* using log directory 'd:/Rcompile/CRANpkg/local/4.3/glmMisrep.Rcheck'
* using R version 4.3.3 (2024-02-29 ucrt)
* using platform: x86_64-w64-mingw32 (64-bit)
* R was compiled by
    gcc.exe (GCC) 12.3.0
    GNU Fortran (GCC) 12.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* checking for file 'glmMisrep/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'glmMisrep' version '0.1.1'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'glmMisrep' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... [0s] OK
* checking whether the package can be loaded with stated dependencies ... [0s] OK
* checking whether the package can be unloaded cleanly ... [0s] OK
* checking whether the namespace can be loaded with stated dependencies ... [0s] OK
* checking whether the namespace can be unloaded cleanly ... [0s] OK
* checking loading without being on the library search path ... [0s] OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [14s] OK
* checking Rd files ... [1s] OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... [0s] OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking examples ... [14s] OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ... [13s] ERROR
  Running 'LN-testing.R' [2s]
  Running 'NB-testing.R' [3s]
  Running 'Norm-testing.R' [1s]
  Running 'Pois-testing.R' [2s]
  Running 'gamma-testing.R' [4s]
Running the tests in 'tests/NB-testing.R' failed.
Complete output:
  > require(glmMisrep)
  Loading required package: glmMisrep
  > 
  > data <- data.frame( Y = c(0,  0,  0,  0,  0,  5,  1,  0,  0,  0,  4,  2,  3,  3, 29,  0,  0, 12,  0,  6,  0,  0,  0,  1,  3, 17,  0, 25,  0,  0,  2,  0,  0,  0,  0, 0,  0,
  +                           4,  2, 16,  0, 19,  0,  0,  2,  0,  0,  0,  0,  0),
  +                     X1 = c(0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
  +                            1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0),
  +                     X2 = c(0.315223940,  0.203016819, -0.046615492, -0.040681413,  0.617273624,  0.813577139,  0.434980275, -1.868117146,
  +                             -0.027052575,  0.228288655,  1.380955283,  0.257543733, -0.353957121,  0.818737902,  0.525026487, -0.506164644,
  +                             -0.949480806, -0.107608844,  0.363661818, -0.295835692,  0.110056356, -0.205246487, -0.285092978, -0.639260383,
  +                              1.576928404,  2.058907651,  0.823073891, -0.340826703, -1.324805151,  0.103545160,  0.497766445,  0.501280218,
  +                             -0.297921410,  1.056264736, -0.767076814, -0.212290592, -1.563216428,  1.026954673, -0.087597819,  1.334059105,
  +                              0.955057522,  1.823380042,  1.082772308, -2.223962848,  1.433183408, -0.004531971, -1.029210193,  0.532295431,
  +                             -0.930908683,  0.391930241),
  +                     X3 = c(0.74306067, 0.67595394, 0.96320251, 0.57668303, 0.81643636, 0.78038200, 0.49999395, 0.99676766, 0.97642715, 0.88478779,
  +                             0.53447733, 0.27009525, 0.64544670, 0.63666898, 0.14489153, 0.89359811, 0.70506987, 0.81595152, 0.78411313, 0.90301090,
  +                             0.48920726, 0.75707194, 0.98999417, 0.85091229, 0.29074286, 0.73562357, 0.97903729, 0.96906234, 0.84957226, 0.55937779,
  +                             0.49149558, 0.83789430, 0.66902416, 0.44173571, 0.81911265, 0.64182433, 0.34363554, 0.78838686, 0.78557154, 0.83241653,
  +                             0.68691255, 0.20317013, 0.78619988, 0.09229911, 0.52779899, 0.79314940, 0.95612951, 0.95234203, 0.54259470, 0.81656990),
  +                     Sex = c("Male",   "Male",   "Female", "Male",   "Male",   "Female", "Female", "Female", "Female", "Male",   "Male",   "Female",
  +                              "Male",   "Female", "Male",   "Female", "Female", "Male",   "Female", "Male",   "Male",   "Female", "Male",   "Female",
  +                              "Female", "Male",   "Male",   "Male",   "Male",   "Female", "Male",   "Female", "Female", "Female", "Male",   "Female",
  +                              "Male",   "Female", "Male",   "Female", "Female", "Male",   "Female", "Male",   "Male",   "Female", "Female", "Female",
  +                              "Female", "Male"),
  +                     Race = c("Black", "Black", "White", "White", "White", "White", "Black", "Other", "Other", "Other", "Black", "Other", "Other", "Other", "Other", "Other", "White", "Other",
  +                               "White", "Black", "White", "Other", "White", "White", "Other", "Other", "Black", "Other", "Other", "Black", "Black", "Black", "Other", "Black", "Black", "Other",
  +                               "White", "Other", "White", "White", "Other", "Other", "Other", "White", "White", "Black", "Black", "Black", "Other", "White"),
  +                     V_star = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
  > 
  > data$Race <- as.factor(data$Race)
  > data$Sex <- as.factor(data$Sex)
  > 
  > t1 <- tryCatch(nbRegMisrepEM(formula = y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.6,0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # The response above is inappropriately specified (y, not Y)
  > stopifnot(
  +   t1$message == "object 'y' not found"
  + )
  > 
  > 
  > t2 <- tryCatch(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_Star",
  +                                 data = data,
  +                                 lambda = c(0.6,0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Argument to 'v_star' is misspelled
  > stopifnot(
  +   t2$message == "variable V_Star not present in dataframe"
  + )
  > 
  > 
  > data$V_star <- ifelse(data$V_star == 1, yes = "yes", no = "no")
  > 
  > t3 <- tryCatch(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.6,0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > 
  > # v* variable is type character (yes and no)
  > stopifnot(
  +   t3$message == "v_star variable must be of class 'factor' or 'numeric'"
  + )
  > 
  > data$V_star <- ifelse(data$V_star == "yes", yes = 1, no = 0)
  > 
  > 
  > data$V_star[10] <- -1
  > 
  > 
  > t4 <- tryCatch(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.6,0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # v* variable must be binary
  > stopifnot(
  +   t4$message == "v_star variable must contain two unique values"
  + )
  > 
  > data$V_star[10] <- 0
  > 
  > 
  > data$V_star <- ifelse(data$V_star == 1, yes = 1, no = 2)
  > 
  > t5 <- tryCatch(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.6,0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # v* must be binary, but more specifically 0/1;
  > stopifnot(
  +   t5$message == "v_star variable must be coded with ones and zeroes"
  + )
  > 
  > 
  > 
  > data$V_star <- ifelse(data$V_star == 1, yes = 1, no = 0)
  > 
  > t6 <- tryCatch(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.49, 0.52),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Inappropriately specified lambda argument
  > stopifnot(
  +   t6$message == "Lambda vector must sum to one"
  + )
  > 
  > 
  > t7 <- tryCatch(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(1/3, 1/3, 1/3),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Inappropriately specified lambda argument
  > stopifnot(
  +   t7$message == "Lambda vector must contain two elements"
  + )
  > 
  > 
  > 
  > data$X4 <- data$X2*0.3
  > 
  > t8 <- tryCatch(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + X4 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.6, 0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Linearly dependent covariates/degenerate design matrix
  > stopifnot(
  +   t8$message == "Linear dependencies exist in the covariates"
  + )
  > 
  > # This is only to make sure the glm.nb() function can fit a model
  > # without throwing warnings messages/failing to converge. For purposes
  > # of testing our error handling, this should work.
  > 
  > data$VS <- data$V_star
  > 
  > t9 <- tryCatch(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + VS,
  +                              v_star = "V_star",
  +                              data = data,
  +                              lambda = c(0.6, 0.4),
  +                              epsilon = 1e-08,
  +                              maxit = 10000,
  +                              maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # V_star variable is absent from formula ragument
  > stopifnot(
  +   t9$message == "v_star variable must be specified in 'formula'"
  + )
  > 
  > 
  > 
  > # EM algorithm should fail to converge within the specified number of attempts
  > t10 <- tryCatch(
  +   capture.output(nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                   v_star = "V_star",
  +                                   data = data,
  +                                   lambda = c(0.6, 0.4),
  +                                   epsilon = 1e-08,
  +                                   maxit = 2,
  +                                   maxrestarts = 1)),
  +   error = function(x) x
  + )
  > 
  > stopifnot(
  +   t10$message == "NOT CONVERGENT! Failed to converge after 1 attempts"
  + )
  > 
  > # On the first attempt, fails to converge, and restarts with new mixing props.
  > # Succeeds on the second attempt.
  > msg <- capture.output(
  +   t11 <-  nbRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                            v_star = "V_star",
  +                            data = data,
  +                            lambda = c(0.6, 0.4),
  +                            epsilon = 1e-08,
  +                            maxit = 9,
  +                            maxrestarts = 4, verb = TRUE),
  +   type = "message"
  + )
  Execution halted
* checking PDF version of manual ... [17s] OK
* checking HTML version of manual ... [2s] OK
* DONE
Status: 1 ERROR