* using log directory 'd:/RCompile/CRANincoming/R-devel/glmMisrep.Rcheck'
* using R version 4.4.0 RC (2024-04-16 r86435 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 13.2.0
    GNU Fortran (GCC) 13.2.0
* 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 '0.1.0'
* package encoding: UTF-8
* checking CRAN incoming feasibility ... WARNING
Maintainer: 'Patrick Rafael <pbr2608@vt.edu>'

Insufficient package version (submitted: 0.1.0, existing: 0.1.0)
* 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 for future file timestamps ... 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 code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... 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 ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... 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 ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking examples ... [10s] OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ... ERROR
  Running 'LN-testing.R'
  Running 'NB-testing.R'
  Running 'Norm-testing.R'
  Running 'Pois-testing.R'
  Running 'gamma-testing.R'
Running the tests in 'tests/Pois-testing.R' failed.
Complete output:
  > require(glmMisrep)
  Loading required package: glmMisrep
  > 
  > data <- data.frame( Y = c(0,   0,   2,   0,   3,   0,  36,   0,   2,   1,   0,   2,   6,   9,   0,   0,   0,   0,   7,   1,   1,   2,  50,   4,   0,   0,   0,   1,
  +                           0,   0,   0,   3,   0,   0,   1,   0,   1,   3, 176,   0,   0,   0,   0,   0,   2, 286,   0,   0,   0,  18),
  +                     X1 = c(0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1),
  +                     X2 = c(1.71870212, -0.55840901,  1.22589915,  0.53000107,  0.62571132,  0.02873955,  0.30989954,  1.35514993,  0.15587503,
  +                              0.27987513,  0.48892178,  0.35218767,  0.52382778,  1.58126751, -0.07855081, -0.57128802, -0.92500953, -2.48543328,
  +                              0.03810910,  0.39929906, -0.54854763, -0.10505694,  0.45120734,  0.32295222, -0.68595918, -0.66892486,  1.72253431,
  +                             -0.28425276, -0.67719912, -0.39644260, -0.16843500,  0.90540261, -1.38574804,  0.14456841,  0.44142810, -1.89442541,
  +                             -0.65961894,  2.13148776,  1.72410805, -1.60207312, -1.09525034, -1.31327168, -0.43378445,  1.33644956,  1.28938359,
  +                              0.90232362, -0.94112768, -0.61851917,  0.37033085, -0.47019541),
  +                     X3 = c(0.6882029, 0.9934165, 0.9173388, 0.9406660, 0.5130041, 0.8590187, 0.4468488, 0.4186652, 0.5098278, 0.3339481, 0.6922477,
  +                             0.6793977, 0.4983724, 0.6079911, 0.7763041, 0.8529067, 0.8287771, 0.9125900, 0.4802076, 0.8981448, 0.3570093, 0.9209584,
  +                             0.4353817, 0.9426418, 0.9550002, 0.4869851, 0.9560156, 0.8247537, 0.1939687, 0.6103839, 0.7721900, 0.5980044, 0.8683831,
  +                             0.7004518, 0.8577210, 0.3576712, 0.9540088, 0.9880046, 0.4304899, 0.7416618, 0.6656063, 0.8920356, 0.6097593, 0.8008748,
  +                             0.8110432, 0.5967969, 0.6983106, 0.9471680, 0.9929186, 0.9059200),
  +                     Sex = c("Female", "Male",   "Male",   "Female", "Male",   "Female", "Male",   "Male",   "Female", "Female", "Male",   "Male",
  +                              "Male",   "Female", "Male",   "Male",   "Female", "Female", "Male",   "Female", "Female", "Male",   "Female", "Male",
  +                              "Male",   "Male",   "Male",   "Female", "Female", "Female", "Male",   "Male",   "Male",   "Male",   "Female", "Female",
  +                              "Female", "Female", "Male",   "Male",   "Male",   "Male",   "Male",   "Male",   "Female", "Male",   "Male",   "Female",
  +                              "Female", "Male"),
  +                     Race = c("Black", "White", "White", "White", "White", "White", "Black", "Black", "Black", "Black", "Black", "Black", "White", "Other",
  +                               "Black", "White", "Black", "White", "Other", "Black", "White", "Other", "Other", "White", "White", "White", "Black", "Other",
  +                               "White", "Other", "White", "Black", "Other", "Black", "Other", "Other", "Other", "Black", "Other", "Black", "White", "Other",
  +                               "Black", "White", "Black", "Other", "White", "Black", "Black", "Other"),
  +                     V_star = c(0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0))
  > 
  > data$Race <- as.factor(data$Race)
  > data$Sex <- as.factor(data$Sex)
  > 
  > t1 <- tryCatch(poisRegMisrepEM(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$message == "object 'y' not found"
  + )
  > 
  > 
  > t2 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_Star",
  +                                 data = data,
  +                                 lambda = c(0.6,0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Argument to 'v_star' is misspelled
  > stopifnot(
  +   t2$message == "variable V_Star not present in dataframe"
  + )
  > 
  > 
  > data$V_star <- ifelse(data$V_star == 1, yes = "yes", no = "no")
  > 
  > t3 <- tryCatch(poisRegMisrepEM(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(poisRegMisrepEM(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(poisRegMisrepEM(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$V_star == 1, yes = 1, no = 0)
  > 
  > t6 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.49, 0.52),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Inappropriately specified lambda argument
  > stopifnot(
  +   t6$message == "Lambda vector must sum to one"
  + )
  > 
  > 
  > t7 <- tryCatch(poisRegMisrepEM(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$X2*0.3
  > 
  > t8 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + X4 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.6, 0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Linearly dependent covariates/degenerate design matrix
  > stopifnot(
  +   t8$message == "Linear dependencies exist in the covariates"
  + )
  > 
  > 
  > t9 <- tryCatch(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race,
  +                                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 absent from formula
  > stopifnot(
  +   t9$message == "v_star variable must be specified in 'formula'"
  + )
  > 
  > 
  > 
  > # EM algorithm should fail to converge within the specified number of attempts
  > t10 <- tryCatch(
  +   capture.output(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                   v_star = "V_star",
  +                                   data = data,
  +                                   lambda = c(0.6, 0.4),
  +                                   epsilon = 1e-08,
  +                                   maxit = 2,
  +                                   maxrestarts = 1)),
  +   error = function(x) x
  + )
  > 
  > stopifnot(
  +   t10$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 <-  poisRegMisrepEM(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 = 16,
  +                            maxrestarts = 4, verb = TRUE),
  +   type = "message"
  + )
  > 
  > stopifnot(
  +   any(msg == "Warning: Failed to converge. Restarting with new mixing proportions")
  + )
  > 
  > 
  > msg <- capture.output(
  +  t12 <-  poisRegMisrepEM(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),
  +  type = "message"
  +   )
  > 
  > # Output validation;
  > 
  > # Output should be a list
  > stopifnot(
  +   is.list(t12)
  + )
  > 
  > # With 14 elements
  > stopifnot(
  +   length(t12) == 14
  + )
  > 
  > # Fisher information matrix should be symmetric
  > stopifnot(
  +   isSymmetric(t12$cov.estimates)
  + )
  > 
  > 
  > # The returned list should have elements with the following names
  > # and types
  > stopifnot(
  +   all.equal(lapply(t12, class),
  +             lapply(list(y = 0.1, lambda = 0.2, params = 0.3, loglik = 0.4,
  +                         posterior = 0.5, all.loglik = 0.6, cov.estimates = matrix(data = c(1,2,3,4), 2, 2),
  +                         std.error = 0.7, z.values = 0.8, p.values = 0.9, ICs = 1.0, ft = "*",
  +                         formula = y ~ x, v_star_name = "v*" ), class) )
  + )
  > 
  > 
  > # Verifying the function can correctly calculate things;
  > stopifnot(
  +   all.equal(t12$lambda,  0.2140361, tolerance = 3e-7 )
  + )
  > 
  > stopifnot(
  +   all.equal(as.numeric(t12$params), c(-1.1486407,  1.9813423,  1.0606001, -4.0278727,  1.8886687,  2.1052795, -0.1815366,  2.2582766), tolerance = 3e-7 )
  + )
  > 
  > stopifnot(
  +   all.equal( t12$loglik, -77.17481, tolerance = 3e-7 )
  + )
  > 
  > stopifnot(
  +   all.equal( t12$posterior, c(9.131063e-01,  9.167867e-01,  9.987564e-01,  8.007038e-01,  1.000000e+00,  7.980578e-01,  1.761218e-22,  9.999998e-01,
  +                               5.707215e-02,  4.986677e-01,  9.591568e-01,  9.998818e-01,  1.596141e-04,  1.000000e+00,  8.834283e-01,  8.269226e-01,
  +                               8.267814e-01,  7.909421e-01,  9.995369e-01,  3.002507e-01,  7.724813e-01,  5.033734e-01,  1.413033e-29,  7.033365e-04,
  +                               9.184187e-01,  9.118807e-01,  9.752457e-01,  4.103920e-01,  8.589374e-01,  9.278243e-01,  8.652112e-01,  1.000000e+00,
  +                               9.998403e-01,  9.999676e-01,  5.418147e-01,  8.817133e-01,  3.273919e-01,  1.253503e-01, 2.099810e-102,  8.124995e-01,
  +                               8.350465e-01,  9.998171e-01,  9.066149e-01,  1.000000e+00,  4.335937e-01, 2.097168e-171,  8.364673e-01,  7.911482e-01,
  +                               7.981111e-01,  1.769426e-10), tolerance = 3e-7 )
  + )
  > 
  > 
  > stopifnot(
  +   all.equal( t12$all.loglik, c(-91.21048, -77.54635, -77.24049, -77.18672, -77.17811, -77.17606, -77.17532, -77.17502, -77.17490, -77.17484, -77.17482,
  +                                -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481, -77.17481), tolerance = 3e-7  )
  + )
  > 
  > 
  > stopifnot(
  + all.equal(t12$cov.estimates,
  + matrix(data = c(0.0114492285, -0.0004087145,  0.0016883055, -0.001674646, -0.01493787,  0.004194090,  0.004620007,  0.003212934,  0.0008121915,
  +                -0.0004087145,  0.1376023242, -0.0103938967, -0.021763262, -0.10804979,  0.001850089, -0.001523425, -0.033947804, -0.0502985311,
  +                 0.0016883055, -0.0103938967,  0.0153809909,  0.005402047, -0.02063417,  0.003663544,  0.001754054,  0.008152043,  0.0006989913,
  +                -0.0016746456, -0.0217632624,  0.0054020474,  0.012549504,  0.02752248, -0.006829300, -0.008499404,  0.001601437,  0.0045586028,
  +                -0.0149378678, -0.1080497865, -0.0206341716,  0.027522475,  0.26797912, -0.040935476, -0.039438494, -0.023606980,  0.0234374153,
  +                 0.0041940900,  0.0018500887,  0.0036635440, -0.006829300, -0.04093548,  0.024832614,  0.009305140,  0.004723165, -0.0062878481,
  +                 0.0046200066, -0.0015234254,  0.0017540537, -0.008499404, -0.03943849,  0.009305140,  0.033156029,  0.023238618, -0.0090039233,
  +                 0.0032129343, -0.0339478042,  0.0081520434,  0.001601437, -0.02360698,  0.004723165,  0.023238618,  0.095149535,  0.0125163473,
  +                 0.0008121915, -0.0502985311,  0.0006989913,  0.004558603,  0.02343742, -0.006287848, -0.009003923,  0.012516347,  0.0479783934),
  +         ncol = 9, nrow = 9, byrow = TRUE, dimnames = list( c("lambda", names(t12$params)), c("lambda", names(t12$params)) ) ), tolerance = 3e-7
  + )
  + )
  > 
  > stopifnot(
  +   all.equal(as.numeric(t12$std.error), c(0.1070011, 0.3709479, 0.1240201, 0.1120246, 0.5176670, 0.1575837, 0.1820880, 0.3084632, 0.2190397), tolerance = 3e-7)
  + )
  > 
  > stopifnot(
  +   all.equal(as.numeric(t12$z.values), c(-3.0965016, 15.9759747,  9.4675669, -7.7808179, 11.9851800, 11.5618810, -0.5885196, 10.3098958), tolerance = 3e-7)
  + )
  > 
  > 
  > stopifnot(
  +   all.equal(as.numeric(t12$p.values), c(1.958188e-03, 1.878943e-57, 2.864376e-21, 7.205715e-15, 4.249202e-33, 6.428496e-31, 5.561836e-01, 6.357023e-25), tolerance = 3e-7)
  + )
  Error: as.numeric(t12$p.values) and c(0.001958188, 1.878943e-57, 2.864376e-21, 7.205715e-15, 4.249202e-33,  .... are not equal:
    Mean relative difference: 7.240945e-07
  Execution halted
* checking PDF version of manual ... [14s] OK
* checking HTML version of manual ... OK
* DONE
Status: 1 ERROR, 1 WARNING