Running ‘censRegFail.R’ [1s/2s]
  Comparing ‘censRegFail.Rout’ to ‘censRegFail.Rout.save’ ... OK
  Running ‘censRegPanelLargerTest.R’ [2s/3s]
  Comparing ‘censRegPanelLargerTest.Rout’ to ‘censRegPanelLargerTest.Rout.save’ ... OK
  Running ‘censRegPanelTest.R’ [2s/2s]
  Running ‘censRegTest.R’ [8s/12s]
  Comparing ‘censRegTest.Rout’ to ‘censRegTest.Rout.save’ ...775,782d774
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
784,785d775
< max.rows = 20 
< max.cols = 7 
787,788d776
< storeValues = FALSE 
< storeParameters = FALSE 
2475,2482d2462
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
2484,2485d2463
< max.rows = 20 
< max.cols = 7 
2487,2488d2464
< storeValues = FALSE 
< storeParameters = FALSE 
3351,3358d3326
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
3360,3361d3327
< max.rows = 20 
< max.cols = 7 
3363,3364d3328
< storeValues = FALSE 
< storeParameters = FALSE 
4202,4209d4165
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
4211,4212d4166
< max.rows = 20 
< max.cols = 7 
4214,4215d4167
< storeValues = FALSE 
< storeParameters = FALSE 
5060,5067d5011
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
5069,5070d5012
< max.rows = 20 
< max.cols = 7 
5072,5073d5013
< storeValues = FALSE 
< storeParameters = FALSE 
5916,5923d5855
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
5925,5926d5856
< max.rows = 20 
< max.cols = 7 
5928,5929d5857
< storeValues = FALSE 
< storeParameters = FALSE 
6783,6790d6710
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
6792,6793d6711
< max.rows = 20 
< max.cols = 7 
6795,6796d6712
< storeValues = FALSE 
< storeParameters = FALSE 
7696,7703d7611
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
7705,7706d7612
< max.rows = 20 
< max.cols = 7 
7708,7709d7613
< storeValues = FALSE 
< storeParameters = FALSE 
9218,9225d9121
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
9227,9228d9122
< max.rows = 20 
< max.cols = 7 
9230,9231d9123
< storeValues = FALSE 
< storeParameters = FALSE 
10133,10140d10024
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
10142,10143d10025
< max.rows = 20 
< max.cols = 7 
10145,10146d10026
< storeValues = FALSE 
< storeParameters = FALSE 
11671,11678d11550
< SGA_momentum = 0 
< Adam_momentum1 = 0.9 
< Adam_momentum2 = 0.999 
< SG_patience = 
< SG_patienceStep = 1 
< SG_learningRate = 0.1 
< SG_batchSize = 
< SG_clip = 
11680,11681d11551
< max.rows = 20 
< max.cols = 7 
11683,11684d11552
< storeValues = FALSE 
< storeParameters = FALSE 
Running the tests in ‘tests/censRegPanelTest.R’ failed.
Complete output:
  > 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" )
  > 
  > # load outputs that were previously produced by this script 
  > saved <- new.env()
  > load( "censRegPanelTest.RData.save", envir = saved )
  > 
  > options( digits = 5 )
  > 
  > printAll <- function( objName, what = "print" ) {
  +    cat( "Comparing new object '", objName, "' to previously saved object...",
  +       sep = "" )
  +    x <- get( objName )
  +    if( !exists( objName, envir = saved, inherits = FALSE ) ) {
  +       cat( " previously saved object not found\n" )
  +    } else {
  +       xSaved <- get( objName, envir = saved, inherits = FALSE )
  +       if( !isTRUE( all.equal( class( x ), class( xSaved ) ) ) ) {
  +          cat( " different classes:\n" )
  +          cat( "new:\n" )
  +          print( class( x ) )
  +          cat( "saved:\n" )
  +          print( class( xSaved ) )
  +       } else if( !isTRUE( all.equal( names( x ), names( xSaved ) ) ) ) {
  +          cat( " different names:\n" )
  +          cat( "new:\n" )
  +          print( names( x ) )
  +          cat( "saved:\n" )
  +          print( names( xSaved ) )
  +       } else {
  +          cat( "\n" )
  +       }
  +       for( n in names( x ) ) {
  +          if( ! n %in% c( "code", "gradient", "iterations", "last.step",
  +                "message" ) ) {
  +             cat( "   comparing component '", n, "' ...", sep = "" )
  +             if( n == "vcov" ) {
  +                tol <- 5e-1
  +             } else if( n == "estimate" ) {
  +                tol <- 5e-2
  +             } else {
  +                tol <- 5e-3
  +             }
  +             testRes <-  all.equal( x[[ n ]], xSaved[[ n ]], tol = tol )
  +             if( isTRUE( testRes ) ) {
  +                cat( " OK\n" )
  +             } else {
  +                cat( " different\n" )
  +                print( testRes )
  +                cat( "new:\n" )
  +                print( x[[ n ]] )
  +                cat( "saved:\n" )
  +                print( xSaved[[ n ]] )
  +             }
  +          }
  +       }
  +    }
  +    
  +    for( mName in c( "Coef", "CoefNoLs", "Vcov", "VcovNoLs",
  +          "CoefSum", "CoefSumNoLs", "LogLik", "Nobs", "ExtractAIC" ) ) {
  +       cat( "   comparing method '", mName, "' ...", sep = "" )
  +       tol <- 5e-3
  +       if( mName == "Coef" ) {
  +          xm <- coef( x )
  +          tol <- 5e-2
  +       } else if( mName == "CoefNoLs" ) {
  +          xm <- coef( x, logSigma = FALSE )
  +          tol <- 5e-2
  +       } else if( mName == "Vcov" ) {
  +          xm <- vcov( x )
  +          tol <- 5e-1
  +       } else if( mName == "VcovNoLs" ) {
  +          xm <- vcov( x, logSigma = FALSE )
  +          tol <- 5e-1
  +       } else if( mName == "CoefSum" ) {
  +          xm <- coef( summary( x ) )
  +          tol <- 5e-2
  +       } else if( mName == "CoefSumNoLs" ) {
  +          xm <- coef( summary( x ), logSigma = FALSE )
  +          tol <- 5e-2
  +       } else if( mName == "LogLik" ) {
  +          xm <- logLik( x )
  +       } else if( mName == "Nobs" ) {
  +          xm <- nobs( x )
  +       } else if( mName == "ExtractAIC" ) {
  +          xm <- extractAIC( x )
  +       } else {
  +          stop( "unknown value of 'mName': ", mName )
  +       }
  +       methodObjName <- paste0( objName, mName )
  +       if( !exists( methodObjName, envir = saved, inherits = FALSE ) ) {
  +          cat( " previously saved object not found\n" )
  +       } else {
  +          xmSaved <- get( methodObjName, envir = saved, inherits = FALSE )
  +          testRes <- all.equal( xm, xmSaved, tol = tol )
  +          if( isTRUE( testRes ) ) {
  +             cat( " OK\n" )
  +          } else {
  +             cat( " different\n" )
  +             print( testRes )
  +             cat( "new:\n" )
  +             print( xm )
  +             cat( "saved:\n" )
  +             print( xmSaved )
  +          }
  +       }
  +       # assign to parent frame so that it will be included in the saved workspace
  +       assign( methodObjName, xm, envir = parent.frame() )
  +    }
  +       
  +    if( what %in% c( "print", "methods", "all" ) ) {
  +       print( x, digits = 1 )
  +       print( x, logSigma = FALSE , digits = 1 )
  +       print( maxLik:::summary.maxLik( x ), digits = 1 )
  +       print( summary( x ), digits = 1 )
  +       print( summary( x ), logSigma = FALSE , digits = 1 )
  +    }
  +    if( what %in% c( "methods", "all" ) ) {
  +       print( round( coef( x ), 2 ) )
  +       print( round( coef( x, logSigma = FALSE ), 2 ) )
  +       print( round( vcov( x ), 2 ) )
  +       print( round( vcov( x, logSigma = FALSE ), 2 ) )
  +       print( round( coef( summary( x ) ), 2 ) )
  +       print( round( coef( summary( x ), logSigma = FALSE ), 2 ) )
  +       try( margEff( x ) )
  +       print( logLik( x ) )
  +       print( nobs( x ) )
  +       print( extractAIC( x ) )
  +    }
  +    
  +    if( what == "all" ) {
  +       for( n in names( x ) ) {
  +          cat( "$", n, "\n", sep = "" )
  +          if( n %in% c( "estimate", "gradientObs" ) ) {
  +             print( round( x[[ n ]], 2 ) )
  +          } else if( n %in% c( "hessian" ) ) {
  +             print( round( x[[ n ]], 1 ) )
  +          } else if( n %in% c( "gradient" ) ) {
  +          } else if( ! n %in% c( "last.step" ) ) {
  +             print( x[[ n ]] )
  +          }
  +          cat( "\n" )
  +       }
  +       cat( "class\n" )
  +       print( class( x ) )
  +    }
  + }
  > 
  > nId <- 15
  > nTime <- 4
  > 
  > set.seed( 123 )
  > pData <- data.frame(
  +    id = rep( paste( "F", 1:nId, sep = "_" ), each = nTime ),
  +    time = rep( 1980 + 1:nTime, nId ) )
  > pData$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 )
  > nData <- pData # save data set without information on panel structure
  > pData <- pdata.frame( pData, c( "id", "time" ) )
  > 
  > 
  > ## Newton-Raphson method
  > randEff <- censReg( y ~ x1 + x2, data = pData )
  > printAll( "randEff" )
  Comparing new object 'randEff' to previously saved object...
     comparing component 'maximum' ... OK
     comparing component 'estimate' ... OK
     comparing component 'hessian' ... OK
     comparing component 'fixed' ... OK
     comparing component 'type' ... OK
     comparing component 'gradientObs' ... OK
     comparing component 'control' ... different
   [1] "Attributes: < Names: 20 string mismatches >"                                      
   [2] "Attributes: < Length mismatch: comparison on first 20 components >"               
   [3] "Attributes: < Component 1: Modes: numeric, character >"                           
   [4] "Attributes: < Component 1: Attributes: < target is NULL, current is list > >"     
   [5] "Attributes: < Component 1: target is numeric, current is character >"             
   [6] "Attributes: < Component 2: Mean relative difference: 1 >"                         
   [7] "Attributes: < Component 3: Mean absolute difference: 150 >"                       
   [8] "Attributes: < Component 4: Modes of target, current: name, numeric >"             
   [9] "Attributes: < Component 4: target, current do not match when deparsed >"          
  [10] "Attributes: < Component 5: Modes of target, current: name, numeric >"             
  [11] "Attributes: < Component 5: target, current do not match when deparsed >"          
  [12] "Attributes: < Component 6: Mean relative difference: 19 >"                        
  [13] "Attributes: < Component 7: Modes of target, current: name, numeric >"             
  [14] "Attributes: < Component 7: target, current do not match when deparsed >"          
  [15] "Attributes: < Component 9: Modes: character, numeric >"                           
  [16] "Attributes: < Component 9: Attributes: < Modes: list, NULL > >"                   
  [17] "Attributes: < Component 9: Attributes: < Lengths: 1, 0 > >"                       
  [18] "Attributes: < Component 9: Attributes: < names for target but not for current > >"
  [19] "Attributes: < Component 9: Attributes: < current is not list-like > >"            
  [20] "Attributes: < Component 9: target is character, current is numeric >"             
  [21] "Attributes: < Component 10: Mean absolute difference: 2 >"                        
  [22] "Attributes: < Component 11: Mean relative difference: 1 >"                        
  [23] "Attributes: < Component 12: Modes: numeric, character >"                          
  [24] "Attributes: < Component 12: target is numeric, current is character >"            
  [25] "Attributes: < Component 13: Mean relative difference: 1 >"                        
  [26] "Attributes: < Component 14: Mean relative difference: 1 >"                        
  [27] "Attributes: < Component 15: Modes: numeric, name >"                               
  [28] "Attributes: < Component 15: target is numeric, current is name >"                 
  [29] "Attributes: < Component 16: Mean relative difference: 16.571 >"                   
  [30] "Attributes: < Component 17: Mean relative difference: 0.5 >"                      
  [31] "Attributes: < Component 18: Mean relative difference: 9 >"                        
  [32] "Attributes: < Component 19: Mean relative difference: 1 >"                        
  [33] "Attributes: < Component 20: Mean relative difference: 1 >"                        
  new:
  A 'MaxControl' object with slots:
  tol = 1e-08 
  reltol = 1.4901e-08 
  gradtol = 1e-06 
  steptol = 1e-10 
  lambdatol = 1e-06 
  qrtol = 1e-10 
  qac = stephalving 
  marquardt_lambda0 = 0.01 
  marquardt_lambdaStep = 2 
  marquardt_maxLambda = 1e+12 
  nm_alpha = 1 
  nm_beta = 0.5 
  nm_gamma = 2 
  sann_cand = <default Gaussian Markov kernel>
  sann_temp = 10 
  sann_tmax = 10 
  sann_randomSeed = 123 
  SGA_momentum = 0 
  Adam_momentum1 = 0.9 
  Adam_momentum2 = 0.999 
  SG_patience = 
  SG_patienceStep = 1 
  SG_learningRate = 0.1 
  SG_batchSize = 
  SG_clip = 
  iterlim = 150 
  max.rows = 20 
  max.cols = 7 
  printLevel = 0 
  storeValues = FALSE 
  storeParameters = FALSE 
  saved:
  A 'MaxControl' object with slots:
  tol = 1e-08 
  reltol = 1.4901e-08 
  gradtol = 1e-06 
  steptol = 1e-10 
  lambdatol = 1e-06 
  qrtol = 1e-10 
  qac = stephalving 
  marquardt_lambda0 = 0.01 
  marquardt_lambdaStep = 2 
  marquardt_maxLambda = 1e+12 
  nm_alpha = 1 
  nm_beta = 0.5 
  nm_gamma = 2 
  sann_cand = <default Gaussian Markov kernel>
  sann_temp = 10 
  sann_tmax = 10 
  sann_randomSeed = 123 
  Error in slot(object, s) : 
    no slot of name "SGA_momentum" for this object of class "MaxControl"
  Calls: printAll ... print.default -> <Anonymous> -> <Anonymous> -> cat -> slot
  Execution halted