0.722 7 496 0.751 12 497 0.781 10 498 0.722 7 499 0.692 12 500 0.722 7 501 0.661 12 502 0.661 12 503 0.692 7 504 0.692 7 505 0.622 14 506 0.521 7 507 0.692 12 508 0.722 12 509 0.722 12 510 0.722 7 511 0.751 10 512 0.692 12 513 0.622 0 514 0.751 7 515 0.661 7 516 0.722 7 517 0.692 7 518 0.722 10 519 0.722 7 520 0.751 7 521 0.781 0 522 0.751 14 523 0.692 12 524 0.582 10 525 0.942 7 526 0.661 7 527 0.692 7 528 0.722 7 529 0.692 7 530 0.751 0 531 0.521 3 532 0.692 12 533 0.622 12 534 0.751 12 535 0.622 12 536 0.751 7 537 0.781 7 538 0.751 0 539 0.722 12 540 0.781 12 541 0.722 0 542 0.722 7 543 0.722 14 544 0.661 12 545 0.722 10 546 0.582 12 547 0.791 3 548 0.722 10 549 0.622 3 550 0.751 7 551 0.622 12 552 0.751 7 553 0.582 12 554 0.791 16 555 0.751 7 556 0.722 10 557 0.722 7 558 0.751 12 559 0.692 9 560 0.692 7 561 0.722 7 562 0.692 0 563 0.751 7 564 0.582 7 565 0.751 7 566 0.751 10 567 0.722 12 568 0.661 7 569 0.551 12 570 0.751 12 571 0.751 7 572 0.722 7 573 0.661 7 574 0.692 12 575 0.622 16 576 0.692 7 577 0.722 12 578 0.751 12 579 0.582 7 580 0.722 10 581 0.661 12 582 0.722 10 583 0.722 7 584 0.751 12 585 0.661 12 586 0.771 7 587 0.491 10 588 0.722 12 589 0.722 12 590 0.661 7 591 0.722 7 592 0.661 12 593 0.692 7 594 0.722 3 595 0.942 7 596 0.442 7 597 0.622 10 598 0.692 7 599 0.722 17 600 0.722 7 601 0.751 7 602 0.442 12 603 0.661 12 604 0.751 7 605 0.771 7 606 0.781 12 607 0.751 7 608 0.692 7 609 0.751 7 610 0.442 9 611 0.722 12 612 0.692 7 613 0.942 12 614 0.692 3 615 0.751 12 616 0.722 7 617 0.781 7 618 0.661 12 619 0.722 7 620 0.751 12 621 0.661 12 622 0.751 7 623 0.692 7 624 0.751 7 625 0.692 7 626 0.442 7 627 0.692 10 628 0.692 14 629 0.692 7 630 0.622 10 631 0.751 3 632 0.692 7 633 0.661 12 634 0.722 7 635 0.751 7 636 0.661 7 637 0.582 7 638 0.722 10 639 0.751 7 640 0.942 7 641 0.551 16 642 0.661 12 643 0.661 7 644 0.582 16 645 0.551 7 646 0.942 7 647 0.442 12 648 0.692 7 649 0.722 7 650 0.722 7 651 0.722 7 652 0.661 12 653 0.661 7 654 0.661 12 655 0.661 7 656 0.722 12 657 0.622 14 658 0.751 7 659 0.722 7 660 0.751 12 661 0.442 16 662 0.661 12 663 0.582 7 664 0.781 10 665 0.521 7 666 0.751 3 667 0.771 3 668 0.692 7 669 0.622 12 670 0.582 10 671 0.751 3 672 0.661 16 673 0.722 10 674 0.751 7 675 0.661 17 676 0.751 7 677 0.582 3 678 0.661 12 679 0.661 7 680 0.442 12 681 0.791 3 682 0.722 10 683 0.442 16 684 0.751 10 685 0.661 7 686 0.751 7 687 0.692 14 688 0.722 7 689 0.722 10 690 0.722 7 691 0.722 3 692 0.722 12 693 0.751 7 694 0.661 12 695 0.582 7 696 0.722 7 697 0.722 10 698 0.722 3 699 0.692 14 700 0.551 12 701 0.661 7 702 0.751 14 703 0.622 12 704 0.751 0 705 0.622 7 706 0.781 7 707 0.751 10 708 0.661 12 709 0.661 12 710 0.801 12 711 0.661 14 712 0.771 7 713 0.722 7 714 0.722 3 715 0.442 7 716 0.751 7 717 0.692 7 718 0.722 7 719 0.751 3 720 0.942 3 721 0.751 7 722 0.462 7 723 0.622 12 724 0.771 7 725 0.781 0 726 0.692 3 727 0.661 7 728 0.442 7 729 0.442 10 730 0.622 12 731 0.442 7 732 0.751 10 733 0.751 12 734 0.692 10 735 0.661 12 736 0.751 12 737 0.751 10 738 0.771 7 739 0.491 16 740 0.692 7 741 0.801 12 742 0.582 7 743 0.722 10 744 0.722 0 745 0.661 3 746 0.942 7 747 0.791 7 748 0.791 7 749 0.622 10 750 0.771 12 751 0.751 10 752 0.622 12 753 0.692 7 attr(,"assign") [1] 0 1 2 3 4 5 6 7 8 9 attr(,"contrasts") attr(,"contrasts")$kids [1] "contr.treatment" attr(,"contrasts")$age30.39 [1] "contr.treatment" attr(,"contrasts")$age50.60 [1] "contr.treatment" > fitted( lfpResult ) 1 2 3 4 5 6 7 8 6.13e-01 6.50e-01 6.67e-01 6.67e-01 7.94e-01 7.91e-01 8.91e-01 3.43e-01 9 10 11 12 13 14 15 16 7.10e-01 7.63e-01 7.61e-01 6.39e-01 4.12e-01 3.47e-01 5.97e-01 4.17e-01 17 18 19 20 21 22 23 24 6.53e-01 5.67e-01 7.25e-01 8.28e-01 7.44e-01 8.52e-01 6.88e-01 6.97e-01 25 26 27 28 29 30 31 32 6.78e-01 6.63e-01 3.91e-01 6.91e-01 9.38e-01 7.67e-01 6.50e-01 4.43e-01 33 34 35 36 37 38 39 40 8.22e-01 4.71e-01 7.68e-01 5.33e-01 8.83e-01 7.49e-01 8.41e-01 9.60e-01 41 42 43 44 45 46 47 48 2.21e-01 7.28e-01 5.21e-01 5.37e-01 5.15e-01 4.83e-01 7.99e-01 8.77e-01 49 50 51 52 53 54 55 56 5.33e-01 6.60e-01 6.50e-01 5.00e-01 4.85e-01 3.37e-01 5.16e-01 9.04e-01 57 58 59 60 61 62 63 64 8.31e-01 9.72e-01 7.69e-01 2.75e-01 7.19e-01 6.60e-01 6.10e-01 7.12e-01 65 66 67 68 69 70 71 72 6.66e-01 6.85e-01 6.74e-01 6.44e-01 6.50e-01 2.93e-01 7.12e-01 5.05e-01 73 74 75 76 77 78 79 80 6.85e-01 7.41e-01 7.17e-01 8.42e-01 3.34e-01 9.32e-01 6.43e-01 4.21e-01 81 82 83 84 85 86 87 88 2.11e-01 4.63e-01 3.90e-01 6.63e-02 6.95e-01 9.17e-01 6.55e-01 4.39e-01 89 90 91 92 93 94 95 96 6.48e-01 4.82e-01 7.34e-01 4.47e-01 8.08e-01 4.47e-01 4.45e-01 5.73e-01 97 98 99 100 101 102 103 104 6.11e-01 6.71e-01 7.26e-01 8.32e-01 7.62e-01 6.19e-01 5.17e-01 9.49e-01 105 106 107 108 109 110 111 112 5.34e-01 4.91e-01 8.92e-01 5.38e-01 7.99e-01 3.44e-01 7.42e-01 6.41e-01 113 114 115 116 117 118 119 120 6.35e-01 9.54e-01 8.33e-01 5.81e-01 7.30e-01 9.25e-01 8.73e-01 7.16e-01 121 122 123 124 125 126 127 128 5.21e-01 7.22e-01 6.10e-01 9.01e-01 9.26e-01 5.51e-01 5.53e-01 6.70e-01 129 130 131 132 133 134 135 136 6.64e-01 3.61e-01 5.42e-01 5.27e-01 5.60e-01 8.60e-01 5.28e-01 5.35e-01 137 138 139 140 141 142 143 144 6.08e-01 7.67e-01 4.27e-01 7.28e-01 7.40e-01 9.98e-01 6.09e-01 4.62e-01 145 146 147 148 149 150 151 152 2.79e-01 7.31e-01 5.51e-01 9.59e-01 4.50e-01 8.93e-01 7.85e-01 6.76e-01 153 154 155 156 157 158 159 160 7.35e-01 8.19e-01 9.02e-01 8.64e-01 2.88e-02 9.04e-01 8.10e-01 6.83e-01 161 162 163 164 165 166 167 168 4.78e-01 7.92e-01 3.42e-01 6.32e-01 7.68e-01 3.84e-01 9.60e-01 8.88e-01 169 170 171 172 173 174 175 176 6.23e-01 7.71e-01 7.23e-01 3.50e-01 8.03e-01 8.00e-01 5.23e-01 4.11e-01 177 178 179 180 181 182 183 184 8.30e-01 7.05e-01 3.05e-01 5.02e-01 6.38e-01 5.44e-01 8.43e-01 7.36e-01 185 186 187 188 189 190 191 192 4.09e-01 6.04e-01 9.39e-01 9.36e-01 3.59e-01 7.77e-01 4.65e-01 2.90e-01 193 194 195 196 197 198 199 200 4.79e-01 6.79e-01 6.02e-01 5.44e-01 8.88e-01 4.39e-01 8.26e-01 8.12e-01 201 202 203 204 205 206 207 208 7.45e-01 8.28e-01 9.77e-01 3.48e-01 7.00e-01 4.71e-01 6.35e-01 6.91e-01 209 210 211 212 213 214 215 216 8.40e-01 9.12e-01 6.59e-01 6.94e-01 6.87e-01 6.66e-01 7.94e-01 8.47e-01 217 218 219 220 221 222 223 224 5.62e-01 6.54e-01 5.09e-01 5.49e-01 8.66e-01 6.38e-01 7.06e-01 4.38e-01 225 226 227 228 229 230 231 232 8.70e-01 6.97e-01 6.05e-01 8.71e-01 7.49e-01 6.99e-01 7.60e-01 2.38e-01 233 234 235 236 237 238 239 240 7.73e-01 7.40e-01 8.39e-01 8.97e-01 1.30e-01 6.85e-01 6.79e-01 9.12e-01 241 242 243 244 245 246 247 248 4.30e-01 7.09e-01 5.73e-01 5.61e-01 4.21e-01 4.59e-01 5.04e-01 8.15e-01 249 250 251 252 253 254 255 256 5.85e-01 9.54e-01 8.80e-01 4.73e-01 7.23e-01 7.15e-01 4.62e-01 1.43e-01 257 258 259 260 261 262 263 264 4.21e-01 3.36e-01 3.05e-01 6.43e-01 2.09e-01 9.90e-01 5.73e-01 7.64e-01 265 266 267 268 269 270 271 272 6.94e-01 4.56e-01 5.87e-01 3.98e-01 8.70e-01 7.00e-01 8.44e-01 4.80e-01 273 274 275 276 277 278 279 280 1.95e-01 7.08e-01 8.90e-01 7.87e-01 7.14e-01 4.36e-01 8.78e-01 7.90e-01 281 282 283 284 285 286 287 288 5.81e-01 6.28e-01 8.45e-01 3.43e-01 9.80e-01 6.85e-01 7.70e-01 7.47e-01 289 290 291 292 293 294 295 296 3.37e-01 7.06e-01 6.78e-01 8.19e-01 9.97e-01 7.29e-01 7.00e-01 6.87e-01 297 298 299 300 301 302 303 304 5.40e-01 5.59e-01 7.54e-01 9.11e-01 7.63e-01 6.10e-01 6.47e-01 8.20e-01 305 306 307 308 309 310 311 312 6.63e-01 4.91e-01 8.49e-01 5.45e-01 4.44e-01 7.13e-01 6.62e-01 8.48e-01 313 314 315 316 317 318 319 320 8.60e-01 7.30e-01 8.50e-01 7.25e-01 7.57e-01 5.33e-01 7.41e-01 4.90e-01 321 322 323 324 325 326 327 328 8.46e-01 6.64e-01 5.26e-01 8.27e-01 7.95e-01 5.56e-01 2.96e-01 8.16e-01 329 330 331 332 333 334 335 336 8.44e-01 9.12e-01 8.04e-01 9.41e-01 6.29e-01 6.84e-01 6.29e-01 5.54e-01 337 338 339 340 341 342 343 344 7.69e-01 4.76e-01 8.50e-01 5.56e-01 7.11e-01 6.47e-01 8.27e-01 7.63e-01 345 346 347 348 349 350 351 352 7.08e-01 6.43e-01 9.02e-01 6.56e-01 2.15e-01 9.13e-01 8.51e-01 6.27e-01 353 354 355 356 357 358 359 360 5.65e-01 5.54e-01 6.17e-01 6.18e-01 7.78e-01 5.08e-01 3.09e-01 4.89e-01 361 362 363 364 365 366 367 368 5.22e-01 6.39e-01 6.17e-01 7.20e-01 5.86e-01 6.94e-01 4.89e-01 6.03e-01 369 370 371 372 373 374 375 376 1.63e-01 7.12e-01 2.94e-01 6.47e-01 6.62e-01 8.41e-01 5.92e-01 5.51e-01 377 378 379 380 381 382 383 384 6.44e-01 7.88e-01 8.12e-01 4.22e-01 8.80e-01 8.09e-01 8.80e-01 7.15e-01 385 386 387 388 389 390 391 392 8.09e-01 9.68e-01 7.63e-01 5.04e-01 7.78e-01 7.34e-01 3.16e-01 8.64e-01 393 394 395 396 397 398 399 400 5.42e-01 6.42e-01 7.14e-01 5.00e-01 3.16e-01 9.59e-01 9.55e-01 9.31e-01 401 402 403 404 405 406 407 408 6.22e-01 4.90e-01 5.16e-01 4.31e-01 7.50e-01 7.39e-01 7.79e-01 5.53e-01 409 410 411 412 413 414 415 416 7.99e-01 5.18e-01 7.35e-01 1.33e-01 6.00e-01 7.04e-01 9.04e-01 5.66e-01 417 418 419 420 421 422 423 424 9.62e-01 3.21e-01 5.14e-01 7.46e-01 5.80e-01 6.18e-01 7.00e-01 2.32e-01 425 426 427 428 429 430 431 432 8.71e-01 9.01e-01 7.27e-01 6.14e-01 4.72e-01 5.58e-01 1.46e-01 4.37e-01 433 434 435 436 437 438 439 440 4.38e-01 4.46e-01 3.35e-01 3.24e-01 5.04e-01 4.36e-01 4.06e-01 5.26e-01 441 442 443 444 445 446 447 448 3.53e-01 6.15e-01 3.32e-01 1.27e-01 1.73e-01 4.52e-01 4.44e-01 5.17e-01 449 450 451 452 453 454 455 456 5.29e-01 5.04e-01 3.00e-01 8.28e-01 7.56e-01 7.59e-01 5.70e-01 4.17e-01 457 458 459 460 461 462 463 464 1.42e-01 6.53e-01 5.18e-01 3.81e-01 5.39e-01 4.33e-01 4.93e-02 5.86e-01 465 466 467 468 469 470 471 472 3.17e-01 6.73e-01 4.18e-01 3.92e-01 3.61e-01 9.10e-01 1.82e-01 5.89e-01 473 474 475 476 477 478 479 480 4.35e-01 3.38e-01 6.37e-01 5.53e-01 1.82e-01 2.83e-01 3.82e-01 5.00e-01 481 482 483 484 485 486 487 488 5.89e-01 4.49e-01 9.37e-01 2.20e-01 6.68e-02 6.96e-01 2.06e-01 6.75e-01 489 490 491 492 493 494 495 496 4.08e-01 6.24e-01 4.05e-01 6.44e-01 5.76e-01 4.16e-01 5.42e-01 4.49e-01 497 498 499 500 501 502 503 504 2.97e-01 1.97e-01 4.21e-01 3.04e-01 3.13e-01 6.66e-01 5.22e-01 4.42e-01 505 506 507 508 509 510 511 512 3.10e-01 3.66e-01 2.87e-01 6.04e-01 5.59e-01 3.39e-01 3.79e-01 3.36e-01 513 514 515 516 517 518 519 520 2.95e-01 1.60e-01 4.93e-01 4.32e-01 3.39e-01 5.44e-01 6.10e-01 2.40e-01 521 522 523 524 525 526 527 528 5.51e-01 4.23e-01 5.05e-01 5.26e-01 2.19e-01 2.61e-01 4.74e-01 4.00e-01 529 530 531 532 533 534 535 536 4.56e-01 4.25e-01 6.65e-01 7.17e-01 5.01e-01 5.65e-01 5.80e-01 2.99e-01 537 538 539 540 541 542 543 544 3.53e-01 2.27e-01 4.50e-01 3.11e-01 2.50e-01 4.02e-01 4.21e-01 1.91e-01 545 546 547 548 549 550 551 552 5.06e-01 5.86e-01 4.74e-01 4.10e-01 2.69e-01 5.63e-01 2.40e-01 3.32e-01 553 554 555 556 557 558 559 560 8.79e-01 7.54e-01 1.73e-01 4.18e-01 5.17e-01 3.05e-01 2.31e-01 4.86e-01 561 562 563 564 565 566 567 568 3.33e-01 4.98e-01 9.24e-01 4.66e-01 3.77e-01 5.93e-01 2.34e-01 5.08e-01 569 570 571 572 573 574 575 576 4.10e-01 5.73e-01 4.23e-01 5.72e-01 8.01e-01 7.99e-01 6.73e-01 3.24e-01 577 578 579 580 581 582 583 584 3.65e-01 5.92e-01 8.94e-01 4.34e-01 6.25e-01 8.72e-01 3.66e-01 6.01e-01 585 586 587 588 589 590 591 592 5.90e-01 3.66e-01 6.38e-01 3.56e-01 4.41e-01 4.18e-01 3.40e-01 3.97e-01 593 594 595 596 597 598 599 600 1.55e-01 2.76e-01 3.48e-02 5.79e-01 2.91e-01 3.07e-01 7.75e-01 2.55e-01 601 602 603 604 605 606 607 608 4.77e-01 5.21e-01 4.44e-01 3.79e-01 4.39e-01 6.85e-01 4.79e-01 3.98e-01 609 610 611 612 613 614 615 616 2.92e-01 2.10e-04 5.10e-01 3.12e-01 2.89e-02 4.22e-01 5.93e-01 3.54e-01 617 618 619 620 621 622 623 624 4.23e-01 7.48e-01 6.03e-01 4.76e-01 2.99e-01 3.14e-01 4.59e-01 5.53e-01 625 626 627 628 629 630 631 632 4.75e-01 8.85e-01 4.72e-01 4.23e-01 2.56e-01 4.69e-01 2.68e-01 2.65e-01 633 634 635 636 637 638 639 640 5.08e-01 3.58e-01 5.43e-01 5.59e-01 3.44e-01 4.47e-01 5.73e-01 1.37e-02 641 642 643 644 645 646 647 648 9.25e-01 4.68e-01 4.65e-01 5.35e-01 4.48e-01 3.70e-02 2.40e-01 5.41e-01 649 650 651 652 653 654 655 656 5.94e-01 3.00e-01 4.92e-01 4.94e-01 6.14e-01 2.31e-01 4.56e-01 2.30e-01 657 658 659 660 661 662 663 664 3.91e-01 2.69e-01 7.34e-01 4.45e-01 2.42e-01 4.34e-01 2.78e-01 5.14e-01 665 666 667 668 669 670 671 672 9.51e-01 6.03e-01 7.38e-01 5.61e-01 6.40e-01 7.01e-01 4.98e-01 7.03e-01 673 674 675 676 677 678 679 680 3.27e-01 6.40e-01 3.71e-01 3.31e-01 7.73e-01 3.88e-01 5.28e-01 2.89e-02 681 682 683 684 685 686 687 688 4.03e-01 2.57e-01 9.86e-01 3.63e-01 3.78e-01 5.87e-01 3.48e-01 5.58e-01 689 690 691 692 693 694 695 696 4.02e-01 3.27e-01 3.46e-01 8.24e-01 5.21e-01 5.96e-01 4.22e-01 5.95e-01 697 698 699 700 701 702 703 704 5.16e-01 5.55e-01 7.21e-01 8.16e-01 2.64e-01 6.36e-01 6.06e-01 7.67e-01 705 706 707 708 709 710 711 712 8.11e-01 3.16e-01 4.47e-01 6.14e-01 4.40e-01 6.30e-01 4.19e-01 1.99e-01 713 714 715 716 717 718 719 720 6.05e-01 2.48e-01 7.05e-01 5.82e-01 4.48e-01 3.45e-01 5.52e-01 1.08e-01 721 722 723 724 725 726 727 728 6.01e-01 4.45e-01 9.30e-01 5.87e-01 3.32e-01 1.84e-01 4.35e-01 4.90e-02 729 730 731 732 733 734 735 736 6.38e-06 4.08e-01 3.16e-01 4.70e-01 4.48e-01 6.05e-01 3.95e-01 5.03e-01 737 738 739 740 741 742 743 744 5.67e-01 6.20e-01 6.72e-01 5.37e-01 8.44e-01 4.88e-01 2.55e-01 2.58e-01 745 746 747 748 749 750 751 752 2.69e-01 2.06e-01 3.33e-01 4.46e-01 4.26e-01 5.25e-01 8.18e-01 4.73e-01 753 3.16e-01 > all.equal( fitted( lfpResult ), predict( lfpResult, type = "response" ) ) [1] TRUE > all.equal( fitted( lfpResult )[ 11:222 ], + predict( lfpResult, newdata = Mroz87[ 11:222, ], type = "response" ) ) [1] TRUE > linearPredictors( lfpResult ) 1 2 3 4 5 6 7 8 0.287243 0.384880 0.430401 0.432889 0.819108 0.810917 1.233226 -0.404056 9 10 11 12 13 14 15 16 0.554418 0.716680 0.708553 0.355945 -0.222456 -0.394171 0.244467 -0.210449 17 18 19 20 21 22 23 24 0.392222 0.169080 0.597919 0.947886 0.656744 1.044634 0.491544 0.515234 25 26 27 28 29 30 31 32 0.462727 0.420607 -0.277630 0.499766 1.540766 0.727993 0.386197 -0.144558 33 34 35 36 37 38 39 40 0.924715 -0.073529 0.733110 0.081723 1.188671 0.672288 0.999718 1.745355 41 42 43 44 45 46 47 48 -0.769597 0.605838 0.053583 0.092805 0.037934 -0.043765 0.837853 1.159314 49 50 51 52 53 54 55 56 0.082162 0.411686 0.384845 -0.000438 -0.038087 -0.421380 0.038947 1.302891 57 58 59 60 61 62 63 64 0.958971 1.905693 0.734105 -0.597836 0.581268 0.413514 0.279089 0.559819 65 66 67 68 69 70 71 72 0.429965 0.481233 0.450006 0.368901 0.386405 -0.543365 0.560059 0.012096 73 74 75 76 77 78 79 80 0.482976 0.645675 0.572635 1.000901 -0.429490 1.494092 0.367772 -0.198667 81 82 83 84 85 86 87 88 -0.801405 -0.092339 -0.278132 -1.504175 0.510279 1.382309 0.398051 -0.153770 89 90 91 92 93 94 95 96 0.380204 -0.045398 0.624413 -0.132394 0.870208 -0.133639 -0.138697 0.185250 97 98 99 100 101 102 103 104 0.282692 0.443613 0.601664 0.962338 0.711639 0.303570 0.041523 1.637005 105 106 107 108 109 110 111 112 0.085744 -0.023626 1.235725 0.096102 0.837431 -0.400316 0.650510 0.360283 113 114 115 116 117 118 119 120 0.345852 1.687316 0.964460 0.203222 0.613034 1.442290 1.141657 0.571043 121 122 123 124 125 126 127 128 0.052699 0.587558 0.278439 1.285226 1.449205 0.128717 0.133625 0.439265 129 130 131 132 133 134 135 136 0.424681 -0.356817 0.106179 0.067716 0.150295 1.080740 0.070172 0.087378 137 138 139 140 141 142 143 144 0.273850 0.728201 -0.184399 0.606906 0.644347 2.860834 0.276722 -0.096369 145 146 147 148 149 150 151 152 -0.584485 0.614454 0.128239 1.740719 -0.124424 1.241249 0.788108 0.456725 153 154 155 156 157 158 159 160 0.626666 0.911455 1.292560 1.096570 -1.899452 1.306966 0.876168 0.477084 161 162 163 164 165 166 167 168 -0.054551 0.814018 -0.407445 0.335979 0.731666 -0.294405 1.746819 1.216263 169 170 171 172 173 174 175 176 0.312268 0.742290 0.591465 -0.386163 0.852823 0.842413 0.058800 -0.224512 177 178 179 180 181 182 183 184 0.955281 0.537833 -0.509475 0.004040 0.353691 0.109356 1.006971 0.632323 185 186 187 188 189 190 191 192 -0.229538 0.262871 1.545301 1.524635 -0.360314 0.763290 -0.088656 -0.552557 193 194 195 196 197 198 199 200 -0.051500 0.465297 0.257809 0.109856 1.214621 -0.153341 0.940204 0.883501 201 202 203 204 205 206 207 208 0.660385 0.944602 2.001246 -0.389411 0.523848 -0.071938 0.345363 0.498905 209 210 211 212 213 214 215 216 0.994699 1.356215 0.408533 0.506750 0.486118 0.428359 0.819132 1.024078 217 218 219 220 221 222 223 224 0.155432 0.396419 0.021348 0.124032 1.107994 0.354018 0.542369 -0.154974 225 226 227 228 229 230 231 232 1.126062 0.517190 0.266314 1.133448 0.671638 0.520214 0.705705 -0.714267 233 234 235 236 237 238 239 240 0.747846 0.643537 0.988346 1.263027 -1.125014 0.480685 0.466160 1.350594 241 242 243 244 245 246 247 248 -0.175987 0.550678 0.183449 0.153144 -0.199374 -0.103950 0.009780 0.897472 249 250 251 252 253 254 255 256 0.214304 1.681315 1.174238 -0.068347 0.591957 0.568956 -0.095766 -1.068890 257 258 259 260 261 262 263 264 -0.198595 -0.422174 -0.510117 0.367519 -0.809715 2.311955 0.184182 0.719056 265 266 267 268 269 270 271 272 0.506878 -0.111400 0.218952 -0.257787 1.124330 0.523864 1.012818 -0.049234 273 274 275 276 277 278 279 280 -0.861374 0.547738 1.227098 0.797620 0.563927 -0.160428 1.165089 0.805450 281 282 283 284 285 286 287 288 0.204646 0.325715 1.015678 -0.403271 2.045332 0.481103 0.738181 0.665863 289 290 291 292 293 294 295 296 -0.420411 0.543092 0.462146 0.910705 2.705457 0.608297 0.523759 0.486912 297 298 299 300 301 302 303 304 0.100273 0.148814 0.687570 1.345740 0.715567 0.279858 0.378334 0.915172 305 306 307 308 309 310 311 312 0.419958 -0.021885 1.030695 0.111984 -0.141435 0.563533 0.417721 1.026337 313 314 315 316 317 318 319 320 1.080643 0.612975 1.037286 0.596754 0.696704 0.083885 0.647842 -0.025468 321 322 323 324 325 326 327 328 1.017387 0.422978 0.065000 0.942615 0.825299 0.140222 -0.534634 0.899883 329 330 331 332 333 334 335 336 1.010267 1.350411 0.857660 1.564046 0.329915 0.479373 0.329040 0.136461 337 338 339 340 341 342 343 344 0.736029 -0.060361 1.036646 0.141435 0.557530 0.376676 0.943921 0.714717 345 346 347 348 349 350 351 352 0.548586 0.365959 1.292435 0.401489 -0.789365 1.361181 1.040667 0.324294 353 354 355 356 357 358 359 360 0.164501 0.135797 0.296365 0.300318 0.765837 0.020126 -0.497368 -0.028626 361 362 363 364 365 366 367 368 0.054520 0.354549 0.296578 0.582519 0.216740 0.507197 -0.026776 0.260623 369 370 371 372 373 374 375 376 -0.983330 0.560088 -0.542305 0.377692 0.418716 0.999872 0.232494 0.128161 377 378 379 380 381 382 383 384 0.369120 0.799931 0.884801 -0.195973 1.176254 0.874432 1.174457 0.568434 385 386 387 388 389 390 391 392 0.872826 1.845795 0.717429 0.008777 0.765068 0.625094 -0.479358 1.098940 393 394 395 396 397 398 399 400 0.105897 0.363912 0.564580 0.000280 -0.480084 1.743663 1.695440 1.482967 401 402 403 404 405 406 407 408 0.311150 -0.026234 0.039895 -0.172900 0.673024 0.641334 0.768818 0.131991 409 410 411 412 413 414 415 416 0.837671 0.044811 0.628231 -1.112495 0.254031 0.536840 1.302326 0.165439 417 418 419 420 421 422 423 424 1.771632 -0.466188 0.034239 0.661275 0.202893 0.300641 0.525077 -0.731506 425 426 427 428 429 430 431 432 1.130409 1.287068 0.602823 0.289174 -0.070247 0.146156 -1.052570 -0.159243 433 434 435 436 437 438 439 440 -0.157305 -0.136740 -0.425104 -0.455276 0.009937 -0.160630 -0.236836 0.065843 441 442 443 444 445 446 447 448 -0.377222 0.292762 -0.433648 -1.141324 -0.942204 -0.120567 -0.141638 0.041598 449 450 451 452 453 454 455 456 0.073986 0.008944 -0.523856 0.946370 0.693888 0.701719 0.175407 -0.208560 457 458 459 460 461 462 463 464 -1.073211 0.392462 0.045817 -0.302815 0.098236 -0.168460 -1.651708 0.216881 465 466 467 468 469 470 471 472 -0.477071 0.447118 -0.206923 -0.275390 -0.354992 1.340845 -0.907837 0.226153 473 474 475 476 477 478 479 480 -0.164515 -0.417284 0.349587 0.132337 -0.907469 -0.572945 -0.299061 0.001236 481 482 483 484 485 486 487 488 0.226113 -0.128211 1.529157 -0.771303 -1.500365 0.514280 -0.821757 0.453786 489 490 491 492 493 494 495 496 -0.232551 0.316741 -0.240930 0.369310 0.191912 -0.210951 0.106456 -0.129342 497 498 499 500 501 502 503 504 -0.532214 -0.853364 -0.199265 -0.512932 -0.485976 0.428564 0.055742 -0.146316 505 506 507 508 509 510 511 512 -0.494808 -0.342878 -0.562279 0.263994 0.147454 -0.415081 -0.307130 -0.422778 513 514 515 516 517 518 519 520 -0.538743 -0.996300 -0.017060 -0.171616 -0.414166 0.109649 0.278605 -0.704754 521 522 523 524 525 526 527 528 0.128792 -0.194273 0.011303 0.064553 -0.774003 -0.639232 -0.064892 -0.253706 529 530 531 532 533 534 535 536 -0.109741 -0.188569 0.425403 0.573150 0.003073 0.162607 0.201446 -0.527024 537 538 539 540 541 542 543 544 -0.376813 -0.748043 -0.126345 -0.492955 -0.674133 -0.248664 -0.200214 -0.875329 545 546 547 548 549 550 551 552 0.014158 0.216208 -0.065279 -0.228612 -0.616869 0.159455 -0.705329 -0.433138 553 554 555 556 557 558 559 560 1.172398 0.686995 -0.943301 -0.206740 0.043745 -0.510883 -0.734735 -0.034932 561 562 563 564 565 566 567 568 -0.431650 -0.004659 1.430976 -0.084197 -0.312865 0.234213 -0.726849 0.019264 569 570 571 572 573 574 575 576 -0.228248 0.182938 -0.193606 0.180263 0.846385 0.837936 0.449443 -0.457224 577 578 579 580 581 582 583 584 -0.345253 0.233748 1.248414 -0.164977 0.318509 1.133962 -0.343229 0.255988 585 586 587 588 589 590 591 592 0.226473 -0.341931 0.354197 -0.369039 -0.148621 -0.205801 -0.412425 -0.259919 593 594 595 596 597 598 599 600 -1.013738 -0.595600 -1.814676 0.200192 -0.549905 -0.504562 0.754290 -0.660366 601 602 603 604 605 606 607 608 -0.057789 0.052886 -0.140023 -0.307357 -0.153625 0.481918 -0.052441 -0.257925 609 610 611 612 613 614 615 616 -0.548470 -3.527112 0.026032 -0.490729 -1.897411 -0.197224 0.235806 -0.375850 617 618 619 620 621 622 623 624 -0.193496 0.667872 0.260546 -0.060543 -0.526829 -0.483633 -0.103216 0.132837 625 626 627 628 629 630 631 632 -0.061629 1.201370 -0.071009 -0.193145 -0.654991 -0.076982 -0.619028 -0.629310 633 634 635 636 637 638 639 640 0.019308 -0.364196 0.107078 0.147515 -0.402404 -0.134269 0.183482 -2.206740 641 642 643 644 645 646 647 648 1.439426 -0.079453 -0.087195 0.088985 -0.131008 -1.786672 -0.705431 0.102809 649 650 651 652 653 654 655 656 0.236881 -0.524780 -0.020226 -0.015752 0.288811 -0.734826 -0.110003 -0.739856 657 658 659 660 661 662 663 664 -0.277712 -0.614708 0.624806 -0.138595 -0.700819 -0.167414 -0.588587 0.036114 665 666 667 668 669 670 671 672 1.656564 0.261554 0.638543 0.152679 0.359758 0.525970 -0.003988 0.534416 673 674 675 676 677 678 679 680 -0.447690 0.358524 -0.330444 -0.438275 0.748431 -0.284200 0.070251 -1.897367 681 682 683 684 685 686 687 688 -0.244887 -0.653605 2.191348 -0.350393 -0.311875 0.220081 -0.390530 0.144839 689 690 691 692 693 694 695 696 -0.247716 -0.447034 -0.394931 0.931166 0.053135 0.242517 -0.195993 0.239903 697 698 699 700 701 702 703 704 0.038955 0.137559 0.587133 0.901795 -0.632276 0.347823 0.269673 0.730522 705 706 707 708 709 710 711 712 0.882062 -0.479552 -0.134075 0.290250 -0.151258 0.330834 -0.204276 -0.844745 713 714 715 716 717 718 719 720 0.265436 -0.680656 0.537779 0.206238 -0.129536 -0.397757 0.129528 -1.239803 721 722 723 724 725 726 727 728 0.255385 -0.138360 1.479236 0.219590 -0.434784 -0.898999 -0.164336 -1.654435 729 730 731 732 733 734 735 736 -4.364028 -0.232989 -0.479976 -0.075269 -0.130575 0.267241 -0.266208 0.006842 737 738 739 740 741 742 743 744 0.169090 0.305904 0.444912 0.092765 1.009792 -0.031233 -0.659959 -0.649883 745 746 747 748 749 750 751 752 -0.615305 -0.819730 -0.430498 -0.135804 -0.186646 0.063072 0.905963 -0.068128 753 -0.478444 > all.equal( linearPredictors( lfpResult ), predict( lfpResult ) ) [1] TRUE > all.equal( linearPredictors( lfpResult )[ 11:222 ], + predict( lfpResult, newdata = Mroz87[ 11:222, ] ) ) [1] TRUE > residuals( lfpResult, type = "response" ) 1 2 3 4 5 6 7 8 3.87e-01 3.50e-01 3.33e-01 3.33e-01 2.06e-01 2.09e-01 1.09e-01 6.57e-01 9 10 11 12 13 14 15 16 2.90e-01 2.37e-01 2.39e-01 3.61e-01 5.88e-01 6.53e-01 4.03e-01 5.83e-01 17 18 19 20 21 22 23 24 3.47e-01 4.33e-01 2.75e-01 1.72e-01 2.56e-01 1.48e-01 3.12e-01 3.03e-01 25 26 27 28 29 30 31 32 3.22e-01 3.37e-01 6.09e-01 3.09e-01 6.17e-02 2.33e-01 3.50e-01 5.57e-01 33 34 35 36 37 38 39 40 1.78e-01 5.29e-01 2.32e-01 4.67e-01 1.17e-01 2.51e-01 1.59e-01 4.05e-02 41 42 43 44 45 46 47 48 7.79e-01 2.72e-01 4.79e-01 4.63e-01 4.85e-01 5.17e-01 2.01e-01 1.23e-01 49 50 51 52 53 54 55 56 4.67e-01 3.40e-01 3.50e-01 5.00e-01 5.15e-01 6.63e-01 4.84e-01 9.63e-02 57 58 59 60 61 62 63 64 1.69e-01 2.83e-02 2.31e-01 7.25e-01 2.81e-01 3.40e-01 3.90e-01 2.88e-01 65 66 67 68 69 70 71 72 3.34e-01 3.15e-01 3.26e-01 3.56e-01 3.50e-01 7.07e-01 2.88e-01 4.95e-01 73 74 75 76 77 78 79 80 3.15e-01 2.59e-01 2.83e-01 1.58e-01 6.66e-01 6.76e-02 3.57e-01 5.79e-01 81 82 83 84 85 86 87 88 7.89e-01 5.37e-01 6.10e-01 9.34e-01 3.05e-01 8.34e-02 3.45e-01 5.61e-01 89 90 91 92 93 94 95 96 3.52e-01 5.18e-01 2.66e-01 5.53e-01 1.92e-01 5.53e-01 5.55e-01 4.27e-01 97 98 99 100 101 102 103 104 3.89e-01 3.29e-01 2.74e-01 1.68e-01 2.38e-01 3.81e-01 4.83e-01 5.08e-02 105 106 107 108 109 110 111 112 4.66e-01 5.09e-01 1.08e-01 4.62e-01 2.01e-01 6.56e-01 2.58e-01 3.59e-01 113 114 115 116 117 118 119 120 3.65e-01 4.58e-02 1.67e-01 4.19e-01 2.70e-01 7.46e-02 1.27e-01 2.84e-01 121 122 123 124 125 126 127 128 4.79e-01 2.78e-01 3.90e-01 9.94e-02 7.36e-02 4.49e-01 4.47e-01 3.30e-01 129 130 131 132 133 134 135 136 3.36e-01 6.39e-01 4.58e-01 4.73e-01 4.40e-01 1.40e-01 4.72e-01 4.65e-01 137 138 139 140 141 142 143 144 3.92e-01 2.33e-01 5.73e-01 2.72e-01 2.60e-01 2.11e-03 3.91e-01 5.38e-01 145 146 147 148 149 150 151 152 7.21e-01 2.69e-01 4.49e-01 4.09e-02 5.50e-01 1.07e-01 2.15e-01 3.24e-01 153 154 155 156 157 158 159 160 2.65e-01 1.81e-01 9.81e-02 1.36e-01 9.71e-01 9.56e-02 1.90e-01 3.17e-01 161 162 163 164 165 166 167 168 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0.63087 1.07944 2.45247 257 258 259 260 261 262 263 264 1.17203 1.40436 1.50959 0.74450 1.94512 0.10247 0.86314 0.55587 265 266 267 268 269 270 271 272 0.66421 1.09301 0.83939 1.22913 0.38730 0.65494 0.42923 1.04007 273 274 275 276 277 278 279 280 2.03494 0.64211 0.35137 0.51953 0.63352 1.13673 0.37275 0.51602 281 282 283 284 285 286 287 288 0.84909 0.77017 0.42811 1.38285 0.14435 0.67847 0.54685 0.58158 289 290 291 292 293 294 295 296 1.40234 0.64459 0.68913 0.47047 0.05850 0.61047 0.65500 0.67524 297 298 299 300 301 302 303 304 0.92308 0.88794 0.57098 0.31293 0.55753 0.79924 0.73798 0.46860 305 306 307 308 309 310 311 312 0.71336 1.01762 0.42229 0.91448 1.11958 0.63373 0.71467 0.42398 313 314 315 316 317 318 319 320 0.40335 0.60808 0.41976 0.61640 0.56656 0.93524 0.59051 1.02053 321 322 323 324 325 326 327 328 0.42745 0.71160 0.94945 0.45728 0.50718 0.89406 1.54053 0.47500 329 330 331 332 333 334 335 336 0.43022 0.31149 0.49302 0.25018 0.76756 0.67944 0.76810 0.89676 337 338 339 340 341 342 343 344 0.54786 1.04935 0.42000 0.89319 0.63690 0.73898 0.45674 0.55793 345 346 347 348 349 350 351 352 0.64166 0.74545 0.32981 0.72420 1.91109 0.30816 0.41846 0.77106 353 354 355 356 357 358 359 360 0.87685 0.89723 0.78867 0.78615 0.53400 0.98407 1.49378 1.02310 361 362 363 364 365 366 367 368 0.95743 0.75238 0.78853 0.62378 0.84088 0.66403 1.02159 0.81173 369 370 371 372 373 374 375 376 2.26835 0.63555 1.55036 0.73837 0.71408 0.43430 0.83031 0.90273 377 378 379 380 381 382 383 384 0.74353 0.51849 0.48138 1.16957 0.36883 0.48580 0.36946 0.63115 385 386 387 388 389 390 391 392 0.48649 0.18317 0.55664 0.99302 0.53436 0.60193 1.47178 0.39657 393 394 395 396 397 398 399 400 0.91894 0.74669 0.63318 0.99978 1.47266 0.20574 0.21706 0.27233 401 402 403 404 405 406 407 408 0.77930 1.02115 0.96867 1.14814 0.57807 0.59375 0.53263 0.89997 409 410 411 412 413 414 415 416 0.50173 0.96487 0.60034 2.55361 0.81605 0.64794 0.32663 0.87620 417 418 419 420 421 422 423 424 0.19937 1.45593 0.97305 0.58384 0.85028 0.78595 0.65429 1.81824 425 426 427 428 429 430 431 432 0.38511 0.33154 0.61328 0.79326 -0.94548 -1.12381 -0.41392 -0.88055 433 434 435 436 437 438 439 440 -0.88192 -0.89656 -0.71037 -0.69303 -1.00796 -0.87958 -0.82741 -1.05395 441 442 443 444 445 446 447 448 -0.73865 -1.26428 -0.70542 -0.38118 -0.45744 -0.90823 -0.89305 -1.03375 449 450 451 452 453 454 455 456 -1.06083 -1.00716 -0.65495 -2.19423 -1.76081 -1.77258 -1.15045 -0.84642 457 458 459 460 461 462 463 464 -0.40613 -1.37072 -1.03724 -0.78457 -1.08157 -0.87408 -0.22771 -1.18936 465 466 467 468 469 470 471 472 -0.68073 -1.43332 -0.84754 -0.80213 -0.75211 -3.18008 -0.47166 -1.19825 473 474 475 476 477 478 479 480 -0.87684 -0.71492 -1.32377 -1.11146 -0.47182 -0.62878 -0.78695 -1.00099 481 482 483 484 485 486 487 488 -1.19821 -0.90269 -3.85288 -0.53149 -0.26746 -1.51479 -0.50875 -1.44118 489 490 491 492 493 494 495 496 -0.83027 -1.28901 -0.82469 -1.34514 -1.16577 -0.84480 -1.08870 -0.90188 497 498 499 500 501 502 503 504 -0.65043 -0.49488 -0.85276 -0.66089 -0.67576 -1.41171 -1.04549 -0.88971 505 506 507 508 509 510 511 512 -0.67086 -0.75954 -0.63439 -1.23529 -1.12498 -0.71621 -0.78184 -0.71172 513 514 515 516 517 518 519 520 -0.64692 -0.43571 -0.98648 -0.87187 -0.71674 -1.09148 -1.24992 -0.56269 521 522 523 524 525 526 527 528 -1.10831 -0.85618 -1.00906 -1.05287 -0.53026 -0.59481 -0.94953 -0.81626 529 530 531 532 533 534 535 536 -0.91612 -0.86011 -1.40807 -1.59065 -1.00245 -1.13871 -1.17472 -0.65323 537 538 539 540 541 542 543 544 -0.73890 -0.54224 -0.90404 -0.67188 -0.57752 -0.81958 -0.85211 -0.48542 545 546 547 548 549 550 551 552 -1.01136 -1.18872 -0.94924 -0.83290 -0.60610 -1.13584 -0.56242 -0.70571 553 554 555 556 557 558 559 560 -2.70138 -1.75052 -0.45699 -0.84766 -1.03552 -0.66201 -0.54846 -0.97251 561 562 563 564 565 566 567 568 -0.70657 -0.99629 -3.48140 -0.93501 -0.77822 -1.20604 -0.55218 -1.01549 569 570 571 572 573 574 575 576 -0.83314 -1.15741 -0.85664 -1.15493 -2.00836 -1.99357 -1.43606 -0.69192 577 578 579 580 581 582 583 584 -0.75808 -1.20559 -2.90505 -0.87652 -1.29086 -2.60535 -0.75932 -1.22735 585 586 587 588 589 590 591 592 -1.19856 -0.76012 -1.32873 -0.74358 -0.88807 -0.84830 -0.71776 -0.81219 593 594 595 596 597 598 599 600 -0.42887 -0.61700 -0.18984 -1.17354 -0.64095 -0.66548 -1.85413 -0.58429 601 602 603 604 605 606 607 608 -0.95493 -1.04311 -0.89420 -0.78170 -0.88452 -1.47489 -0.95902 -0.81349 609 610 611 612 613 614 615 616 -0.64172 -0.01449 -1.02099 -0.67312 -0.17247 -0.85416 -1.20758 -0.73948 617 618 619 620 621 622 623 624 -0.85671 -1.72237 -1.23186 -0.95283 -0.65334 -0.67706 -0.92091 -1.11190 625 626 627 628 629 630 631 632 -0.95201 -2.77678 -0.94491 -0.85696 -0.58695 -0.94041 -0.60500 -0.59980 633 634 635 636 637 638 639 640 -1.01553 -0.74651 -1.08924 -1.12504 -0.72366 -0.89833 -1.15792 -0.11771 641 642 643 644 645 646 647 648 -3.51151 -0.93856 -0.93277 -1.07361 -0.90067 -0.19600 -0.56237 -1.08553 649 650 651 652 653 654 655 656 -1.20863 -0.65445 -0.98399 -0.98751 -1.26025 -0.54842 -0.91593 -0.54606 657 658 659 660 661 662 663 664 -0.80063 -0.60720 -1.66093 -0.89523 -0.56458 -0.87481 -0.62062 -1.02924 665 666 667 668 669 670 671 672 -4.41477 -1.23286 -1.68024 -1.12970 -1.33474 -1.52951 -0.99682 -1.54025 673 674 675 676 677 678 679 680 -0.69735 -1.33340 -0.76723 -0.70275 -1.84482 -0.79645 -1.05767 -0.17248 681 682 683 684 685 686 687 688 -0.82207 -0.58764 -8.32806 -0.75493 -0.77885 -1.19242 -0.73069 -1.12263 689 690 691 692 693 694 695 696 -0.82020 -0.69773 -0.72808 -2.16462 -1.04331 -1.21412 -0.85500 -1.21157 697 698 699 700 701 702 703 704 -1.03157 -1.11611 -1.60933 -2.10882 -0.59830 -1.32187 -1.24095 -1.81671 705 706 707 708 709 710 711 712 -2.07234 -0.67934 -0.89847 -1.26172 -0.88620 -1.30380 -0.84934 -0.49863 713 714 715 716 717 718 719 720 -1.23672 -0.57434 -1.54455 -1.17924 -0.90174 -0.72640 -1.10897 -0.34710 721 722 723 724 725 726 727 728 -1.22676 -0.89539 -3.65794 -1.19195 -0.70476 -0.47537 -0.87697 -0.22704 729 730 731 732 733 734 735 736 -0.00253 -0.82998 -0.67910 -0.94170 -0.90099 -1.23852 -0.80808 -1.00547 737 738 739 740 741 742 743 744 -1.14464 -1.27777 -1.43073 -1.07685 -2.32337 -0.97539 -0.58449 -0.58949 745 746 747 748 749 750 751 752 -0.60689 -0.50965 -0.70724 -0.89723 -0.86143 -1.05162 -2.11663 -0.94708 753 -0.67996 > residuals( lfpResult, type = "deviance" ) 1 2 3 4 5 6 7 8 0.98927 0.92848 0.90071 0.89921 0.67989 0.68423 0.47984 1.46272 9 10 11 12 13 14 15 16 0.82703 0.73514 0.73962 0.94632 1.33175 1.45548 1.01643 1.32324 17 18 19 20 21 22 23 24 0.92397 1.06505 0.80189 0.61360 0.76847 0.56618 0.86403 0.85000 25 26 27 28 29 30 31 32 0.88123 0.90666 1.37109 0.85915 0.35685 0.72893 0.92767 1.27691 33 34 35 36 37 38 39 40 0.62526 1.22764 0.72613 1.12254 0.49950 0.75976 0.58794 0.28741 41 42 43 44 45 46 47 48 1.73818 0.79735 1.14132 1.11518 1.15181 1.20721 0.67002 0.51271 49 50 51 52 53 54 55 56 1.12225 0.91208 0.92850 1.17771 1.20333 1.47543 1.15113 0.45003 57 58 59 60 61 62 63 64 0.60806 0.23981 0.72559 1.60691 0.81147 0.91097 0.99443 0.82389 65 66 67 68 69 70 71 72 0.90098 0.87016 0.88887 0.93831 0.92754 1.56594 0.82375 1.16922 73 74 75 76 77 78 79 80 0.86912 0.77471 0.81646 0.58736 1.48139 0.37408 0.93901 1.31492 81 82 83 84 85 86 87 88 1.76282 1.24062 1.37145 2.32983 0.85292 0.41744 0.92040 1.28335 89 90 91 92 93 94 95 96 0.93135 1.20833 0.78675 1.26842 0.65316 1.26929 1.27282 1.05454 97 98 99 100 101 102 103 104 0.99215 0.89273 0.79974 0.60638 0.73792 0.97899 1.14940 0.32296 105 106 107 108 109 110 111 112 1.11987 1.19346 0.47876 1.11299 0.67024 1.45998 0.77198 0.94363 113 114 115 116 117 118 119 120 0.95258 0.30611 0.60533 1.04291 0.79323 0.39380 0.52075 0.81738 121 122 123 124 125 126 127 128 1.14191 0.80784 0.99484 0.45748 0.39113 1.09146 1.08823 0.89535 129 130 131 132 133 134 135 136 0.90418 1.42825 1.10632 1.13187 1.07731 0.54902 1.13023 1.11878 137 138 139 140 141 142 143 144 0.99774 0.72882 1.30485 0.79674 0.77546 0.06504 0.99592 1.24340 145 146 147 148 149 150 151 152 1.59684 0.79242 1.09177 0.28888 1.26287 0.47635 0.69638 0.88483 153 154 155 156 157 158 159 160 0.78547 0.63199 0.45438 0.54160 2.66422 0.44832 0.65008 0.87264 161 162 163 164 165 166 167 168 1.21460 0.68259 1.46520 0.95872 0.72692 1.38313 0.28695 0.48728 169 170 171 172 173 174 175 176 0.97353 0.72112 0.80559 1.44963 0.66219 0.66763 1.13783 1.33321 177 178 179 180 181 182 183 184 0.60990 0.83672 1.54062 1.17467 0.94771 1.10422 0.58439 0.78226 185 186 187 188 189 190 191 192 1.33678 1.00471 0.35521 0.36275 1.43079 0.70973 1.23807 1.57283 193 194 195 196 197 198 199 200 1.21251 0.87969 1.00793 1.10389 0.48800 1.28305 0.61745 0.64630 201 202 203 204 205 206 207 208 0.76643 0.61524 0.21422 1.45200 0.84493 1.22655 0.95288 0.85966 209 210 211 212 213 214 215 216 0.59040 0.42798 0.91400 0.85501 0.86725 0.90195 0.67988 0.57608 217 218 219 220 221 222 223 224 1.07395 0.92140 1.16298 1.09454 0.53627 0.94751 0.83406 1.28419 225 226 227 228 229 230 231 232 0.52791 0.84885 1.00252 0.52451 0.76012 0.84706 0.74119 1.69556 233 234 235 236 237 238 239 240 0.71810 0.77591 0.59352 0.46695 2.01890 0.87049 0.87917 0.43027 241 242 243 244 245 246 247 248 1.29894 0.82921 1.05571 1.07544 1.31541 1.24865 1.17079 0.63913 249 250 251 252 253 254 255 256 1.03577 0.30809 0.50597 1.22407 0.80531 0.81859 1.24299 1.97383 257 258 259 260 261 262 263 264 1.31486 1.47601 1.54110 0.93916 1.76928 0.14453 1.05523 0.73384 265 266 267 268 269 270 271 272 0.85493 1.25382 1.03278 1.35690 0.52870 0.84492 0.58155 1.21096 273 274 275 276 277 278 279 280 1.80955 0.83093 0.48252 0.69130 0.82151 1.28801 0.51010 0.68713 281 282 283 284 285 286 287 288 1.04199 0.96512 0.58016 1.46214 0.20309 0.87024 0.72336 0.76335 289 290 291 292 293 294 295 296 1.47472 0.83364 0.88158 0.63237 0.08266 0.79594 0.84498 0.86678 297 298 299 300 301 302 303 304 1.11023 1.07827 0.75124 0.43226 0.73575 0.99394 0.93250 0.63010 305 306 307 308 309 310 311 312 0.90705 1.19228 0.57289 1.10249 1.27473 0.82173 0.90841 0.57499 313 314 315 316 317 318 319 320 0.54907 0.79327 0.56971 0.80255 0.74617 1.12110 0.77348 1.19472 321 322 323 324 325 326 327 328 0.57933 0.90522 1.13368 0.61624 0.67663 1.08390 1.55941 0.63789 329 330 331 332 333 334 335 336 0.58279 0.43035 0.65967 0.34845 0.96249 0.87127 0.96304 1.08637 337 338 339 340 341 342 343 344 0.72454 1.21859 0.57002 1.08311 0.82522 0.93352 0.61559 0.73622 345 346 347 348 349 350 351 352 0.83043 0.94013 0.45443 0.91830 1.75348 0.42596 0.56809 0.96600 353 354 355 356 357 358 359 360 1.06803 1.08681 0.98352 0.98103 0.70835 1.16380 1.53161 1.19687 361 362 363 364 365 366 367 368 1.14069 0.94718 0.98339 0.81075 1.03420 0.85475 1.19561 1.00614 369 370 371 372 373 374 375 376 1.90563 0.82374 1.56515 0.93289 0.90780 0.58786 1.02409 1.09182 377 378 379 380 381 382 383 384 0.93818 0.69007 0.64563 1.31301 0.50507 0.65097 0.50587 0.81889 385 386 387 388 389 390 391 392 0.65180 0.25690 0.73473 1.17147 0.70877 0.78636 1.51823 0.54049 393 394 395 396 397 398 399 400 1.10651 0.94139 0.82113 1.17722 1.51877 0.28795 0.30345 0.37826 401 402 403 404 405 406 407 408 0.97423 1.19524 1.15050 1.29677 0.75935 0.77716 0.70675 1.08931 409 410 411 412 413 414 415 416 0.67012 1.14720 0.78458 2.00882 1.01033 0.83730 0.45027 1.06742 417 418 419 420 421 422 423 424 0.27920 1.50847 1.15430 0.76593 1.04312 0.98083 0.84420 1.70880 425 426 427 428 429 430 431 432 0.52590 0.45670 0.79907 0.98805 -1.13018 -1.27802 -0.56239 -1.07146 433 434 435 436 437 438 439 440 -1.07272 -1.08619 -0.90393 -0.88570 -1.18415 -1.07055 -1.02131 -1.22235 441 442 443 444 445 446 447 448 -0.93318 -1.38195 -0.89875 -0.52090 -0.61645 -1.09682 -1.08297 -1.20573 449 450 451 452 453 454 455 456 -1.22795 -1.18348 -0.84492 -1.87637 -1.67994 -1.68593 -1.29853 -1.03947 457 458 459 460 461 462 463 464 -0.55258 -1.45423 -1.20862 -0.97946 -1.24470 -1.06545 -0.31797 -1.32780 465 466 467 468 469 470 471 472 -0.87265 -1.49438 -1.04052 -0.99677 -0.94691 -2.19459 -0.63383 -1.33438 473 474 475 476 477 478 479 480 -1.06802 -0.90868 -1.42300 -1.26838 -0.63402 -0.81628 -0.98182 -1.17825 481 482 483 484 485 486 487 488 -1.33435 -1.09179 -2.35067 -0.70541 -0.37173 -1.54420 -0.67850 -1.49930 489 490 491 492 493 494 495 496 -1.02405 -1.39922 -1.01869 -1.43734 -1.31015 -1.03793 -1.25039 -1.09105 497 498 499 500 501 502 503 504 -0.84001 -0.66191 -1.04547 -0.85136 -0.86734 -1.48071 -1.21542 -1.07991 505 506 507 508 509 510 511 512 -0.86209 -0.95442 -0.82246 -1.36133 -1.27893 -0.91002 -0.97675 -0.90534 513 514 515 516 517 518 519 520 -0.83619 -0.58961 -1.16587 -1.06340 -0.91057 -1.25260 -1.37179 -0.74172 521 522 523 524 525 526 527 528 -1.26591 -1.04869 -1.18508 -1.22147 -0.70395 -0.77835 -1.13376 -1.01054 529 530 531 532 533 534 535 536 -1.10397 -1.05239 -1.47839 -1.58830 -1.17949 -1.28954 -1.31688 -0.84306 537 538 539 540 541 542 543 544 -0.93344 -0.71799 -1.09302 -0.86319 -0.75873 -1.01375 -1.04485 -0.65051 545 546 547 548 549 550 551 552 -1.18702 -1.32732 -1.13350 -1.02658 -0.79105 -1.28733 -0.74140 -0.89906 553 554 555 556 557 558 559 560 -2.05716 -1.67466 -0.61590 -1.04064 -1.20720 -0.85257 -0.72524 -1.15383 561 562 563 564 565 566 567 568 -0.89996 -1.17425 -2.26899 -1.12089 -0.97316 -1.34010 -0.72956 -1.19049 569 570 571 572 573 574 575 576 -1.02681 -1.30383 -1.04913 -1.30194 -1.79784 -1.79125 -1.49610 -0.88453 577 578 579 580 581 582 583 584 -0.95295 -1.33977 -2.11891 -1.06772 -1.40050 -2.02611 -0.95421 -1.35561 585 586 587 588 589 590 591 592 -1.33460 -0.95501 -1.42634 -0.93823 -1.07840 -1.04125 -0.91163 -1.00658 593 594 595 596 597 598 599 600 -0.58110 -0.80322 -0.26611 -1.31599 -0.82966 -0.85631 -1.72636 -0.76644 601 602 603 604 605 606 607 608 -1.13850 -1.21346 -1.08403 -0.97661 -1.07513 -1.52013 -1.14208 -1.00785 609 610 611 612 613 614 615 616 -0.83050 -0.02050 -1.19510 -0.86451 -0.24213 -1.04679 -1.34123 -0.93403 617 618 619 620 621 622 623 624 -1.04920 -1.66006 -1.35887 -1.13666 -0.84317 -0.86873 -1.10828 -1.26873 625 626 627 628 629 630 631 632 -1.13594 -2.08064 -1.12967 -1.04942 -0.76946 -1.12569 -0.78982 -0.78397 633 634 635 636 637 638 639 640 -1.19052 -0.94121 -1.25082 -1.27897 -0.91774 -1.08781 -1.30421 -0.16589 641 642 643 644 645 646 647 648 -2.27599 -1.12405 -1.11890 -1.23830 -1.08995 -0.27458 -0.74134 -1.24786 649 650 651 652 653 654 655 656 -1.34200 -0.84438 -1.16373 -1.16675 -1.37911 -0.72520 -1.10379 -0.72245 657 658 659 660 661 662 663 664 -0.99530 -0.79228 -1.62731 -1.08497 -0.74389 -1.06613 -0.80725 -1.20198 665 666 667 668 669 670 671 672 -2.45762 -1.35959 -1.63774 -1.28259 -1.43039 -1.55293 -1.17471 -1.55924 673 674 675 676 677 678 679 680 -0.89027 -1.42949 -0.96217 -0.89595 -1.72184 -0.99119 -1.22538 -0.24214 681 682 683 684 685 686 687 688 -1.01616 -0.77024 -2.91670 -0.94976 -0.97378 -1.33007 -0.92501 -1.27710 689 690 691 692 693 694 695 696 -1.01436 -0.89067 -0.92231 -1.86437 -1.21363 -1.34601 -1.04758 -1.34415 697 698 699 700 701 702 703 704 -1.20392 -1.27202 -1.59883 -1.84124 -0.78229 -1.42172 -1.36539 -1.70805 705 706 707 708 709 710 711 712 -1.82575 -0.87117 -1.08794 -1.38015 -1.07668 -1.40941 -1.04223 -0.66641 713 714 715 716 717 718 719 720 -1.36236 -0.75509 -1.56176 -1.32027 -1.09092 -0.92058 -1.26642 -0.47698 721 722 723 724 725 726 727 728 -1.35518 -1.08512 -2.30906 -1.32972 -0.89806 -0.63834 -1.06814 -0.31705 729 730 731 732 733 734 735 736 -0.00357 -1.02377 -0.87091 -1.12684 -1.09024 -1.36365 -1.00259 -1.18205 737 738 739 740 741 742 743 744 -1.29409 -1.39141 -1.49275 -1.24091 -1.92665 -1.15632 -0.76667 -0.77233 745 746 747 748 749 750 751 752 -0.79194 -0.67957 -0.90066 -1.08680 -1.05363 -1.22045 -1.84452 -1.13160 753 -0.87183 > > # estimation with glm() > lfpResult2 <- glm( lfp ~ kids + age30.39 + age50.60 + educ + hushrs + + huseduc + huswage + mtr + motheduc, data = Mroz87, + family = binomial( link = "probit" ) ) > all.equal( coef( lfpResult ), coef( lfpResult2 ), tol = 1e-3 ) [1] TRUE > all.equal( stdEr( lfpResult ), stdEr( lfpResult2 ), tol = 1e-1 ) [1] TRUE > all.equal( logLik( lfpResult ), logLik( lfpResult2 ) ) [1] TRUE > all.equal( lrtest( lfpResult ), lrtest( lfpResult2 ) ) [1] TRUE > all.equal( lrtest( lfpResult, lfp ~ age50.60 + educ + hushrs + huswage + mtr ), + lrtest( lfpResult2, lfp ~ age50.60 + educ + hushrs + huswage + mtr ), + tol = 1e-7 ) [1] TRUE > all.equal( model.frame( lfpResult ), model.frame( lfpResult2 ) ) [1] TRUE > all.equal( model.matrix( lfpResult ), model.matrix( lfpResult2 ) ) [1] TRUE > all.equal( fitted( lfpResult ), fitted( lfpResult2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( lfpResult, type = "response" ), + predict( lfpResult2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( predict( lfpResult, newdata = Mroz87[ 5:333, ], type = "response" ), + predict( lfpResult2, newdata = Mroz87[ 5:333, ], type = "response" ), + tol = 1e-4 ) [1] TRUE > all.equal( predict( lfpResult ), predict( lfpResult2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( lfpResult, newdata = Mroz87[ 2:444, ] ), + predict( lfpResult2, newdata = Mroz87[ 2:444, ] ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( lfpResult, type = "response" ), + residuals( lfpResult2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( lfpResult, type = "pearson" ), + residuals( lfpResult2, type = "pearson" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( lfpResult, type = "deviance" ), + residuals( lfpResult2, type = "deviance" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( lfpResult ), residuals( lfpResult2 ), tol = 1e-4 ) [1] TRUE > > > ## Greene( 2003 ): example 22.8, page 786 (only probit part ) > greene <- probit( lfp ~ age + I( age^2 ) + faminc + kids + educ, data = Mroz87 ) > print( greene ) Call: probit(formula = lfp ~ age + I(age^2) + faminc + kids + educ, data = Mroz87) Coefficients: (Intercept) age I(age^2) faminc kidsTRUE educ -4.16e+00 1.85e-01 -2.43e-03 4.58e-06 -4.49e-01 9.82e-02 > summary( greene ) -------------------------------------------- Probit binary choice model/Maximum Likelihood estimation Newton-Raphson maximisation, 4 iterations Return code 1: gradient close to zero (gradtol) Log-Likelihood: -491 Model: Y == '1' in contrary to '0' 753 observations (325 'negative' and 428 'positive') and 6 free parameters (df = 747) Estimates: Estimate Std. error t value Pr(> t) (Intercept) -4.16e+00 1.40e+00 -2.96 0.0030 ** age 1.85e-01 6.60e-02 2.81 0.0049 ** I(age^2) -2.43e-03 7.74e-04 -3.14 0.0017 ** faminc 4.58e-06 4.21e-06 1.09 0.2762 kidsTRUE -4.49e-01 1.31e-01 -3.43 0.0006 *** educ 9.82e-02 2.30e-02 4.27 1.9e-05 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significance test: chi2(5) = 48.1 (p=3.47e-09) -------------------------------------------- > coef( greene ) (Intercept) age I(age^2) faminc kidsTRUE educ -4.16e+00 1.85e-01 -2.43e-03 4.58e-06 -4.49e-01 9.82e-02 > stdEr( greene ) (Intercept) age I(age^2) faminc kidsTRUE educ 1.40e+00 6.60e-02 7.74e-04 4.21e-06 1.31e-01 2.30e-02 > vcov( greene ) (Intercept) age I(age^2) faminc kidsTRUE educ (Intercept) 1.97e+00 -8.95e-02 1.02e-03 6.41e-07 1.89e-02 -8.61e-03 age -8.95e-02 4.35e-03 -5.07e-05 -2.51e-08 -2.00e-03 1.24e-04 I(age^2) 1.02e-03 -5.07e-05 5.98e-07 2.48e-10 2.95e-05 -1.24e-06 faminc 6.41e-07 -2.51e-08 2.48e-10 1.77e-11 -4.14e-08 -3.38e-08 kidsTRUE 1.89e-02 -2.00e-03 2.95e-05 -4.14e-08 1.71e-02 -2.44e-05 educ -8.61e-03 1.24e-04 -1.24e-06 -3.38e-08 -2.44e-05 5.28e-04 > nobs( greene ) [1] 753 > nObs( greene ) [1] 753 > df.residual( greene ) [1] 747 > logLik( greene ) 'log Lik.' -491 (df=6) > lrtest( greene ) Likelihood ratio test Model 1: lfp ~ age + I(age^2) + faminc + kids + educ Model 2: lfp ~ 1 #Df LogLik Df Chisq Pr(>Chisq) 1 6 -491 2 1 -515 -5 48 3.5e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > lrtest( greene, lfp ~ age + kids + educ ) Likelihood ratio test Model 1: lfp ~ age + I(age^2) + faminc + kids + educ Model 2: lfp ~ age + kids + educ #Df LogLik Df Chisq Pr(>Chisq) 1 6 -491 2 4 -497 -2 11.7 0.0028 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > model.frame( greene ) lfp age I(age^2) faminc kids educ 1 1 32 1024 16310 TRUE 12 2 1 30 900 21800 TRUE 12 3 1 35 1225 21040 TRUE 12 4 1 34 1156 7300 TRUE 12 5 1 31 961 27300 TRUE 14 6 1 54 2916 19495 FALSE 12 7 1 37 1369 21152 TRUE 16 8 1 54 2916 18900 FALSE 12 9 1 48 2304 20405 TRUE 12 10 1 39 1521 20425 TRUE 12 11 1 33 1089 32300 TRUE 12 12 1 42 1764 28700 TRUE 11 13 1 30 900 15500 TRUE 12 14 1 43 1849 16860 TRUE 12 15 1 43 1849 31431 TRUE 10 16 1 35 1225 19180 TRUE 11 17 1 43 1849 18600 TRUE 12 18 1 39 1521 19151 TRUE 12 19 1 45 2025 18100 FALSE 12 20 1 35 1225 20300 TRUE 12 21 1 42 1764 30419 TRUE 16 22 1 30 900 14090 FALSE 12 23 1 48 2304 22679 FALSE 13 24 1 45 2025 12160 FALSE 12 25 1 31 961 12487 TRUE 12 26 1 43 1849 29850 TRUE 17 27 1 59 3481 18100 FALSE 12 28 1 32 1024 26000 TRUE 12 29 1 31 961 26100 TRUE 17 30 1 42 1764 17730 FALSE 12 31 1 50 2500 6719 FALSE 11 32 1 59 3481 18550 FALSE 16 33 1 36 1296 24600 TRUE 13 34 1 51 2601 23100 TRUE 12 35 1 45 2025 24656 TRUE 16 36 1 42 1764 15897 TRUE 11 37 1 46 2116 20320 FALSE 12 38 1 46 2116 21384 TRUE 10 39 1 51 2601 25561 FALSE 14 40 1 30 900 36550 FALSE 17 41 1 30 900 15810 TRUE 12 42 1 57 3249 25500 FALSE 12 43 1 31 961 24000 TRUE 16 44 1 48 2304 22172 TRUE 12 45 1 30 900 17930 TRUE 12 46 1 34 1156 7000 TRUE 12 47 1 48 2304 25300 TRUE 16 48 1 45 2025 16212 FALSE 12 49 1 51 2601 22650 FALSE 12 50 1 30 900 6985 TRUE 12 51 1 46 2116 30000 TRUE 12 52 1 58 3364 18500 FALSE 12 53 1 37 1369 16658 TRUE 12 54 1 52 2704 10300 FALSE 8 55 1 52 2704 11000 FALSE 10 56 1 31 961 19900 FALSE 16 57 1 55 3025 32500 FALSE 14 58 1 34 1156 37300 FALSE 17 59 1 55 3025 30018 FALSE 14 60 1 39 1521 12807 TRUE 12 61 1 40 1600 39500 TRUE 14 62 1 43 1849 22050 TRUE 12 63 1 48 2304 15500 FALSE 8 64 1 47 2209 13810 FALSE 12 65 1 41 1681 11950 TRUE 12 66 1 36 1296 19175 FALSE 8 67 1 46 2116 17900 TRUE 17 68 1 34 1156 15850 FALSE 12 69 1 41 1681 27017 TRUE 12 70 1 51 2601 18900 TRUE 12 71 1 33 1089 21800 FALSE 12 72 1 52 2704 33552 FALSE 12 73 1 58 3364 22650 FALSE 9 74 1 34 1156 15200 TRUE 10 75 1 31 961 13120 TRUE 12 76 1 48 2304 21660 TRUE 12 77 1 32 1024 18190 TRUE 12 78 1 49 2401 9600 FALSE 17 79 1 32 1024 13755 TRUE 15 80 1 58 3364 35350 FALSE 12 81 1 50 2500 12405 FALSE 6 82 1 60 3600 12180 FALSE 14 83 1 50 2500 22962 TRUE 12 84 1 56 3136 25700 FALSE 14 85 1 51 2601 3305 FALSE 9 86 1 54 2916 32950 TRUE 17 87 1 59 3481 17000 FALSE 13 88 1 46 2116 13250 TRUE 9 89 1 46 2116 50750 TRUE 15 90 1 39 1521 15632 TRUE 12 91 1 44 1936 28316 TRUE 12 92 1 33 1089 17290 TRUE 12 93 1 33 1089 33600 TRUE 12 94 1 48 2304 29200 TRUE 12 95 1 31 961 19870 TRUE 12 96 1 45 2025 16225 TRUE 12 97 1 45 2025 28600 TRUE 12 98 1 32 1024 30800 TRUE 13 99 1 47 2209 25700 FALSE 12 100 1 34 1156 27000 TRUE 13 101 1 37 1369 12077 TRUE 12 102 1 36 1296 29612 TRUE 12 103 1 47 2209 24479 TRUE 12 104 1 48 2304 79750 TRUE 16 105 1 42 1764 20050 TRUE 12 106 1 33 1089 21500 TRUE 13 107 1 46 2116 16120 FALSE 11 108 1 47 2209 24686 TRUE 12 109 1 44 1936 24669 TRUE 12 110 1 36 1296 26400 TRUE 12 111 1 31 961 16245 TRUE 17 112 1 55 3025 23300 FALSE 14 113 1 45 2025 27200 TRUE 16 114 1 47 2209 51000 FALSE 17 115 1 46 2116 55000 TRUE 12 116 1 49 2401 15389 FALSE 11 117 1 49 2401 23150 FALSE 12 118 1 45 2025 29774 TRUE 12 119 1 38 1444 91044 TRUE 17 120 1 47 2209 28200 FALSE 10 121 1 54 2916 36150 TRUE 13 122 1 41 1681 15652 FALSE 11 123 1 43 1849 18900 TRUE 12 124 1 31 961 23600 TRUE 16 125 1 47 2209 36200 FALSE 17 126 1 35 1225 18700 TRUE 12 127 1 45 2025 24125 TRUE 16 128 1 33 1089 15800 TRUE 12 129 1 54 2916 19742 TRUE 16 130 1 35 1225 22600 TRUE 8 131 1 31 961 17500 TRUE 12 132 1 55 3025 19820 FALSE 12 133 1 34 1156 20155 TRUE 12 134 1 38 1444 37300 TRUE 13 135 1 45 2025 24860 TRUE 11 136 1 47 2209 21450 TRUE 12 137 1 39 1521 29650 TRUE 12 138 1 36 1296 23000 TRUE 14 139 1 33 1089 21120 TRUE 12 140 1 50 2500 26000 FALSE 12 141 1 58 3364 28000 FALSE 12 142 1 49 2401 45500 FALSE 17 143 1 41 1681 16000 TRUE 14 144 1 51 2601 18232 TRUE 12 145 1 53 2809 28160 FALSE 9 146 1 36 1296 5965 TRUE 12 147 1 46 2116 19000 TRUE 12 148 1 36 1296 36872 TRUE 12 149 1 53 2809 42000 TRUE 14 150 1 40 1600 26900 TRUE 16 151 1 42 1764 30800 TRUE 17 152 1 33 1089 21520 TRUE 15 153 1 43 1849 24795 TRUE 12 154 1 31 961 12900 TRUE 16 155 1 47 2209 42700 FALSE 17 156 1 54 2916 38800 FALSE 17 157 1 33 1089 2500 TRUE 12 158 1 43 1849 26921 FALSE 16 159 1 46 2116 18300 TRUE 13 160 1 35 1225 17200 TRUE 12 161 1 37 1369 14209 TRUE 11 162 1 37 1369 32300 TRUE 16 163 1 34 1156 21400 TRUE 14 164 1 43 1849 14700 TRUE 16 165 1 46 2116 25516 FALSE 12 166 1 35 1225 13300 TRUE 9 167 1 46 2116 31000 FALSE 17 168 1 46 2116 48800 FALSE 14 169 1 43 1849 15519 TRUE 12 170 1 30 900 19500 FALSE 12 171 1 41 1681 14545 TRUE 11 172 1 54 2916 22897 TRUE 12 173 1 31 961 14300 TRUE 12 174 1 44 1936 14884 FALSE 10 175 1 32 1024 27400 TRUE 12 176 1 47 2209 16400 FALSE 5 177 1 46 2116 25704 TRUE 17 178 1 37 1369 12800 FALSE 11 179 1 51 2601 40000 TRUE 12 180 1 49 2401 47000 TRUE 12 181 1 36 1296 25872 TRUE 14 182 1 39 1521 26868 TRUE 11 183 1 48 2304 18000 TRUE 12 184 1 38 1444 30200 TRUE 14 185 1 40 1600 26220 TRUE 12 186 1 39 1521 40500 TRUE 10 187 1 37 1369 33570 FALSE 16 188 1 49 2401 16917 TRUE 13 189 1 33 1089 18000 TRUE 12 190 1 30 900 8337 FALSE 12 191 1 54 2916 17100 FALSE 12 192 1 39 1521 17800 TRUE 11 193 1 43 1849 13740 TRUE 12 194 1 31 961 27000 TRUE 9 195 1 33 1089 32600 TRUE 13 196 1 40 1600 28030 TRUE 12 197 1 36 1296 23100 TRUE 12 198 1 51 2601 24580 FALSE 12 199 1 44 1936 29000 TRUE 13 200 1 42 1764 42430 TRUE 16 201 1 40 1600 41800 TRUE 12 202 1 34 1156 39100 TRUE 16 203 1 30 900 31800 FALSE 17 204 1 54 2916 22200 FALSE 12 205 1 51 2601 19500 FALSE 12 206 1 44 1936 14027 TRUE 9 207 1 43 1849 21195 TRUE 12 208 1 34 1156 19013 TRUE 12 209 1 45 2025 20900 FALSE 13 210 1 39 1521 26820 FALSE 12 211 1 50 2500 12800 FALSE 12 212 1 52 2704 7850 FALSE 12 213 1 41 1681 18600 TRUE 12 214 1 59 3481 5380 FALSE 10 215 1 52 2704 6114 FALSE 12 216 1 46 2116 8234 FALSE 16 217 1 41 1681 20646 TRUE 12 218 1 33 1089 16640 TRUE 11 219 1 45 2025 13900 FALSE 12 220 1 36 1296 11500 TRUE 10 221 1 48 2304 34000 TRUE 12 222 1 47 2209 28700 TRUE 12 223 1 45 2025 9563 FALSE 12 224 1 37 1369 20960 TRUE 12 225 1 46 2116 38590 TRUE 16 226 1 43 1849 27900 TRUE 17 227 1 42 1764 25633 TRUE 12 228 1 34 1156 35200 TRUE 17 229 1 52 2704 29500 FALSE 12 230 1 37 1369 10000 TRUE 12 231 1 37 1369 19800 TRUE 12 232 1 52 2704 12900 FALSE 8 233 1 30 900 26080 TRUE 12 234 1 31 961 13066 TRUE 13 235 1 38 1444 12500 TRUE 12 236 1 43 1849 35600 TRUE 12 237 1 49 2401 19424 TRUE 8 238 1 55 3025 26250 FALSE 12 239 1 38 1444 36525 TRUE 17 240 1 52 2704 34700 FALSE 17 241 1 48 2304 8311 FALSE 12 242 1 32 1024 28626 TRUE 13 243 1 32 1024 24400 TRUE 12 244 1 38 1444 14025 TRUE 12 245 1 46 2116 21367 TRUE 12 246 1 40 1600 14136 TRUE 12 247 1 31 961 19900 TRUE 9 248 1 43 1849 37000 TRUE 10 249 1 51 2601 18500 FALSE 12 250 1 30 900 34550 TRUE 16 251 1 52 2704 49620 FALSE 13 252 1 30 900 10400 TRUE 8 253 1 51 2601 33000 FALSE 16 254 1 31 961 16200 TRUE 13 255 1 34 1156 22660 TRUE 12 256 1 49 2401 16000 FALSE 11 257 1 35 1225 25780 TRUE 13 258 1 53 2809 20675 TRUE 12 259 1 32 1024 40213 TRUE 12 260 1 38 1444 15500 TRUE 10 261 1 54 2916 35207 FALSE 12 262 1 47 2209 35702 TRUE 17 263 1 45 2025 17800 TRUE 15 264 1 47 2209 50900 TRUE 16 265 1 59 3481 17280 FALSE 10 266 1 32 1024 15150 TRUE 11 267 1 45 2025 36200 TRUE 12 268 1 40 1600 17465 TRUE 12 269 1 47 2209 45205 TRUE 14 270 1 36 1296 14500 TRUE 16 271 1 56 3136 32011 FALSE 14 272 1 41 1681 35200 TRUE 8 273 1 48 2304 13579 TRUE 7 274 1 36 1296 10455 TRUE 12 275 1 41 1681 32600 FALSE 12 276 1 41 1681 19150 FALSE 14 277 1 36 1296 24400 TRUE 12 278 1 37 1369 21700 TRUE 12 279 1 38 1444 26201 FALSE 12 280 1 43 1849 11920 TRUE 14 281 1 54 2916 16300 FALSE 16 282 1 38 1444 16500 TRUE 12 283 1 30 900 30000 TRUE 12 284 1 49 2401 62500 FALSE 12 285 1 45 2025 58500 TRUE 13 286 1 51 2601 40900 FALSE 13 287 1 34 1156 16308 FALSE 10 288 1 34 1156 9300 TRUE 12 289 1 41 1681 35700 TRUE 12 290 1 49 2401 35700 TRUE 12 291 1 32 1024 24500 FALSE 12 292 1 32 1024 13000 FALSE 14 293 1 32 1024 52600 TRUE 17 294 1 47 2209 25360 FALSE 10 295 1 39 1521 9400 TRUE 9 296 1 49 2401 26800 FALSE 12 297 1 37 1369 33040 TRUE 12 298 1 59 3481 26350 FALSE 16 299 1 50 2500 39000 FALSE 12 300 1 32 1024 35100 TRUE 17 301 1 46 2116 22502 FALSE 12 302 1 43 1849 21950 TRUE 17 303 1 37 1369 13000 TRUE 11 304 1 32 1024 18180 TRUE 16 305 1 39 1521 20957 TRUE 11 306 1 34 1156 13700 TRUE 13 307 1 39 1521 20000 TRUE 11 308 1 45 2025 12260 TRUE 8 309 1 50 2500 24850 FALSE 11 310 1 40 1600 29150 TRUE 12 311 1 30 900 23591 TRUE 10 312 1 57 3249 24717 FALSE 17 313 1 39 1521 30455 TRUE 12 314 1 53 2809 19600 FALSE 12 315 1 48 2304 31875 TRUE 17 316 1 46 2116 24055 TRUE 14 317 1 47 2209 18795 FALSE 12 318 1 43 1849 12198 TRUE 12 319 1 47 2209 52645 FALSE 12 320 1 47 2209 16600 TRUE 12 321 1 47 2209 32590 FALSE 12 322 1 46 2116 10020 FALSE 12 323 1 34 1156 12048 TRUE 9 324 1 48 2304 39750 FALSE 10 325 1 30 900 15700 TRUE 12 326 1 51 2601 24250 TRUE 12 327 1 52 2704 43210 TRUE 12 328 1 37 1369 37100 TRUE 12 329 1 32 1024 23820 TRUE 12 330 1 36 1296 31600 TRUE 17 331 1 35 1225 24000 TRUE 12 332 1 45 2025 30750 FALSE 17 333 1 56 3136 11050 FALSE 12 334 1 40 1600 12239 TRUE 10 335 1 45 2025 12870 TRUE 12 336 1 32 1024 17200 TRUE 12 337 1 45 2025 23980 FALSE 12 338 1 40 1600 18600 TRUE 12 339 1 38 1444 23920 TRUE 12 340 1 49 2401 16084 TRUE 12 341 1 47 2209 31100 TRUE 16 342 1 52 2704 20460 FALSE 13 343 1 34 1156 36000 TRUE 13 344 1 44 1936 17302 TRUE 12 345 1 36 1296 16450 TRUE 16 346 1 50 2500 41170 FALSE 17 347 1 45 2025 20130 FALSE 12 348 1 44 1936 9200 TRUE 14 349 1 57 3249 24751 TRUE 12 350 1 35 1225 57300 FALSE 17 351 1 46 2116 37200 FALSE 12 352 1 30 900 14000 TRUE 14 353 1 42 1764 20610 TRUE 12 354 1 34 1156 14800 TRUE 12 355 1 45 2025 40005 TRUE 17 356 1 35 1225 23750 TRUE 16 357 1 40 1600 35300 FALSE 16 358 1 32 1024 17350 TRUE 12 359 1 54 2916 21650 FALSE 9 360 1 38 1444 6740 TRUE 12 361 1 43 1849 32275 TRUE 12 362 1 54 2916 33220 FALSE 16 363 1 39 1521 26500 TRUE 14 364 1 37 1369 38700 TRUE 12 365 1 46 2116 15400 TRUE 12 366 1 56 3136 19007 FALSE 11 367 1 41 1681 16771 TRUE 12 368 1 45 2025 31100 TRUE 16 369 1 44 1936 66300 TRUE 17 370 1 50 2500 43550 TRUE 17 371 1 37 1369 37250 TRUE 14 372 1 44 1936 24900 TRUE 12 373 1 32 1024 24200 TRUE 14 374 1 34 1156 16200 TRUE 12 375 1 32 1024 11431 TRUE 10 376 1 37 1369 13200 TRUE 12 377 1 44 1936 15420 TRUE 13 378 1 34 1156 18400 TRUE 16 379 1 33 1089 43500 TRUE 12 380 1 43 1849 21972 TRUE 7 381 1 35 1225 7774 TRUE 16 382 1 43 1849 24470 TRUE 14 383 1 34 1156 13600 FALSE 12 384 1 36 1296 22500 TRUE 10 385 1 41 1681 13600 TRUE 12 386 1 41 1681 90800 FALSE 16 387 1 35 1225 10776 TRUE 10 388 1 32 1024 19007 TRUE 12 389 1 30 900 23900 FALSE 14 390 1 43 1849 26060 FALSE 12 391 1 54 2916 13300 FALSE 6 392 1 35 1225 15620 TRUE 15 393 1 50 2500 16500 FALSE 12 394 1 34 1156 20880 TRUE 17 395 1 52 2704 30600 FALSE 14 396 1 35 1225 39000 TRUE 13 397 1 55 3025 15428 FALSE 6 398 1 35 1225 23300 FALSE 16 399 1 49 2401 42100 TRUE 14 400 1 38 1444 36430 TRUE 15 401 1 42 1764 26000 TRUE 14 402 1 48 2304 62060 TRUE 8 403 1 51 2601 28300 FALSE 14 404 1 43 1849 24149 TRUE 12 405 1 43 1849 28141 TRUE 12 406 1 38 1444 23057 TRUE 12 407 1 44 1936 28900 TRUE 12 408 1 36 1296 24000 TRUE 12 409 1 38 1444 13900 FALSE 12 410 1 47 2209 31810 FALSE 8 411 1 34 1156 19840 TRUE 12 412 1 40 1600 25490 TRUE 17 413 1 31 961 20800 TRUE 12 414 1 46 2116 2400 FALSE 12 415 1 36 1296 32650 TRUE 14 416 1 39 1521 16370 TRUE 13 417 1 36 1296 35500 TRUE 17 418 1 37 1369 15100 TRUE 8 419 1 39 1521 14100 TRUE 12 420 1 36 1296 19600 TRUE 11 421 1 49 2401 19434 TRUE 12 422 1 45 2025 23882 TRUE 12 423 1 32 1024 17300 TRUE 17 424 1 36 1296 19772 TRUE 10 425 1 40 1600 35641 TRUE 12 426 1 43 1849 34220 TRUE 13 427 1 33 1089 30000 TRUE 12 428 1 30 900 18000 TRUE 12 429 0 49 2401 21025 TRUE 12 430 0 30 900 23600 TRUE 16 431 0 30 900 22800 TRUE 12 432 0 41 1681 35910 TRUE 12 433 0 45 2025 21700 TRUE 12 434 0 43 1849 21823 TRUE 12 435 0 42 1764 31000 TRUE 13 436 0 60 3600 15300 FALSE 12 437 0 57 3249 12925 FALSE 12 438 0 38 1444 15830 TRUE 10 439 0 56 3136 30200 FALSE 12 440 0 32 1024 16600 TRUE 12 441 0 49 2401 11000 TRUE 7 442 0 55 3025 15000 FALSE 12 443 0 36 1296 20528 TRUE 9 444 0 44 1936 13126 TRUE 12 445 0 44 1936 15550 TRUE 10 446 0 35 1225 18010 TRUE 14 447 0 44 1936 18874 TRUE 14 448 0 45 2025 24800 TRUE 12 449 0 34 1156 17500 TRUE 12 450 0 30 900 16150 TRUE 17 451 0 39 1521 15189 TRUE 8 452 0 36 1296 6000 TRUE 12 453 0 38 1444 37250 TRUE 17 454 0 53 2809 27760 FALSE 12 455 0 36 1296 9090 TRUE 12 456 0 32 1024 14500 TRUE 12 457 0 51 2601 19700 TRUE 9 458 0 38 1444 16788 FALSE 11 459 0 33 1089 18520 TRUE 12 460 0 54 2916 20950 FALSE 12 461 0 38 1444 7574 TRUE 9 462 0 30 900 10027 TRUE 11 463 0 34 1156 5000 TRUE 12 464 0 34 1156 7040 TRUE 9 465 0 50 2500 40800 TRUE 12 466 0 30 900 16050 TRUE 17 467 0 38 1444 33100 TRUE 12 468 0 54 2916 33856 FALSE 14 469 0 30 900 20500 TRUE 12 470 0 55 3025 28600 FALSE 12 471 0 51 2601 18750 TRUE 10 472 0 44 1936 20300 TRUE 12 473 0 53 2809 13420 FALSE 12 474 0 42 1764 18400 TRUE 10 475 0 38 1444 16682 TRUE 12 476 0 38 1444 32685 TRUE 13 477 0 41 1681 7050 TRUE 12 478 0 35 1225 10867 TRUE 8 479 0 33 1089 18220 TRUE 12 480 0 48 2304 26613 FALSE 13 481 0 47 2209 25000 FALSE 12 482 0 34 1156 15700 TRUE 12 483 0 33 1089 40250 TRUE 13 484 0 31 961 73600 TRUE 13 485 0 58 3364 10592 FALSE 8 486 0 49 2401 8000 FALSE 12 487 0 55 3025 13400 TRUE 8 488 0 44 1936 23700 FALSE 14 489 0 44 1936 18900 FALSE 9 490 0 36 1296 48300 TRUE 16 491 0 38 1444 24470 TRUE 12 492 0 37 1369 28630 TRUE 16 493 0 47 2209 25320 FALSE 12 494 0 47 2209 13530 TRUE 12 495 0 32 1024 14800 TRUE 12 496 0 43 1849 17400 TRUE 12 497 0 42 1764 15980 TRUE 11 498 0 56 3136 16576 FALSE 12 499 0 38 1444 21850 TRUE 13 500 0 52 2704 14600 TRUE 12 501 0 50 2500 21600 FALSE 12 502 0 33 1089 24000 FALSE 16 503 0 44 1936 20883 TRUE 16 504 0 41 1681 19500 TRUE 12 505 0 45 2025 42800 TRUE 12 506 0 53 2809 41500 FALSE 14 507 0 53 2809 18965 FALSE 14 508 0 42 1764 16100 TRUE 12 509 0 32 1024 14700 TRUE 13 510 0 56 3136 18800 FALSE 12 511 0 37 1369 14750 TRUE 11 512 0 40 1600 21000 TRUE 12 513 0 54 2916 35400 TRUE 15 514 0 53 2809 10700 FALSE 7 515 0 48 2304 24500 TRUE 12 516 0 36 1296 17045 TRUE 12 517 0 57 3249 18800 FALSE 12 518 0 51 2601 14000 FALSE 12 519 0 33 1089 18214 TRUE 13 520 0 52 2704 20177 FALSE 12 521 0 56 3136 8300 FALSE 10 522 0 36 1296 14200 TRUE 12 523 0 36 1296 21768 TRUE 14 524 0 46 2116 29553 TRUE 12 525 0 31 961 4350 TRUE 10 526 0 52 2704 24000 FALSE 11 527 0 46 2116 18300 TRUE 12 528 0 35 1225 17200 TRUE 12 529 0 59 3481 16476 FALSE 12 530 0 36 1296 13400 TRUE 8 531 0 51 2601 44988 TRUE 7 532 0 31 961 18200 TRUE 16 533 0 31 961 28000 TRUE 14 534 0 32 1024 11550 TRUE 12 535 0 35 1225 28450 TRUE 16 536 0 40 1600 15096 TRUE 12 537 0 33 1089 8009 TRUE 10 538 0 54 2916 10040 FALSE 7 539 0 36 1296 16700 TRUE 12 540 0 50 2500 8400 TRUE 10 541 0 54 2916 13000 FALSE 8 542 0 48 2304 17970 TRUE 11 543 0 41 1681 18450 TRUE 15 544 0 50 2500 31000 TRUE 12 545 0 46 2116 24135 TRUE 12 546 0 42 1764 31700 TRUE 13 547 0 31 961 10190 TRUE 9 548 0 53 2809 21574 FALSE 12 549 0 51 2601 26680 TRUE 12 550 0 47 2209 17700 TRUE 12 551 0 50 2500 29400 TRUE 12 552 0 37 1369 22159 TRUE 6 553 0 30 900 35000 TRUE 12 554 0 49 2401 8630 FALSE 12 555 0 52 2704 17080 TRUE 12 556 0 47 2209 32500 TRUE 12 557 0 49 2401 16000 FALSE 12 558 0 44 1936 18850 TRUE 12 559 0 53 2809 17500 FALSE 8 560 0 30 900 19392 TRUE 12 561 0 54 2916 14450 TRUE 12 562 0 47 2209 21800 TRUE 7 563 0 56 3136 7700 FALSE 15 564 0 49 2401 31800 TRUE 12 565 0 48 2304 17258 FALSE 6 566 0 49 2401 13399 TRUE 12 567 0 56 3136 16073 TRUE 12 568 0 46 2116 23260 FALSE 12 569 0 45 2025 37300 TRUE 12 570 0 32 1024 11000 TRUE 12 571 0 43 1849 13075 TRUE 12 572 0 34 1156 13700 TRUE 12 573 0 30 900 25100 TRUE 12 574 0 38 1444 18600 TRUE 17 575 0 33 1089 29000 TRUE 16 576 0 52 2704 19237 FALSE 12 577 0 43 1849 19855 TRUE 11 578 0 33 1089 9450 TRUE 12 579 0 45 2025 30000 FALSE 10 580 0 36 1296 15000 TRUE 10 581 0 34 1156 24701 TRUE 12 582 0 37 1369 15900 TRUE 14 583 0 46 2116 16240 TRUE 10 584 0 47 2209 21100 FALSE 12 585 0 31 961 23000 TRUE 16 586 0 57 3249 6340 FALSE 5 587 0 30 900 42250 TRUE 12 588 0 30 900 14694 FALSE 12 589 0 44 1936 21417 TRUE 12 590 0 53 2809 20200 FALSE 13 591 0 51 2601 12090 FALSE 8 592 0 39 1521 24760 TRUE 12 593 0 52 2704 23000 FALSE 8 594 0 46 2116 19365 TRUE 8 595 0 47 2209 5550 TRUE 12 596 0 52 2704 68035 TRUE 8 597 0 45 2025 29300 TRUE 12 598 0 60 3600 18500 FALSE 11 599 0 41 1681 22582 TRUE 13 600 0 39 1521 21500 TRUE 8 601 0 49 2401 28070 TRUE 12 602 0 32 1024 50300 TRUE 15 603 0 33 1089 23500 TRUE 12 604 0 36 1296 15500 TRUE 10 605 0 37 1369 13440 TRUE 13 606 0 30 900 8100 TRUE 12 607 0 44 1936 9800 TRUE 11 608 0 48 2304 20300 TRUE 12 609 0 40 1600 15000 TRUE 11 610 0 47 2209 56100 FALSE 13 611 0 36 1296 22846 TRUE 12 612 0 40 1600 22225 TRUE 11 613 0 46 2116 17635 TRUE 12 614 0 52 2704 18500 FALSE 12 615 0 44 1936 13390 TRUE 12 616 0 45 2025 15150 TRUE 10 617 0 30 900 16200 TRUE 7 618 0 40 1600 33920 TRUE 12 619 0 43 1849 14000 TRUE 12 620 0 49 2401 16736 TRUE 12 621 0 46 2116 30650 TRUE 12 622 0 52 2704 12400 FALSE 11 623 0 31 961 19022 TRUE 12 624 0 42 1764 11203 TRUE 10 625 0 33 1089 19876 TRUE 11 626 0 57 3249 57000 FALSE 16 627 0 49 2401 18290 FALSE 10 628 0 45 2025 20220 TRUE 14 629 0 56 3136 22150 FALSE 11 630 0 41 1681 30623 TRUE 12 631 0 56 3136 9380 FALSE 5 632 0 48 2304 22000 TRUE 10 633 0 52 2704 23675 TRUE 16 634 0 51 2601 33671 FALSE 12 635 0 35 1225 12367 TRUE 11 636 0 45 2025 21950 FALSE 12 637 0 54 2916 32000 FALSE 12 638 0 54 2916 22610 TRUE 12 639 0 31 961 12092 TRUE 12 640 0 53 2809 3777 TRUE 6 641 0 35 1225 36000 TRUE 14 642 0 36 1296 26900 TRUE 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2916 35020 0 16 645 1 37 1369 37600 1 12 646 1 44 1936 1500 0 12 647 1 34 1156 96000 1 17 648 1 49 2401 18150 0 12 649 1 49 2401 15500 0 12 650 1 60 3600 14000 0 9 651 1 51 2601 14756 0 12 652 1 30 900 22000 1 12 653 1 47 2209 24466 1 12 654 1 36 1296 24400 1 12 655 1 35 1225 24000 1 12 656 1 58 3364 15500 0 12 657 1 41 1681 30800 1 14 658 1 51 2601 10660 1 10 659 1 47 2209 13350 0 12 660 1 45 2025 10090 1 9 661 1 60 3600 55600 0 14 662 1 30 900 25700 1 16 663 1 55 3025 29000 0 11 664 1 32 1024 7286 1 12 665 1 36 1296 37752 1 12 666 1 55 3025 13072 0 12 667 1 47 2209 7044 0 12 668 1 47 2209 18200 1 12 669 1 37 1369 27000 1 11 670 1 50 2500 30300 1 12 671 1 30 900 12000 1 12 672 1 48 2304 31500 1 17 673 1 43 1849 27092 1 10 674 1 48 2304 20968 1 11 675 1 41 1681 27000 1 14 676 1 50 2500 11225 0 12 677 1 58 3364 37700 0 8 678 1 38 1444 28200 1 13 679 1 37 1369 34000 1 12 680 1 50 2500 63200 0 16 681 1 42 1764 7500 1 8 682 1 37 1369 17410 1 9 683 1 41 1681 51000 1 16 684 1 31 961 12916 1 12 685 1 51 2601 21900 0 12 686 1 36 1296 17640 1 12 687 1 54 2916 20000 0 15 688 1 49 2401 15000 0 12 689 1 48 2304 14060 1 9 690 1 42 1764 15825 1 9 691 1 41 1681 16510 1 12 692 1 55 3025 13000 0 16 693 1 42 1764 10000 0 9 694 1 32 1024 22000 1 15 695 1 43 1849 29800 1 12 696 1 33 1089 15000 1 12 697 1 48 2304 22300 1 15 698 1 43 1849 14550 1 12 699 1 47 2209 19730 1 17 700 1 54 2916 35000 0 12 701 1 51 2601 21014 1 12 702 1 51 2601 10876 1 10 703 1 43 1849 27850 1 13 704 1 53 2809 9560 0 12 705 1 34 1156 30300 1 11 706 1 31 961 7720 1 8 707 1 56 3136 10550 0 12 708 1 42 1764 24106 1 16 709 1 32 1024 22995 1 12 710 1 35 1225 6000 1 12 711 1 30 900 24350 1 12 712 1 51 2601 7608 0 10 713 1 47 2209 28200 1 12 714 1 54 2916 16150 1 12 715 1 31 961 51200 1 15 716 1 47 2209 12646 0 10 717 1 47 2209 19000 1 14 718 1 40 1600 19000 1 12 719 1 48 2304 14400 0 8 720 1 34 1156 7232 1 8 721 1 38 1444 21943 1 12 722 1 32 1024 47500 1 12 723 1 48 2304 28900 1 16 724 1 41 1681 12400 1 12 725 1 49 2401 6531 1 5 726 1 59 3481 22422 0 8 727 1 58 3364 22200 0 13 728 1 41 1681 77000 1 12 729 1 45 2025 88000 1 12 730 1 30 900 26040 1 14 731 1 41 1681 63500 1 12 732 1 30 900 12100 1 12 733 1 53 2809 17505 1 12 734 1 31 961 18000 0 12 735 1 43 1849 28069 1 14 736 1 31 961 14000 1 12 737 1 51 2601 8117 0 12 738 1 43 1849 11895 0 9 739 1 31 961 45250 1 14 740 1 48 2304 31106 0 11 741 1 31 961 4000 1 12 742 1 44 1936 40500 1 12 743 1 48 2304 21620 1 11 744 1 53 2809 23426 1 12 745 1 42 1764 26000 1 10 746 1 39 1521 7840 1 12 747 1 32 1024 6800 1 10 748 1 36 1296 5330 1 12 749 1 40 1600 28200 1 13 750 1 31 961 10000 1 12 751 1 43 1849 9952 0 12 752 1 60 3600 24984 0 12 753 1 39 1521 28363 1 9 attr(,"assign") [1] 0 1 2 3 4 5 attr(,"contrasts") attr(,"contrasts")$kids [1] "contr.treatment" > fitted( greene ) 1 2 3 4 5 6 7 8 9 10 11 12 13 0.538 0.520 0.574 0.542 0.622 0.519 0.726 0.518 0.490 0.582 0.578 0.545 0.509 14 15 16 17 18 19 20 21 22 23 24 25 26 0.554 0.503 0.532 0.557 0.580 0.704 0.572 0.730 0.679 0.703 0.694 0.518 0.754 27 28 29 30 31 32 33 34 35 36 37 38 39 0.344 0.556 0.725 0.729 0.563 0.497 0.623 0.430 0.694 0.522 0.695 0.448 0.684 40 41 42 43 44 45 46 47 48 49 50 51 52 0.855 0.509 0.430 0.688 0.493 0.513 0.541 0.652 0.701 0.607 0.493 0.542 0.381 53 54 55 56 57 58 59 60 61 62 63 64 65 0.574 0.404 0.483 0.822 0.589 0.881 0.585 0.568 0.686 0.564 0.504 0.670 0.560 66 67 68 69 70 71 72 73 74 75 76 77 78 0.597 0.705 0.723 0.587 0.422 0.725 0.601 0.282 0.478 0.519 0.493 0.542 0.793 79 80 81 82 83 84 85 86 87 88 89 90 91 0.647 0.411 0.380 0.368 0.453 0.544 0.455 0.560 0.379 0.395 0.689 0.573 0.565 92 93 94 95 96 97 98 99 100 101 102 103 104 0.551 0.580 0.506 0.532 0.531 0.553 0.603 0.689 0.615 0.566 0.594 0.516 0.739 105 106 107 108 109 110 111 112 113 114 115 116 117 0.568 0.597 0.652 0.516 0.558 0.588 0.710 0.573 0.698 0.864 0.587 0.600 0.651 118 119 120 121 122 123 124 125 126 127 128 129 130 0.555 0.847 0.621 0.410 0.698 0.558 0.688 0.849 0.570 0.694 0.548 0.497 0.421 131 132 133 134 135 136 137 138 139 140 141 142 143 0.527 0.488 0.565 0.649 0.507 0.510 0.598 0.657 0.558 0.635 0.398 0.837 0.643 144 145 146 147 148 149 150 151 152 153 154 155 156 0.421 0.447 0.552 0.522 0.607 0.489 0.733 0.762 0.671 0.568 0.670 0.856 0.735 157 158 159 160 161 162 163 164 165 166 167 168 169 0.524 0.847 0.559 0.567 0.531 0.743 0.643 0.698 0.703 0.443 0.853 0.798 0.552 170 171 172 173 174 175 176 177 178 179 180 181 182 0.688 0.526 0.350 0.521 0.638 0.558 0.407 0.718 0.699 0.461 0.519 0.662 0.555 183 184 185 186 187 188 189 190 191 192 193 194 195 0.486 0.673 0.590 0.541 0.866 0.503 0.552 0.669 0.515 0.538 0.548 0.428 0.616 196 197 198 199 200 201 202 203 204 205 206 207 208 0.593 0.583 0.610 0.604 0.748 0.617 0.740 0.850 0.524 0.601 0.422 0.562 0.563 209 210 211 212 213 214 215 216 217 218 219 220 221 0.741 0.753 0.612 0.555 0.572 0.256 0.552 0.801 0.575 0.511 0.697 0.484 0.515 222 223 224 225 226 227 228 229 230 231 232 233 234 0.523 0.690 0.582 0.704 0.751 0.578 0.765 0.594 0.562 0.580 0.409 0.528 0.558 235 236 237 238 239 240 241 242 243 244 245 246 247 0.568 0.588 0.319 0.500 0.780 0.774 0.644 0.599 0.553 0.571 0.526 0.568 0.415 248 249 250 251 252 253 254 255 256 257 258 259 260 0.513 0.599 0.692 0.666 0.347 0.761 0.564 0.570 0.601 0.620 0.374 0.581 0.496 261 262 263 264 265 266 267 268 269 270 271 272 273 0.548 0.720 0.648 0.710 0.274 0.497 0.567 0.574 0.630 0.713 0.555 0.446 0.292 274 275 276 277 278 279 280 281 282 283 284 285 286 0.560 0.756 0.796 0.585 0.583 0.753 0.622 0.665 0.575 0.535 0.715 0.644 0.674 287 288 289 290 291 292 293 294 295 296 297 298 299 0.655 0.545 0.602 0.498 0.720 0.766 0.774 0.616 0.445 0.657 0.603 0.511 0.657 300 301 302 303 304 305 306 307 308 309 310 311 312 0.749 0.698 0.742 0.529 0.690 0.544 0.592 0.542 0.369 0.596 0.595 0.445 0.622 313 314 315 316 317 318 319 320 321 322 323 324 325 0.600 0.549 0.698 0.608 0.678 0.546 0.731 0.501 0.700 0.678 0.433 0.624 0.509 326 327 328 329 330 331 332 333 334 335 336 337 338 0.432 0.441 0.611 0.552 0.770 0.579 0.861 0.439 0.487 0.525 0.540 0.713 0.576 339 340 341 342 343 344 345 346 347 348 349 350 351 0.589 0.462 0.678 0.616 0.631 0.545 0.716 0.818 0.707 0.607 0.265 0.902 0.721 352 353 354 355 356 357 358 359 360 361 362 363 364 0.584 0.569 0.555 0.751 0.723 0.867 0.540 0.406 0.558 0.582 0.693 0.667 0.613 365 366 367 368 369 370 371 372 373 374 375 376 377 0.515 0.415 0.568 0.705 0.796 0.680 0.684 0.559 0.629 0.558 0.451 0.568 0.580 378 379 380 381 382 383 384 385 386 387 388 389 390 0.708 0.598 0.370 0.698 0.643 0.720 0.504 0.563 0.912 0.477 0.543 0.760 0.735 391 392 393 394 395 396 397 398 399 400 401 402 403 0.285 0.676 0.619 0.745 0.670 0.643 0.262 0.850 0.588 0.718 0.654 0.410 0.689 404 405 406 407 408 409 410 411 412 413 414 415 416 0.567 0.574 0.587 0.566 0.584 0.735 0.551 0.564 0.763 0.533 0.665 0.673 0.613 417 418 419 420 421 422 423 424 425 426 427 428 429 0.775 0.416 0.570 0.538 0.469 0.545 0.723 0.499 0.606 0.623 0.574 0.513 0.471 430 431 432 433 434 435 436 437 438 439 440 441 442 0.674 0.522 0.603 0.541 0.563 0.625 0.302 0.408 0.496 0.474 0.539 0.271 0.479 443 444 445 446 447 448 449 450 451 452 453 454 455 0.461 0.537 0.463 0.644 0.624 0.546 0.560 0.697 0.417 0.552 0.781 0.564 0.557 456 457 458 459 460 461 462 463 464 465 466 467 468 0.535 0.313 0.706 0.553 0.522 0.442 0.460 0.538 0.424 0.486 0.697 0.605 0.622 469 470 471 472 473 474 475 476 477 478 479 480 481 0.518 0.504 0.347 0.550 0.538 0.487 0.576 0.641 0.551 0.400 0.553 0.709 0.688 482 483 484 485 486 487 488 489 490 491 492 493 494 0.557 0.630 0.664 0.233 0.625 0.184 0.784 0.608 0.763 0.590 0.737 0.689 0.496 495 496 497 498 499 500 501 502 503 504 505 506 507 0.535 0.555 0.522 0.449 0.623 0.390 0.628 0.841 0.699 0.573 0.579 0.663 0.624 508 509 510 511 512 513 514 515 516 517 518 519 520 0.561 0.574 0.453 0.532 0.580 0.487 0.341 0.498 0.572 0.418 0.591 0.591 0.577 521 522 523 524 525 526 527 528 529 530 531 532 533 0.359 0.567 0.655 0.541 0.425 0.545 0.520 0.567 0.341 0.410 0.285 0.679 0.623 534 535 536 537 538 539 540 541 542 543 544 545 546 0.529 0.730 0.570 0.456 0.313 0.571 0.352 0.354 0.447 0.683 0.468 0.531 0.627 547 548 549 550 551 552 553 554 555 556 557 558 559 0.398 0.552 0.436 0.503 0.465 0.353 0.544 0.626 0.394 0.530 0.638 0.548 0.390 560 561 562 563 564 565 566 567 568 569 570 571 572 0.516 0.336 0.321 0.550 0.491 0.429 0.458 0.281 0.699 0.569 0.528 0.547 0.553 573 574 575 576 577 578 579 580 581 582 583 584 585 0.526 0.755 0.717 0.576 0.521 0.537 0.653 0.490 0.573 0.648 0.439 0.682 0.687 586 587 588 589 590 591 592 593 594 595 596 597 598 0.171 0.557 0.680 0.552 0.588 0.432 0.590 0.427 0.368 0.481 0.334 0.554 0.274 599 600 601 602 603 604 605 606 607 608 609 610 611 0.617 0.428 0.484 0.707 0.562 0.491 0.607 0.495 0.492 0.490 0.531 0.768 0.582 612 613 614 615 616 617 618 619 620 621 622 623 624 0.544 0.519 0.574 0.538 0.451 0.321 0.603 0.549 0.464 0.543 0.524 0.530 0.474 625 626 627 628 629 630 631 632 633 634 635 636 637 0.517 0.641 0.567 0.615 0.420 0.593 0.198 0.415 0.561 0.626 0.519 0.710 0.542 638 639 640 641 642 643 644 645 646 647 648 649 650 0.350 0.517 0.162 0.674 0.589 0.368 0.696 0.611 0.688 0.842 0.642 0.638 0.207 651 652 653 654 655 656 657 658 659 660 661 662 663 0.593 0.521 0.516 0.585 0.579 0.376 0.668 0.334 0.669 0.403 0.445 0.678 0.466 664 665 666 667 668 669 670 671 672 673 674 675 676 0.522 0.609 0.476 0.659 0.504 0.554 0.467 0.502 0.698 0.495 0.452 0.661 0.609 677 678 679 680 681 682 683 684 685 686 687 688 689 0.272 0.634 0.605 0.818 0.390 0.459 0.765 0.519 0.605 0.573 0.635 0.637 0.364 690 691 692 693 694 695 696 697 698 699 700 701 702 0.443 0.568 0.630 0.610 0.661 0.577 0.547 0.610 0.550 0.694 0.547 0.426 0.334 703 704 705 706 707 708 709 710 711 712 713 714 715 0.612 0.531 0.545 0.356 0.438 0.720 0.550 0.547 0.525 0.502 0.522 0.339 0.698 716 717 718 719 720 721 722 723 724 725 726 727 728 0.594 0.583 0.577 0.502 0.387 0.585 0.594 0.658 0.561 0.205 0.219 0.426 0.673 729 730 731 732 733 734 735 736 737 738 739 740 741 0.658 0.605 0.650 0.503 0.368 0.698 0.649 0.521 0.581 0.606 0.652 0.646 0.503 742 743 744 745 746 747 748 749 750 751 752 753 0.587 0.453 0.379 0.501 0.559 0.443 0.551 0.631 0.514 0.710 0.318 0.479 > all.equal( fitted( greene ), predict( greene, type = "response" ) ) [1] TRUE > all.equal( fitted( greene )[ 11:222 ], + predict( greene, newdata = Mroz87[ 11:222, ], type = "response" ) ) [1] TRUE > linearPredictors( greene ) 1 2 3 4 5 6 7 8 9.56e-02 5.08e-02 1.86e-01 1.05e-01 3.10e-01 4.81e-02 6.01e-01 4.54e-02 9 10 11 12 13 14 15 16 -2.44e-02 2.07e-01 1.97e-01 1.13e-01 2.19e-02 1.36e-01 6.50e-03 7.92e-02 17 18 19 20 21 22 23 24 1.44e-01 2.01e-01 5.35e-01 1.82e-01 6.12e-01 4.64e-01 5.33e-01 5.07e-01 25 26 27 28 29 30 31 32 4.56e-02 6.87e-01 -4.02e-01 1.40e-01 5.99e-01 6.10e-01 1.59e-01 -7.16e-03 33 34 35 36 37 38 39 40 3.14e-01 -1.76e-01 5.08e-01 5.43e-02 5.09e-01 -1.31e-01 4.80e-01 1.06e+00 41 42 43 44 45 46 47 48 2.34e-02 -1.76e-01 4.91e-01 -1.64e-02 3.31e-02 1.04e-01 3.91e-01 5.26e-01 49 50 51 52 53 54 55 56 2.71e-01 -1.71e-02 1.05e-01 -3.02e-01 1.87e-01 -2.43e-01 -4.37e-02 9.21e-01 57 58 59 60 61 62 63 64 2.25e-01 1.18e+00 2.14e-01 1.72e-01 4.84e-01 1.60e-01 9.35e-03 4.39e-01 65 66 67 68 69 70 71 72 1.50e-01 2.47e-01 5.40e-01 5.93e-01 2.19e-01 -1.96e-01 5.97e-01 2.56e-01 73 74 75 76 77 78 79 80 -5.77e-01 -5.53e-02 4.85e-02 -1.87e-02 1.04e-01 8.16e-01 3.78e-01 -2.25e-01 81 82 83 84 85 86 87 88 -3.06e-01 -3.36e-01 -1.17e-01 1.10e-01 -1.13e-01 1.52e-01 -3.09e-01 -2.66e-01 89 90 91 92 93 94 95 96 4.94e-01 1.85e-01 1.63e-01 1.28e-01 2.03e-01 1.58e-02 7.94e-02 7.70e-02 97 98 99 100 101 102 103 104 1.34e-01 2.60e-01 4.94e-01 2.93e-01 1.66e-01 2.38e-01 3.93e-02 6.40e-01 105 106 107 108 109 110 111 112 1.72e-01 2.45e-01 3.92e-01 4.02e-02 1.46e-01 2.24e-01 5.54e-01 1.83e-01 113 114 115 116 117 118 119 120 5.20e-01 1.10e+00 2.19e-01 2.53e-01 3.87e-01 1.39e-01 1.02e+00 3.09e-01 121 122 123 124 125 126 127 128 -2.26e-01 5.18e-01 1.45e-01 4.89e-01 1.03e+00 1.75e-01 5.06e-01 1.21e-01 129 130 131 132 133 134 135 136 -7.03e-03 -2.00e-01 6.85e-02 -2.94e-02 1.64e-01 3.83e-01 1.84e-02 2.54e-02 137 138 139 140 141 142 143 144 2.49e-01 4.04e-01 1.45e-01 3.45e-01 -2.58e-01 9.80e-01 3.65e-01 -1.99e-01 145 146 147 148 149 150 151 152 -1.33e-01 1.30e-01 5.44e-02 2.72e-01 -2.73e-02 6.23e-01 7.12e-01 4.42e-01 153 154 155 156 157 158 159 160 1.72e-01 4.40e-01 1.06e+00 6.27e-01 6.01e-02 1.02e+00 1.49e-01 1.68e-01 161 162 163 164 165 166 167 168 7.79e-02 6.52e-01 3.66e-01 5.19e-01 5.33e-01 -1.44e-01 1.05e+00 8.36e-01 169 170 171 172 173 174 175 176 1.30e-01 4.89e-01 6.41e-02 -3.85e-01 5.39e-02 3.54e-01 1.46e-01 -2.36e-01 177 178 179 180 181 182 183 184 5.76e-01 5.20e-01 -9.90e-02 4.75e-02 4.18e-01 1.38e-01 -3.55e-02 4.49e-01 185 186 187 188 189 190 191 192 2.27e-01 1.02e-01 1.11e+00 7.84e-03 1.31e-01 4.38e-01 3.71e-02 9.64e-02 193 194 195 196 197 198 199 200 1.22e-01 -1.83e-01 2.96e-01 2.35e-01 2.08e-01 2.79e-01 2.64e-01 6.67e-01 201 202 203 204 205 206 207 208 2.98e-01 6.43e-01 1.04e+00 6.05e-02 2.56e-01 -1.97e-01 1.56e-01 1.59e-01 209 210 211 212 213 214 215 216 6.46e-01 6.85e-01 2.85e-01 1.38e-01 1.81e-01 -6.57e-01 1.30e-01 8.47e-01 217 218 219 220 221 222 223 224 1.90e-01 2.67e-02 5.15e-01 -4.10e-02 3.78e-02 5.86e-02 4.96e-01 2.07e-01 225 226 227 228 229 230 231 232 5.37e-01 6.78e-01 1.97e-01 7.24e-01 2.37e-01 1.57e-01 2.02e-01 -2.31e-01 233 234 235 236 237 238 239 240 7.04e-02 1.46e-01 1.72e-01 2.22e-01 -4.72e-01 8.96e-06 7.73e-01 7.52e-01 241 242 243 244 245 246 247 248 3.69e-01 2.50e-01 1.33e-01 1.79e-01 6.52e-02 1.72e-01 -2.15e-01 3.20e-02 249 250 251 252 253 254 255 256 2.52e-01 5.02e-01 4.28e-01 -3.94e-01 7.11e-01 1.61e-01 1.75e-01 2.56e-01 257 258 259 260 261 262 263 264 3.06e-01 -3.21e-01 2.05e-01 -1.10e-02 1.20e-01 5.82e-01 3.79e-01 5.53e-01 265 266 267 268 269 270 271 272 -6.02e-01 -7.87e-03 1.69e-01 1.87e-01 3.31e-01 5.62e-01 1.39e-01 -1.36e-01 273 274 275 276 277 278 279 280 -5.47e-01 1.51e-01 6.94e-01 8.29e-01 2.14e-01 2.10e-01 6.83e-01 3.10e-01 281 282 283 284 285 286 287 288 4.26e-01 1.90e-01 8.84e-02 5.67e-01 3.69e-01 4.52e-01 3.99e-01 1.14e-01 289 290 291 292 293 294 295 296 2.59e-01 -4.30e-03 5.82e-01 7.26e-01 7.53e-01 2.96e-01 -1.38e-01 4.04e-01 297 298 299 300 301 302 303 304 2.62e-01 2.86e-02 4.05e-01 6.73e-01 5.19e-01 6.50e-01 7.23e-02 4.97e-01 305 306 307 308 309 310 311 312 1.11e-01 2.32e-01 1.06e-01 -3.34e-01 2.42e-01 2.40e-01 -1.37e-01 3.11e-01 313 314 315 316 317 318 319 320 2.53e-01 1.23e-01 5.19e-01 2.74e-01 4.62e-01 1.15e-01 6.17e-01 3.19e-03 321 322 323 324 325 326 327 328 5.25e-01 4.62e-01 -1.68e-01 3.17e-01 2.29e-02 -1.71e-01 -1.49e-01 2.81e-01 329 330 331 332 333 334 335 336 1.30e-01 7.38e-01 1.99e-01 1.08e+00 -1.53e-01 -3.35e-02 6.17e-02 9.97e-02 337 338 339 340 341 342 343 344 5.62e-01 1.92e-01 2.24e-01 -9.42e-02 4.62e-01 2.94e-01 3.35e-01 1.12e-01 345 346 347 348 349 350 351 352 5.71e-01 9.06e-01 5.44e-01 2.72e-01 -6.28e-01 1.29e+00 5.87e-01 2.11e-01 353 354 355 356 357 358 359 360 1.74e-01 1.39e-01 6.77e-01 5.91e-01 1.11e+00 1.00e-01 -2.37e-01 1.45e-01 361 362 363 364 365 366 367 368 2.07e-01 5.04e-01 4.31e-01 2.88e-01 3.79e-02 -2.15e-01 1.72e-01 5.38e-01 369 370 371 372 373 374 375 376 8.28e-01 4.68e-01 4.78e-01 1.47e-01 3.28e-01 1.46e-01 -1.23e-01 1.71e-01 377 378 379 380 381 382 383 384 2.02e-01 5.48e-01 2.48e-01 -3.31e-01 5.18e-01 3.67e-01 5.83e-01 9.35e-03 385 386 387 388 389 390 391 392 1.58e-01 1.35e+00 -5.75e-02 1.08e-01 7.06e-01 6.27e-01 -5.69e-01 4.56e-01 393 394 395 396 397 398 399 400 3.02e-01 6.58e-01 4.39e-01 3.66e-01 -6.39e-01 1.04e+00 2.21e-01 5.76e-01 401 402 403 404 405 406 407 408 3.95e-01 -2.26e-01 4.93e-01 1.70e-01 1.88e-01 2.20e-01 1.66e-01 2.13e-01 409 410 411 412 413 414 415 416 6.27e-01 1.29e-01 1.62e-01 7.14e-01 8.36e-02 4.27e-01 4.49e-01 2.86e-01 417 418 419 420 421 422 423 424 7.56e-01 -2.13e-01 1.78e-01 9.42e-02 -7.88e-02 1.12e-01 5.91e-01 -3.15e-03 425 426 427 428 429 430 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656 3.52e-01 -8.19e-01 2.34e-01 5.17e-02 3.92e-02 2.14e-01 1.99e-01 -3.15e-01 657 658 659 660 661 662 663 664 4.33e-01 -4.30e-01 4.37e-01 -2.46e-01 -1.37e-01 4.61e-01 -8.56e-02 5.43e-02 665 666 667 668 669 670 671 672 2.76e-01 -6.04e-02 4.08e-01 1.05e-02 1.36e-01 -8.38e-02 5.90e-03 5.17e-01 673 674 675 676 677 678 679 680 -1.34e-02 -1.20e-01 4.16e-01 2.78e-01 -6.06e-01 3.42e-01 2.67e-01 9.09e-01 681 682 683 684 685 686 687 688 -2.79e-01 -1.04e-01 7.22e-01 4.75e-02 2.67e-01 1.83e-01 3.45e-01 3.50e-01 689 690 691 692 693 694 695 696 -3.48e-01 -1.42e-01 1.71e-01 3.32e-01 2.80e-01 4.16e-01 1.95e-01 1.17e-01 697 698 699 700 701 702 703 704 2.79e-01 1.26e-01 5.08e-01 1.19e-01 -1.86e-01 -4.29e-01 2.85e-01 7.68e-02 705 706 707 708 709 710 711 712 1.12e-01 -3.69e-01 -1.56e-01 5.83e-01 1.26e-01 1.17e-01 6.25e-02 5.26e-03 713 714 715 716 717 718 719 720 5.63e-02 -4.16e-01 5.17e-01 2.38e-01 2.11e-01 1.94e-01 4.31e-03 -2.88e-01 721 722 723 724 725 726 727 728 2.15e-01 2.38e-01 4.07e-01 1.52e-01 -8.25e-01 -7.75e-01 -1.87e-01 4.48e-01 729 730 731 732 733 734 735 736 4.06e-01 2.67e-01 3.87e-01 6.36e-03 -3.36e-01 5.20e-01 3.84e-01 5.25e-02 737 738 739 740 741 742 743 744 2.04e-01 2.68e-01 3.92e-01 3.75e-01 6.68e-03 2.19e-01 -1.17e-01 -3.09e-01 745 746 747 748 749 750 751 752 2.43e-03 1.49e-01 -1.44e-01 1.27e-01 3.34e-01 3.42e-02 5.53e-01 -4.74e-01 753 -5.16e-02 > all.equal( linearPredictors( greene ), predict( greene ) ) [1] TRUE > all.equal( linearPredictors( greene )[ 11:222 ], + predict( greene, newdata = Mroz87[ 11:222, ] ) ) [1] TRUE > residuals( greene, type = "response" ) 1 2 3 4 5 6 7 8 9 10 0.4619 0.4797 0.4263 0.4582 0.3784 0.4808 0.2741 0.4819 0.5098 0.4182 11 12 13 14 15 16 17 18 19 20 0.4221 0.4550 0.4912 0.4459 0.4974 0.4684 0.4427 0.4205 0.2965 0.4276 21 22 23 24 25 26 27 28 29 30 0.2703 0.3212 0.2970 0.3059 0.4818 0.2462 0.6561 0.4443 0.2746 0.2710 31 32 33 34 35 36 37 38 39 40 0.4368 0.5029 0.3769 0.5700 0.3056 0.4783 0.3052 0.5521 0.3155 0.1450 41 42 43 44 45 46 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-0.5866 -0.4534 -0.3788 -0.5010 -0.5592 -0.4426 -0.5506 -0.6309 -0.5136 751 752 753 -0.7100 -0.3179 -0.4794 > residuals( greene, type = "pearson" ) 1 2 3 4 5 6 7 8 9 10 11 0.927 0.960 0.862 0.920 0.780 0.962 0.614 0.964 1.020 0.848 0.855 12 13 14 15 16 17 18 19 20 21 22 0.914 0.983 0.897 0.995 0.939 0.891 0.852 0.649 0.864 0.609 0.688 23 24 25 26 27 28 29 30 31 32 33 0.650 0.664 0.964 0.571 1.381 0.894 0.615 0.610 0.881 1.006 0.778 34 35 36 37 38 39 40 41 42 43 44 1.151 0.663 0.958 0.663 1.110 0.679 0.412 0.982 1.151 0.673 1.013 45 46 47 48 49 50 51 52 53 54 55 0.974 0.921 0.731 0.654 0.805 1.014 0.920 1.273 0.861 1.215 1.035 56 57 58 59 60 61 62 63 64 65 66 0.466 0.835 0.367 0.843 0.872 0.677 0.880 0.993 0.702 0.887 0.821 67 68 69 70 71 72 73 74 75 76 77 0.646 0.618 0.839 1.169 0.616 0.815 1.596 1.045 0.962 1.015 0.920 78 79 80 81 82 83 84 85 86 87 88 0.511 0.738 1.197 1.278 1.309 1.098 0.916 1.094 0.886 1.281 1.238 89 90 91 92 93 94 95 96 97 98 99 0.671 0.863 0.878 0.903 0.851 0.987 0.939 0.940 0.899 0.812 0.671 100 101 102 103 104 105 106 107 108 109 110 0.791 0.876 0.826 0.969 0.594 0.872 0.822 0.730 0.968 0.890 0.836 111 112 113 114 115 116 117 118 119 120 121 0.639 0.864 0.657 0.396 0.839 0.816 0.733 0.895 0.426 0.781 1.199 122 123 124 125 126 127 128 129 130 131 132 0.658 0.890 0.674 0.421 0.869 0.665 0.908 1.006 1.173 0.947 1.024 133 134 135 136 137 138 139 140 141 142 143 0.877 0.735 0.985 0.980 0.819 0.722 0.890 0.758 1.230 0.442 0.746 144 145 146 147 148 149 150 151 152 153 154 1.172 1.112 0.901 0.958 0.805 1.022 0.603 0.559 0.701 0.871 0.702 155 156 157 158 159 160 161 162 163 164 165 0.410 0.601 0.953 0.425 0.888 0.874 0.940 0.589 0.745 0.658 0.650 166 167 168 169 170 171 172 173 174 175 176 1.122 0.415 0.502 0.901 0.674 0.950 1.363 0.958 0.753 0.890 1.208 177 178 179 180 181 182 183 184 185 186 187 0.627 0.657 1.082 0.963 0.715 0.896 1.029 0.697 0.834 0.922 0.394 188 189 190 191 192 193 194 195 196 197 198 0.994 0.901 0.703 0.971 0.926 0.907 1.157 0.789 0.829 0.846 0.800 199 200 201 202 203 204 205 206 207 208 209 0.809 0.581 0.787 0.593 0.420 0.953 0.815 1.171 0.883 0.881 0.592 210 211 212 213 214 215 216 217 218 219 220 0.572 0.796 0.895 0.865 1.706 0.901 0.498 0.859 0.979 0.660 1.033 221 222 223 224 225 226 227 228 229 230 231 0.970 0.954 0.670 0.848 0.648 0.576 0.854 0.554 0.827 0.882 0.851 232 233 234 235 236 237 238 239 240 241 242 1.203 0.945 0.890 0.872 0.837 1.462 1.000 0.531 0.540 0.744 0.819 243 244 245 246 247 248 249 250 251 252 253 0.899 0.867 0.949 0.872 1.188 0.975 0.818 0.667 0.709 1.373 0.560 254 255 256 257 258 259 260 261 262 263 264 0.879 0.869 0.815 0.783 1.294 0.849 1.009 0.909 0.624 0.738 0.639 265 266 267 268 269 270 271 272 273 274 275 1.630 1.006 0.874 0.861 0.767 0.635 0.895 1.115 1.556 0.887 0.568 276 277 278 279 280 281 282 283 284 285 286 0.506 0.842 0.845 0.573 0.780 0.710 0.859 0.932 0.632 0.744 0.695 287 288 289 290 291 292 293 294 295 296 297 0.726 0.913 0.813 1.003 0.624 0.553 0.540 0.789 1.117 0.723 0.811 298 299 300 301 302 303 304 305 306 307 308 0.977 0.722 0.578 0.657 0.589 0.944 0.670 0.915 0.830 0.919 1.307 309 310 311 312 313 314 315 316 317 318 319 0.824 0.825 1.116 0.779 0.817 0.907 0.658 0.803 0.689 0.912 0.606 320 321 322 323 324 325 326 327 328 329 330 0.997 0.654 0.689 1.144 0.776 0.982 1.147 1.126 0.799 0.901 0.547 331 332 333 334 335 336 337 338 339 340 341 0.853 0.402 1.130 1.027 0.952 0.923 0.635 0.858 0.836 1.078 0.689 342 343 344 345 346 347 348 349 350 351 352 0.790 0.765 0.914 0.630 0.472 0.644 0.804 1.666 0.330 0.622 0.844 353 354 355 356 357 358 359 360 361 362 363 0.870 0.895 0.576 0.619 0.392 0.923 1.208 0.890 0.848 0.666 0.707 364 365 366 367 368 369 370 371 372 373 374 0.794 0.970 1.188 0.871 0.647 0.506 0.686 0.680 0.889 0.769 0.890 375 376 377 378 379 380 381 382 383 384 385 1.103 0.872 0.851 0.642 0.820 1.304 0.658 0.745 0.624 0.993 0.881 386 387 388 389 390 391 392 393 394 395 396 0.311 1.047 0.917 0.562 0.601 1.586 0.693 0.785 0.585 0.702 0.745 397 398 399 400 401 402 403 404 405 406 407 1.680 0.420 0.838 0.627 0.728 1.198 0.672 0.873 0.861 0.839 0.876 408 409 410 411 412 413 414 415 416 417 418 0.844 0.601 0.902 0.878 0.558 0.935 0.709 0.697 0.795 0.538 1.185 419 420 421 422 423 424 425 426 427 428 429 0.868 0.928 1.065 0.914 0.619 1.003 0.806 0.778 0.862 0.974 -0.945 430 431 432 433 434 435 436 437 438 439 440 -1.439 -1.045 -1.231 -1.085 -1.135 -1.292 -0.658 -0.830 -0.992 -0.949 -1.080 441 442 443 444 445 446 447 448 449 450 451 -0.610 -0.960 -0.925 -1.077 -0.929 -1.344 -1.288 -1.097 -1.129 -1.517 -0.845 452 453 454 455 456 457 458 459 460 461 462 -1.110 -1.889 -1.136 -1.122 -1.072 -0.675 -1.550 -1.112 -1.045 -0.890 -0.922 463 464 465 466 467 468 469 470 471 472 473 -1.078 -0.859 -0.972 -1.516 -1.237 -1.282 -1.036 -1.009 -0.729 -1.106 -1.078 474 475 476 477 478 479 480 481 482 483 484 -0.974 -1.165 -1.338 -1.108 -0.816 -1.111 -1.562 -1.486 -1.121 -1.304 -1.406 485 486 487 488 489 490 491 492 493 494 495 -0.550 -1.290 -0.475 -1.907 -1.246 -1.795 -1.199 -1.675 -1.487 -0.991 -1.073 496 497 498 499 500 501 502 503 504 505 506 -1.117 -1.045 -0.903 -1.285 -0.799 -1.298 -2.303 -1.524 -1.159 -1.172 -1.401 507 508 509 510 511 512 513 514 515 516 517 -1.288 -1.130 -1.161 -0.910 -1.066 -1.176 -0.974 -0.720 -0.995 -1.155 -0.848 518 519 520 521 522 523 524 525 526 527 528 -1.203 -1.202 -1.168 -0.748 -1.143 -1.378 -1.085 -0.860 -1.095 -1.042 -1.144 529 530 531 532 533 534 535 536 537 538 539 -0.720 -0.833 -0.632 -1.454 -1.285 -1.061 -1.644 -1.151 -0.915 -0.676 -1.154 540 541 542 543 544 545 546 547 548 549 550 -0.737 -0.740 -0.899 -1.466 -0.938 -1.064 -1.296 -0.812 -1.111 -0.880 -1.007 551 552 553 554 555 556 557 558 559 560 561 -0.932 -0.739 -1.093 -1.293 -0.807 -1.063 -1.329 -1.100 -0.799 -1.032 -0.711 562 563 564 565 566 567 568 569 570 571 572 -0.688 -1.106 -0.982 -0.867 -0.919 -0.625 -1.526 -1.149 -1.059 -1.099 -1.113 573 574 575 576 577 578 579 580 581 582 583 -1.054 -1.756 -1.592 -1.164 -1.042 -1.076 -1.371 -0.980 -1.159 -1.357 -0.884 584 585 586 587 588 589 590 591 592 593 594 -1.464 -1.480 -0.454 -1.122 -1.457 -1.111 -1.196 -0.873 -1.199 -0.863 -0.763 595 596 597 598 599 600 601 602 603 604 605 -0.963 -0.709 -1.116 -0.614 -1.269 -0.865 -0.969 -1.555 -1.133 -0.982 -1.242 606 607 608 609 610 611 612 613 614 615 616 -0.991 -0.984 -0.980 -1.064 -1.818 -1.180 -1.092 -1.039 -1.161 -1.078 -0.906 617 618 619 620 621 622 623 624 625 626 627 -0.687 -1.233 -1.103 -0.930 -1.090 -1.050 -1.062 -0.949 -1.034 -1.336 -1.144 628 629 630 631 632 633 634 635 636 637 638 -1.263 -0.852 -1.208 -0.497 -0.843 -1.131 -1.293 -1.039 -1.563 -1.088 -0.733 639 640 641 642 643 644 645 646 647 648 649 -1.036 -0.439 -1.438 -1.198 -0.763 -1.512 -1.255 -1.484 -2.307 -1.340 -1.327 650 651 652 653 654 655 656 657 658 659 660 -0.510 -1.206 -1.042 -1.032 -1.187 -1.173 -0.777 -1.417 -0.708 -1.422 -0.822 661 662 663 664 665 666 667 668 669 670 671 -0.896 -1.450 -0.934 -1.044 -1.247 -0.953 -1.389 -1.008 -1.115 -0.935 -1.005 672 673 674 675 676 677 678 679 680 681 682 -1.519 -0.989 -0.909 -1.397 -1.249 -0.611 -1.315 -1.238 -2.122 -0.800 -0.920 683 684 685 686 687 688 689 690 691 692 693 -1.803 -1.039 -1.238 -1.158 -1.319 -1.324 -0.756 -0.893 -1.147 -1.305 -1.251 694 695 696 697 698 699 700 701 702 703 704 -1.398 -1.169 -1.098 -1.250 -1.105 -1.507 -1.100 -0.862 -0.708 -1.256 -1.063 705 706 707 708 709 710 711 712 713 714 715 -1.094 -0.744 -0.883 -1.604 -1.106 -1.098 -1.051 -1.004 -1.046 -0.716 -1.519 716 717 718 719 720 721 722 723 724 725 726 -1.209 -1.183 -1.168 -1.003 -0.794 -1.187 -1.210 -1.387 -1.129 -0.507 -0.530 727 728 729 730 731 732 733 734 735 736 737 -0.861 -1.435 -1.386 -1.238 -1.364 -1.005 -0.764 -1.522 -1.361 -1.043 -1.177 738 739 740 741 742 743 744 745 746 747 748 -1.239 -1.370 -1.352 -1.005 -1.191 -0.911 -0.781 -1.002 -1.126 -0.891 -1.107 749 750 751 752 753 -1.307 -1.028 -1.565 -0.683 -0.960 > residuals( greene, type = "deviance" ) 1 2 3 4 5 6 7 8 9 10 11 1.113 1.143 1.054 1.107 0.975 1.145 0.800 1.147 1.194 1.041 1.047 12 13 14 15 16 17 18 19 20 21 22 1.102 1.163 1.087 1.173 1.124 1.081 1.045 0.839 1.056 0.794 0.880 23 24 25 26 27 28 29 30 31 32 33 0.839 0.855 1.147 0.752 1.461 1.084 0.801 0.795 1.072 1.182 0.973 34 35 36 37 38 39 40 41 42 43 44 1.299 0.854 1.141 0.853 1.267 0.871 0.560 1.162 1.299 0.864 1.189 45 46 47 48 49 50 51 52 53 54 55 1.155 1.108 0.925 0.844 1.000 1.189 1.107 1.388 1.053 1.347 1.207 56 57 58 59 60 61 62 63 64 65 66 0.627 1.029 0.502 1.036 1.063 0.868 1.071 1.171 0.895 1.077 1.015 67 68 69 70 71 72 73 74 75 76 77 0.835 0.805 1.032 1.313 0.802 1.009 1.591 1.215 1.145 1.190 1.108 78 79 80 81 82 83 84 85 86 87 88 0.682 0.932 1.333 1.391 1.413 1.258 1.104 1.255 1.076 1.393 1.363 89 90 91 92 93 94 95 96 97 98 99 0.862 1.055 1.069 1.092 1.043 1.167 1.124 1.126 1.088 1.006 0.863 100 101 102 103 104 105 106 107 108 109 110 0.985 1.067 1.020 1.151 0.778 1.063 1.016 0.924 1.150 1.080 1.030 111 112 113 114 115 116 117 118 119 120 121 0.827 1.056 0.847 0.540 1.033 1.011 0.927 1.085 0.577 0.976 1.335 122 123 124 125 126 127 128 129 130 131 132 0.848 1.080 0.865 0.572 1.061 0.855 1.097 1.182 1.316 1.131 1.197 133 134 135 136 137 138 139 140 141 142 143 1.068 0.929 1.165 1.160 1.014 0.917 1.081 0.953 1.357 0.597 0.941 144 145 146 147 148 149 150 151 152 153 154 1.315 1.269 1.091 1.141 0.999 1.196 0.788 0.738 0.894 1.063 0.895 155 156 157 158 159 160 161 162 163 164 165 0.558 0.785 1.137 0.576 1.078 1.066 1.125 0.771 0.940 0.848 0.839 166 167 168 169 170 171 172 173 174 175 176 1.277 0.564 0.671 1.091 0.865 1.134 1.449 1.141 0.947 1.080 1.341 177 178 179 180 181 182 183 184 185 186 187 0.815 0.847 1.245 1.145 0.909 1.085 1.202 0.889 1.028 1.109 0.537 188 189 190 191 192 193 194 195 196 197 198 1.172 1.090 0.896 1.152 1.113 1.096 1.304 0.984 1.022 1.040 0.994 199 200 201 202 203 204 205 206 207 208 209 1.004 0.763 0.982 0.776 0.570 1.137 1.009 1.314 1.074 1.072 0.775 210 211 212 213 214 215 216 217 218 219 220 0.753 0.991 1.085 1.057 1.651 1.090 0.665 1.051 1.159 0.850 1.205 221 222 223 224 225 226 227 228 229 230 231 1.152 1.138 0.862 1.040 0.837 0.757 1.047 0.731 1.021 1.073 1.044 232 233 234 235 236 237 238 239 240 241 242 1.338 1.130 1.080 1.063 1.031 1.512 1.177 0.705 0.716 0.938 1.013 243 244 245 246 247 248 249 250 251 252 253 1.089 1.059 1.134 1.063 1.326 1.156 1.012 0.858 0.902 1.455 0.738 254 255 256 257 258 259 260 261 262 263 264 1.070 1.061 1.009 0.978 1.403 1.042 1.185 1.097 0.811 0.932 0.828 265 266 267 268 269 270 271 272 273 274 275 1.610 1.183 1.065 1.054 0.962 0.823 1.085 1.271 1.568 1.077 0.748 276 277 278 279 280 281 282 283 284 285 286 0.675 1.036 1.038 0.754 0.975 0.903 1.051 1.118 0.819 0.938 0.887 287 288 289 290 291 292 293 294 295 296 297 0.920 1.101 1.007 1.180 0.811 0.730 0.715 0.984 1.273 0.917 1.005 298 299 300 301 302 303 304 305 306 307 308 1.158 0.916 0.760 0.848 0.772 1.129 0.861 1.103 1.024 1.106 1.412 309 310 311 312 313 314 315 316 317 318 319 1.018 1.019 1.272 0.974 1.011 1.095 0.848 0.998 0.882 1.101 0.791 320 321 322 323 324 325 326 327 328 329 330 1.175 0.844 0.882 1.293 0.971 1.162 1.296 1.280 0.993 1.091 0.723 331 332 333 334 335 336 337 338 339 340 341 1.045 0.548 1.283 1.200 1.136 1.111 0.823 1.050 1.030 1.242 0.881 342 343 344 345 346 347 348 349 350 351 352 0.985 0.960 1.102 0.818 0.635 0.833 0.999 1.630 0.455 0.808 1.038 353 354 355 356 357 358 359 360 361 362 363 1.062 1.085 0.757 0.806 0.535 1.110 1.342 1.081 1.041 0.857 0.900 364 365 366 367 368 369 370 371 372 373 374 0.989 1.152 1.327 1.063 0.837 0.675 0.878 0.872 1.079 0.964 1.080 375 376 377 378 379 380 381 382 383 384 385 1.262 1.064 1.044 0.830 1.014 1.410 0.848 0.939 0.811 1.171 1.072 386 387 388 389 390 391 392 393 394 395 396 0.429 1.217 1.105 0.741 0.785 1.585 0.886 0.980 0.768 0.896 0.940 397 398 399 400 401 402 403 404 405 406 407 1.638 0.569 1.031 0.815 0.922 1.335 0.863 1.065 1.053 1.032 1.067 408 409 410 411 412 413 414 415 416 417 418 1.037 0.785 1.091 1.069 0.736 1.121 0.903 0.890 0.990 0.714 1.325 419 420 421 422 423 424 425 426 427 428 429 1.060 1.114 1.231 1.102 0.806 1.180 1.000 0.973 1.054 1.155 -1.129 430 431 432 433 434 435 436 437 438 439 440 -1.498 -1.215 -1.359 -1.247 -1.287 -1.401 -0.848 -1.023 -1.171 -1.133 -1.244 441 442 443 444 445 446 447 448 449 450 451 -0.796 -1.143 -1.112 -1.241 -1.116 -1.437 -1.399 -1.257 -1.282 -1.545 -1.038 452 453 454 455 456 457 458 459 460 461 462 -1.267 -1.743 -1.288 -1.277 -1.237 -0.867 -1.565 -1.269 -1.215 -1.080 -1.110 463 464 465 466 467 468 469 470 471 472 473 -1.242 -1.051 -1.153 -1.545 -1.363 -1.395 -1.208 -1.185 -0.924 -1.264 -1.242 474 475 476 477 478 479 480 481 482 483 484 -1.156 -1.309 -1.432 -1.265 -1.011 -1.268 -1.572 -1.527 -1.276 -1.410 -1.477 485 486 487 488 489 490 491 492 493 494 495 -0.727 -1.400 -0.638 -1.752 -1.369 -1.697 -1.335 -1.635 -1.528 -1.170 -1.238 496 497 498 499 500 501 502 503 504 505 506 -1.273 -1.215 -1.092 -1.396 -0.994 -1.405 -1.919 -1.550 -1.305 -1.315 -1.474 507 508 509 510 511 512 513 514 515 516 517 -1.399 -1.283 -1.306 -1.099 -1.232 -1.318 -1.155 -0.914 -1.174 -1.302 -1.041 518 519 520 521 522 523 524 525 526 527 528 -1.338 -1.337 -1.312 -0.942 -1.293 -1.459 -1.248 -1.053 -1.256 -1.212 -1.294 529 530 531 532 533 534 535 536 537 538 539 -0.913 -1.027 -0.820 -1.507 -1.397 -1.228 -1.618 -1.299 -1.103 -0.867 -1.301 540 541 542 543 544 545 546 547 548 549 550 -0.931 -0.935 -1.088 -1.515 -1.123 -1.231 -1.404 -1.007 -1.268 -1.071 -1.183 551 552 553 554 555 556 557 558 559 560 561 -1.118 -0.934 -1.254 -1.402 -1.001 -1.229 -1.427 -1.260 -0.994 -1.204 -0.905 562 563 564 565 566 567 568 569 570 571 572 -0.881 -1.264 -1.162 -1.059 -1.106 -0.813 -1.551 -1.297 -1.226 -1.259 -1.270 573 574 575 576 577 578 579 580 581 582 583 -1.222 -1.677 -1.589 -1.309 -1.213 -1.240 -1.454 -1.161 -1.305 -1.445 -1.074 584 585 586 587 588 589 590 591 592 593 594 -1.513 -1.523 -0.612 -1.277 -1.509 -1.268 -1.333 -1.064 -1.335 -1.055 -0.958 595 596 597 598 599 600 601 602 603 604 605 -1.145 -0.902 -1.272 -0.800 -1.385 -1.057 -1.151 -1.568 -1.285 -1.162 -1.366 606 607 608 609 610 611 612 613 614 615 616 -1.169 -1.164 -1.161 -1.230 -1.709 -1.321 -1.253 -1.210 -1.307 -1.242 -1.094 617 618 619 620 621 622 623 624 625 626 627 -0.879 -1.360 -1.262 -1.116 -1.251 -1.219 -1.229 -1.133 -1.206 -1.431 -1.294 628 629 630 631 632 633 634 635 636 637 638 -1.381 -1.044 -1.341 -0.664 -1.036 -1.284 -1.402 -1.210 -1.573 -1.250 -0.927 639 640 641 642 643 644 645 646 647 648 649 -1.207 -0.594 -1.497 -1.334 -0.958 -1.542 -1.375 -1.525 -1.921 -1.434 -1.425 650 651 652 653 654 655 656 657 658 659 660 -0.680 -1.340 -1.213 -1.204 -1.326 -1.315 -0.972 -1.484 -0.901 -1.487 -1.016 661 662 663 664 665 666 667 668 669 670 671 -1.086 -1.505 -1.120 -1.214 -1.370 -1.137 -1.466 -1.185 -1.271 -1.121 -1.181 672 673 674 675 676 677 678 679 680 681 682 -1.546 -1.168 -1.097 -1.471 -1.371 -0.797 -1.417 -1.363 -1.847 -0.995 -1.108 683 684 685 686 687 688 689 690 691 692 693 -1.701 -1.210 -1.364 -1.304 -1.420 -1.423 -0.951 -1.083 -1.296 -1.410 -1.373 694 695 696 697 698 699 700 701 702 703 704 -1.472 -1.313 -1.258 -1.372 -1.264 -1.540 -1.259 -1.054 -0.902 -1.376 -1.230 705 706 707 708 709 710 711 712 713 714 715 -1.254 -0.938 -1.074 -1.596 -1.264 -1.258 -1.220 -1.181 -1.216 -0.909 -1.547 716 717 718 719 720 721 722 723 724 725 726 -1.343 -1.323 -1.311 -1.180 -0.989 -1.326 -1.343 -1.465 -1.282 -0.677 -0.703 727 728 729 730 731 732 733 734 735 736 737 -1.054 -1.495 -1.464 -1.363 -1.450 -1.182 -0.959 -1.548 -1.448 -1.213 -1.319 738 739 740 741 742 743 744 745 746 747 748 -1.364 -1.454 -1.442 -1.182 -1.329 -1.099 -0.976 -1.179 -1.280 -1.081 -1.265 749 750 751 752 753 -1.412 -1.201 -1.574 -0.875 -1.143 > > # estimation with glm() > greene2 <- glm( lfp ~ age + I( age^2 ) + faminc + kids + educ, + data = Mroz87, family = binomial( link = "probit" ) ) > all.equal( coef( greene ), coef( greene2 ), tol = 1e-3 ) [1] TRUE > all.equal( stdEr( greene ), stdEr( greene2 ), tol = 1e-1 ) [1] TRUE > all.equal( logLik( greene ), logLik( greene2 ) ) [1] TRUE > all.equal( lrtest( greene ), lrtest( greene2 ) ) [1] TRUE > all.equal( lrtest( greene, lfp ~ age + kids + educ ), + lrtest( greene2, lfp ~ age + kids + educ ) ) [1] TRUE > all.equal( model.frame( greene ), model.frame( greene2 ) ) [1] TRUE > all.equal( model.matrix( greene ), model.matrix( greene2 ) ) [1] TRUE > all.equal( fitted( greene ), fitted( greene2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( greene, type = "response" ), + predict( greene2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( predict( greene, newdata = Mroz87[ 5:333, ], type = "response" ), + predict( greene2, newdata = Mroz87[ 5:333, ], type = "response" ), + tol = 1e-4 ) [1] TRUE > all.equal( predict( greene ), predict( greene2 ), tol = 1e-4 ) [1] TRUE > all.equal( predict( greene, newdata = Mroz87[ 2:444, ] ), + predict( greene2, newdata = Mroz87[ 2:444, ] ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( greene, type = "response" ), + residuals( greene2, type = "response" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( greene, type = "pearson" ), + residuals( greene2, type = "pearson" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( greene, type = "deviance" ), + residuals( greene2, type = "deviance" ), tol = 1e-4 ) [1] TRUE > all.equal( residuals( greene ), residuals( greene2 ), tol = 1e-4 ) [1] TRUE > > > ## factors as dependent variable (from Achim Zeileis) > probit( lfp ~ exper, data = Mroz87 ) Call: probit(formula = lfp ~ exper, data = Mroz87) Coefficients: (Intercept) exper -0.4431 0.0605 > probit( factor( lfp ) ~ exper, data = Mroz87 ) Call: probit(formula = factor(lfp) ~ exper, data = Mroz87) Coefficients: (Intercept) exper -0.4431 0.0605 > probit( factor( lfp, labels = c( "no", "yes" ) ) ~ exper, data = Mroz87 ) Call: probit(formula = factor(lfp, labels = c("no", "yes")) ~ exper, data = Mroz87) Coefficients: (Intercept) exper -0.4431 0.0605 > > ## NA in data/linear predictors/na.exclude (by Gabor Grothendieck) > x <- runif(20) > y <- x + rnorm(length(x)) > 0 > y[1] <- y[4] <- NA > m <- probit(y ~ x, na.action = na.exclude) > length(linearPredictors(m)) [1] 20 > # should be 20 > nobs( m ) [1] 18 > nObs( m ) [1] 18 > df.residual( m ) [1] 16 > logLik( m ) 'log Lik.' -10.1 (df=2) > > # example, where the "cutoff" in the log likelihood function is used > # (inspired by an example from Jon K. Peck) > set.seed( 123 ) > nObs <- 1000 > dta2 <- data.frame( id = c( 1:nObs ) ) > for( i in 1:3 ) { + dta2[[ paste( "x", i, sep = "" ) ]] <- rnorm( nObs, 5, 3 ) + } > dta2$y <- with( dta2, -5.5 + 0.25 * x1 + 0.6 * x2 + 0.85 * x3 + rnorm( nObs ) ) > 0 > p2 <- probit( y ~ x1 + x2 + x3, data=dta2 ) > nobs( p2 ) [1] 1000 > nObs( p2 ) [1] 1000 > df.residual( p2 ) [1] 996 > logLik( p2 ) 'log Lik.' -139 (df=4) > > ## This test probes the extreme outliers. We generate a normal > ## model, and add a positive and a negative extreme outlier. The > ## likelihood code should be robust and not crash. The estimates > ## (and standard errors) are probably of little value. > x <- runif(20) - 0.5 > y <- x + rnorm(20) > 0 > x[1] <- 1000 > y[1] <- FALSE > x[2] <- -1000 > y[2] <- TRUE > m <- probit(y ~ x) > print(summary(m)) -------------------------------------------- Probit binary choice model/Maximum Likelihood estimation Newton-Raphson maximisation, 10 iterations Return code 2: successive function values within tolerance limit (tol) Log-Likelihood: -11.5 Model: Y == 'TRUE' in contrary to 'FALSE' 20 observations (13 'negative' and 7 'positive') and 2 free parameters (df = 18) Estimates: Estimate Std. error t value Pr(> t) (Intercept) -0.43072 0.30557 -1.41 0.16 x -0.00399 0.01958 -0.20 0.84 Significance test: chi2(1) = 2.98 (p=0.0844) -------------------------------------------- > nobs( m ) [1] 20 > nObs( m ) [1] 20 > df.residual( m ) [1] 18 > logLik( m ) 'log Lik.' -11.5 (df=2) > > proc.time() user system elapsed 1.89 0.32 2.18