R version 4.4.0 RC (2024-04-16 r86451 ucrt) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library( "censReg" )
Loading required package: maxLik
Loading required package: miscTools

Please cite the 'maxLik' package as:
Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.

If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:
https://r-forge.r-project.org/projects/maxlik/

Please cite the 'censReg' package as:
Henningsen, Arne (2017). censReg: Censored Regression (Tobit) Models. R package version 0.5. http://CRAN.R-Project.org/package=censReg.

If you have questions, suggestions, or comments regarding the 'censReg' package, please use a forum or 'tracker' at the R-Forge site of the 'sampleSelection' project:
https://r-forge.r-project.org/projects/sampleselection/
> library( "plm" )
> 
> options( digits = 5 )
> 
> nId <- 100
> nTime <- 3
> 
> set.seed( 123 )
> pData <- data.frame(
+    id = rep( paste( "F", 1:nId, sep = "_" ), each = nTime ),
+    time = rep( 1980 + 1:nTime, nId ) )
> pData$ui <- rep( rnorm( nId ), each = nTime )
> pData$x1 <- rnorm( nId * nTime )
> pData$x2 <- runif( nId * nTime )
> pData$ys <- -1 + pData$ui + 2 * pData$x1 + 3 * pData$x2 + rnorm( nId * nTime )
> pData$y <- ifelse( pData$ys > 0, pData$ys, 0 )
> pData <- pdata.frame( pData, c( "id", "time" ), stringsAsFactors = FALSE )
> 
> 
> # ## Newton-Raphson method
> # randEff <- censReg( y ~ x1 + x2, data = pData )
> # maxLik:::summary.maxLik( randEff )
> 
> ## BHHH method
> randEffBhhh <- censReg( y ~ x1 + x2, data = pData, method = "BHHH" )
> print( maxLik:::summary.maxLik( randEffBhhh ), digits = 2 )
--------------------------------------------
Maximum Likelihood estimation
BHHH maximisation, 11 iterations
Return code 8: successive function values within relative tolerance limit (reltol)
Log-Likelihood: -330.52 
5  free parameters
Estimates:
            Estimate Std. error t value Pr(> t)    
(Intercept)   -1.087      0.235    -4.6   4e-06 ***
x1             2.069      0.092    22.4  <2e-16 ***
x2             3.115      0.370     8.4  <2e-16 ***
logSigmaMu    -0.106      0.129    -0.8     0.4    
logSigmaNu     0.026      0.065     0.4     0.7    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------
> 
> ## BFGS method (optim)
> randEffBfgs <- censReg( y ~ x1 + x2, data = pData, method = "BFGS" )
> print( maxLik:::summary.maxLik( randEffBfgs ), digits = 2 )
--------------------------------------------
Maximum Likelihood estimation
BFGS maximization, 29 iterations
Return code 0: successful convergence 
Log-Likelihood: -330.52 
5  free parameters
Estimates:
            Estimate Std. error t value Pr(> t)    
(Intercept)   -1.087      0.226    -4.8   2e-06 ***
x1             2.070      0.097    21.4  <2e-16 ***
x2             3.115      0.327     9.5  <2e-16 ***
logSigmaMu    -0.106      0.131    -0.8     0.4    
logSigmaNu     0.026      0.070     0.4     0.7    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------
> 
> ## BFGS method (R)
> randEffBfgsr <- censReg( y ~ x1 + x2, data = pData, method = "BFGSR" )
> print( maxLik:::summary.maxLik( randEffBfgsr ), digits = 2 )
--------------------------------------------
Maximum Likelihood estimation
BFGSR maximization, 22 iterations
Return code 2: successive function values within tolerance limit (tol)
Log-Likelihood: -330.52 
5  free parameters
Estimates:
            Estimate Std. error t value Pr(> t)    
(Intercept)   -1.086      0.226    -4.8   2e-06 ***
x1             2.070      0.097    21.4  <2e-16 ***
x2             3.115      0.327     9.5  <2e-16 ***
logSigmaMu    -0.106      0.131    -0.8     0.4    
logSigmaNu     0.026      0.070     0.4     0.7    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--------------------------------------------
> 
> 
> proc.time()
   user  system elapsed 
   1.43    0.18    1.62