* using log directory 'd:/RCompile/CRANincoming/R-devel/glmMisrep.Rcheck'
* using R Under development (unstable) (2024-03-14 r86117 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 ... NOTE
Maintainer: 'Patrick Rafael <pbr2608@vt.edu>'

New submission

Possibly misspelled words in DESCRIPTION:
  Xia (7:813)
  al (7:821)
  et (7:817)
  glmMisrep (7:18)
* 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 R 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 ... NOTE
Mismatches for methods registered for non-generic:
predict:
  function(object, ...)
predict.misrepEM:
  function(object, newdata)
See section 'Registering S3 methods' in the 'Writing R Extensions'
manual.
* 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 ... 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$message == "v_star variable must be specified in 'formula'"
  + )
  > 
  > 
  > 
  > # EM algorithm should fail to converge within the specified number of attempts
  > #  (four)
  > t10 <- tryCatch(
  +   capture.output(poisRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                   v_star = "V_star",
  +                                   data = data,
  +                                   lambda = c(0.6, 0.4),
  +                                   epsilon = 1e-08,
  +                                   maxit = 3,
  +                                   maxrestarts = 4)),
  +   error = function(x) x
  + )
  > 
  > stopifnot(
  +   t10$message == "NOT CONVERGENT! Failed to converge after 4 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)
  + )
  > 
  > stopifnot(
  +   msg[1] == "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)
  +   )
  > 
  > # 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 = 2e-7 )
  + )
  > 
  > stopifnot(
  +   all.equal(as.numeric(t12$params), c(-1.1486407,  1.9813423,  1.0606001, -4.0278727,  1.8886687,  2.1052795, -0.1815366,  2.2582766), tolerance = 2e-8 )
  + )
  > 
  > stopifnot(
  +   all.equal( t12$loglik, -77.17481, tolerance = 4e-8 )
  + )
  > 
  > stopifnot(
  +   all.equal( t12$posterior, c(9.131063e-01,  9.167867e-01,  9.987564e-01,  8.007038e-01,  1.000000e+00,  7.980578e-01,  1.761218e-22,  9.999998e-01,
  +                               5.707215e-02,  4.986677e-01,  9.591568e-01,  9.998818e-01,  1.596141e-04,  1.000000e+00,  8.834283e-01,  8.269226e-01,
  +                               8.267814e-01,  7.909421e-01,  9.995369e-01,  3.002507e-01,  7.724813e-01,  5.033734e-01,  1.413033e-29,  7.033365e-04,
  +                               9.184187e-01,  9.118807e-01,  9.752457e-01,  4.103920e-01,  8.589374e-01,  9.278243e-01,  8.652112e-01,  1.000000e+00,
  +                               9.998403e-01,  9.999676e-01,  5.418147e-01,  8.817133e-01,  3.273919e-01,  1.253503e-01, 2.099810e-102,  8.124995e-01,
  +                               8.350465e-01,  9.998171e-01,  9.066149e-01,  1.000000e+00,  4.335937e-01, 2.097168e-171,  8.364673e-01,  7.911482e-01,
  +                               7.981111e-01,  1.769426e-10), tolerance = 3e-8 )
  + )
  > 
  > 
  > stopifnot(
  +   all.equal( t12$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 = 3.2e-8  )
  + )
  > 
  > 
  > 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$params)) ) ), tolerance = 2.5e-8
  + )
  + )
  Error: t12$cov.estimates and matrix(data = c(0.0114492285, -0.0004087145, 0.0016883055, -0.001674646,  .... are not equal:
    Mean relative difference: 2.99669e-08
  Execution halted
Running the tests in 'tests/gamma-testing.R' failed.
Complete output:
  > require(glmMisrep)
  Loading required package: glmMisrep
  > 
  > data <- data.frame( Y = c(45.857704, 26.966636, 1058.634073, 623.319053, 1087.177701, 19.323827, 86.047383, 606.742023,
  +                       1647.937035, 266.180140, 157.192319, 281.382229, 648.672212, 140.771360, 102.904828, 380.052888,
  +                         22.064627, 208.741717, 67.074619, 174.856091, 31.569650, 626.408259, 993.658540, 189.571355,
  +                        374.603446, 1802.252002, 543.301728, 158.015234, 104.378652, 582.149021, 52.249839, 45.702009,
  +                      11806.824361, 10053.322011,  1.087628, 55.884707, 271.186925, 178.080733, 563.668826, 1596.744067,
  +                          8.947570, 398.868445, 39.580796, 1293.273355, 23.025039, 184.609120, 238.859666, 169.540935,
  +                       775.025538, 4.616906),
  +                     X1 = c(1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,
  +                            0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0),
  +                     X2 = c(-1.072049676,  0.727369558,  1.420449831,  0.919953091,  0.274830496,  0.546589583,  0.766522073,  0.102791747,
  +                              0.326183969,  1.929334793, -0.843138417, -0.064225873,  0.256683194,  0.625580034,  0.469241089,  0.255148171,
  +                             -0.235947534,  1.816474277, -1.692768826, -1.068503018, -0.137072147,  0.772976621, -0.719329599, -1.581414358,
  +                              0.049968814,  0.184292275, -1.321527190,  0.593936587, -1.441344834,  0.493651003, -0.468055987, -0.312559805,
  +                             -0.196035448, -0.541855456,  1.913445157,  0.403104413, -1.503943713,  0.401514018,  1.613023384, -1.063038013,
  +                              0.414808888,  0.417654550,  0.410841245,  0.218083137, -0.462372658, -0.908533492,  0.157572760,  0.750073708,
  +                              0.005803132,  0.152076943),
  +                     X3 = c(0.48973164, 0.69612108, 0.81936453, 0.83451060, 0.97515909, 0.88015868, 0.88890374, 0.37991780, 0.89681143, 0.99601041,
  +                             0.67524620, 0.92379599, 0.85120739, 0.41159392, 0.53689827, 0.77538594, 0.19803702, 0.95549912, 0.14601890, 0.80506128,
  +                             0.12918729, 0.89932864, 0.74272902, 0.47642162, 0.29003290, 0.61241891, 0.62205612, 0.77209866, 0.83853287, 0.80933236,
  +                             0.42349111, 0.81845100, 0.95771341, 0.73298222, 0.02518371, 0.63923492, 0.54736138, 0.57766185, 0.64674272, 0.54556027,
  +                             0.76556145, 0.74711758, 0.74089894, 0.99431826, 0.49083626, 0.27663899, 0.85629864, 0.71527708, 0.62011519, 0.25683099),
  +                     Sex = c("Female", "Female", "Male",   "Male",   "Female", "Female", "Female", "Female", "Female", "Female", "Female", "Male",
  +                             "Female", "Male",   "Male",   "Female", "Female", "Male",   "Male",   "Female", "Female", "Female", "Male",   "Male",
  +                             "Male",   "Female", "Male",   "Female", "Male",   "Female", "Male",   "Male",   "Male",   "Male",   "Female", "Female",
  +                             "Female", "Female", "Male",   "Male",   "Female", "Female", "Male",   "Female", "Male",   "Male",   "Male",   "Female",
  +                             "Male",   "Female"),
  +                     Race = c("Other", "White", "Black", "White", "White", "Other", "White", "White", "Other", "Other", "Other", "Black", "White", "Other",
  +                               "Black", "Black", "Black", "Black", "White", "Black", "White", "White", "Black", "White", "Black", "Black", "Black", "Other",
  +                               "Other", "Black", "White", "Other", "Black", "White", "Other", "White", "White", "Black", "Black", "White", "Other", "White",
  +                               "Black", "Other", "White", "White", "White", "White", "Black", "Black"),
  +                     V_star = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 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(gammaRegMisrepEM(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 (should be Y, not y)
  > stopifnot(
  +   t1$message == "object 'y' not found"
  + )
  > 
  > 
  > t2 <- tryCatch(gammaRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_Star",
  +                                 data = data,
  +                                 lambda = c(0.6,0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Argument to 'v_star' is misspelled
  > stopifnot(
  +   t2$message == "variable V_Star not present in dataframe"
  + )
  > 
  > 
  > data$V_star <- ifelse(data$V_star == 1, yes = "yes", no = "no")
  > 
  > t3 <- tryCatch(gammaRegMisrepEM(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(gammaRegMisrepEM(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(gammaRegMisrepEM(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$V_star == 1, yes = 1, no = 0)
  > 
  > t6 <- tryCatch(gammaRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.49, 0.52),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Inappropriately specified lambda argument
  > stopifnot(
  +   t6$message == "Lambda vector must sum to one"
  + )
  > 
  > 
  > t7 <- tryCatch(gammaRegMisrepEM(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$X2*0.3
  > 
  > t8 <- tryCatch(gammaRegMisrepEM(formula = Y ~ X1 + X2 + X3 + X4 + Sex + Race + V_star,
  +                                 v_star = "V_star",
  +                                 data = data,
  +                                 lambda = c(0.6, 0.4),
  +                                 epsilon = 1e-08,
  +                                 maxit = 10000,
  +                                 maxrestarts = 20),
  +                error = function(x) x )
  > 
  > # Linearly dependent covariates/degenerate design matrix
  > stopifnot(
  +   t8$message == "Linear dependencies exist in the covariates"
  + )
  > 
  > t9 <- tryCatch(gammaRegMisrepEM(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 is not present in formula argument
  > stopifnot(
  +   t9$message == "v_star variable must be specified in 'formula'"
  + )
  > 
  > # EM algorithm should fail to converge within the specified number of attempts
  > #  (four)
  > t10 <- tryCatch(
  +   capture.output(gammaRegMisrepEM(formula = Y ~ X1 + X2 + X3 + Sex + Race + V_star,
  +                                     v_star = "V_star",
  +                                     data = data,
  +                                     lambda = c(0.6, 0.4),
  +                                     epsilon = 1e-08,
  +                                     maxit = 3,
  +                                     maxrestarts = 4)),
  +   error = function(x) x
  + )
  > 
  > stopifnot(
  +   t10$message == "NOT CONVERGENT! Failed to converge after 4 attempts"
  + )
  > 
  > 
  > # On the first attempt, fails to converge, and restarts with new mixing props.
  > # Succeeds on the second attempt.
  > msg <- capture.output(
  +  t11 <-  gammaRegMisrepEM(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 = 30,
  +                                 maxrestarts = 4)
  +   )
  > 
  > stopifnot(
  +   msg[1] == "Failed to converge. Restarting with new mixing proportions"
  + )
  > 
  > 
  > # This should succeed;
  > msg <- capture.output(
  +   t12 <-  gammaRegMisrepEM(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)
  +                       )
  > 
  > 
  > 
  > # Output validation;
  > 
  > # Outpute should be a list
  > stopifnot(
  +   is.list(t12)
  + )
  > 
  > # With 14 elements
  > stopifnot(
  +   length(t12) == 14
  + )
  > 
  > # Fisher information matrix should be symmetric
  > stopifnot(
  +   isSymmetric(t12$cov.estimates)
  + )
  > 
  > 
  > # The returned list should have elements with the following names
  > # and types
  > stopifnot(
  +   all.equal(lapply(t12, class),
  +             lapply(list(y = 0.1, lambda = 0.2, params = 0.3, loglik = 0.4,
  +                         posterior = 0.5, all.loglik = 0.6, cov.estimates = matrix(data = c(1,2,3,4), 2, 2),
  +                         std.error = 0.7, t.values = 0.8, p.values = 0.9, ICs = 1.0, ft = "*",
  +                         formula = y ~ x, v_star_name = "v*" ), class) )
  + )
  > 
  > 
  > # Verifying the function can correctly calculate things;
  > stopifnot(
  +   all.equal(t12$lambda, 0.1079016 , tolerance = 1.4e-7)
  + )
  > 
  > stopifnot(
  +   all.equal(as.numeric(t12$params), c(6.14834270,  1.11254217,  1.85164785, -0.05971879,  3.68687601,  1.03378987, -1.03786486,  0.01177176,  2.11841547) )
  + )
  > 
  > stopifnot(
  +   all.equal( t12$loglik, -303.1538, tolerance = 4e-8 )
  + )
  > 
  > stopifnot(
  +   all.equal( t12$posterior, c(9.999299e-01, 9.999881e-01, 9.999295e-01, 2.103412e-02, 9.988509e-01, 9.999868e-01, 9.998953e-01, 3.558117e-12,
  +                               3.158194e-15, 1.047717e-10, 3.497216e-21, 9.999045e-01, 6.002373e-16, 5.375916e-19, 9.972457e-01, 3.068774e-11,
  +                               9.999950e-01, 9.999795e-01, 9.999914e-01, 9.999972e-01, 5.394296e-09, 2.489884e-12, 9.998797e-01, 9.999950e-01,
  +                               1.426431e-29, 2.989857e-17, 9.999592e-01, 9.995238e-01, 9.993700e-01, 1.407886e-17, 9.997910e-01, 9.999866e-01,
  +                               6.992912e-09, 5.881790e-22, 9.999294e-01, 9.963445e-01, 9.976430e-01, 9.998867e-01, 9.999221e-01, 1.061977e-02,
  +                               9.999959e-01, 9.996503e-01, 9.999986e-01, 1.269408e-05, 9.999974e-01, 9.998703e-01, 9.998375e-01, 9.999912e-01,
  +                               9.993344e-01, 9.999934e-01), tolerance = 2.8e-8 )
  + )
  > 
  > 
  > stopifnot(
  +   all.equal( t12$all.loglik, c(-323.9350, -307.5493, -305.8435, -303.7539, -303.4699, -303.4653, -303.4630, -303.4617, -303.4607, -303.4600,
  +                                -303.4595, -303.4590, -303.4586, -303.4581, -303.4577, -303.4573, -303.4568, -303.4562, -303.4555, -303.4546,
  +                                -303.4534, -303.4517, -303.4491, -303.4447, -303.4369, -303.4219, -303.3919, -303.3337, -303.2463, -303.1768,
  +                                -303.1562, -303.1540, -303.1538, -303.1538, -303.1538, -303.1538, -303.1538), tolerance = 9e-8  )
  + )
  > 
  > 
  > stopifnot(
  + all.equal(t12$cov.estimates,
  + matrix(data = c(2.630725e-03,  0.0006112473,  0.0001634518,  5.617343e-05, -6.832485e-05, -0.0001355185, -0.0001500114, -4.349024e-05, -0.0001856351,  8.435390e-06,
  +                 6.112473e-04,  1.5012984017,  0.0093211267,  2.562910e-03, -3.769762e-03, -0.0070031944, -0.0080879663, -2.985069e-03, -0.0090989843,  1.480260e-03,
  +                 1.634518e-04,  0.0093211267,  0.0536024555, -9.696850e-03, -3.032083e-03, -0.0396014536, -0.0160312824, -1.492919e-02, -0.0164256693, -7.126126e-03,
  +                 5.617343e-05,  0.0025629100, -0.0096968496,  1.537242e-02,  2.629801e-03, -0.0022881318,  0.0023638365,  4.015305e-03,  0.0016797283,  2.267238e-03,
  +                -6.832485e-05, -0.0037697616, -0.0030320829,  2.629801e-03,  5.854989e-03, -0.0029843267,  0.0031799149,  2.419577e-03,  0.0034715873,  2.819921e-05,
  +                -1.355185e-04, -0.0070031944, -0.0396014536, -2.288132e-03, -2.984327e-03,  0.0622968809,  0.0022770254, -2.445532e-03,  0.0006865788, -7.372706e-04,
  +                -1.500114e-04, -0.0080879663, -0.0160312824,  2.363836e-03,  3.179915e-03,  0.0022770254,  0.0176896392,  7.219233e-03,  0.0072975355,  2.140232e-03,
  +                -4.349024e-05, -0.0029850693, -0.0149291928,  4.015305e-03,  2.419577e-03, -0.0024455320,  0.0072192326,  2.655431e-02,  0.0123892138, -3.532181e-04,
  +                -1.856351e-04, -0.0090989843, -0.0164256693,  1.679728e-03,  3.471587e-03,  0.0006865788,  0.0072975355,  1.238921e-02,  0.0217022826,  9.302658e-04,
  +                 8.435390e-06,  0.0014802596, -0.0071261264,  2.267238e-03,  2.819921e-05, -0.0007372706,  0.0021402318, -3.532181e-04,  0.0009302658,  1.533717e-02),
  +         ncol = 10, nrow = 10, byrow = TRUE, dimnames = list( c("lambda", names(t12$params)), c("lambda", names(t12$params)) ) )
  + )
  + )
  Error: t12$cov.estimates and matrix(data = c(0.002630725, 0.0006112473, 0.0001634518, 5.617343e-05,  .... are not equal:
    Mean relative difference: 1.685175e-08
  Execution halted
* checking PDF version of manual ... [13s] OK
* checking HTML version of manual ... OK
* DONE
Status: 1 ERROR, 2 NOTEs