Last updated on 2025-03-17 12:51:16 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.1 | 8.07 | 54.58 | 62.65 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.1.1 | 6.66 | 36.44 | 43.10 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.1.1 | 104.61 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.1.1 | 99.52 | OK | |||
| r-devel-macos-arm64 | 0.1.1 | 30.00 | OK | |||
| r-devel-macos-x86_64 | 0.1.1 | 56.00 | OK | |||
| r-devel-windows-x86_64 | 0.1.1 | 9.00 | 80.00 | 89.00 | OK | |
| r-patched-linux-x86_64 | 0.1.1 | 7.10 | 52.11 | 59.21 | ERROR | |
| r-release-linux-x86_64 | 0.1.1 | 9.43 | 50.93 | 60.36 | OK | |
| r-release-macos-arm64 | 0.1.1 | 29.00 | OK | |||
| r-release-macos-x86_64 | 0.1.1 | 47.00 | OK | |||
| r-release-windows-x86_64 | 0.1.1 | 8.00 | 78.00 | 86.00 | OK | |
| r-oldrel-macos-arm64 | 0.1.1 | OK | ||||
| r-oldrel-macos-x86_64 | 0.1.1 | 76.00 | OK | |||
| r-oldrel-windows-x86_64 | 0.1.1 | 8.00 | 84.00 | 92.00 | OK |
Version: 0.1.1
Check: tests
Result: ERROR
Running ‘LN-testing.R’ [2s/2s]
Running ‘NB-testing.R’ [2s/2s]
Running ‘Norm-testing.R’ [1s/1s]
Running ‘Pois-testing.R’ [2s/2s]
Running ‘gamma-testing.R’ [3s/3s]
Running the tests in ‘tests/LN-testing.R’ failed.
Complete output:
> require(glmMisrep)
Loading required package: glmMisrep
>
> data <- data.frame( Y = c(76.537552, 2528.253825, 742.661850, 275.771394, 422.115809, 994.273666, 27.363153, 107.370891,
+ 1133.422491, 42.577534, 21.222886, 82.130986, 254.636406, 116.754997, 96.768592, 238.197024,
+ 617.472558, 9.391864, 100.894256, 332.483705, 279.115849, 1101.826204, 599.726263, 218.041193,
+ 519.177473, 3529.107060, 1879.559865, 47.560367, 422.300405, 140.919276, 398.857500, 1003.204619,
+ 1518.945852, 638.622736, 1667.131817, 352.307789, 86.629404, 5524.732901, 372.118071, 3732.461629,
+ 4.532981, 147.234528, 41.538661, 10929.665862, 85.689014, 33.029995, 20.335263, 67.657641,
+ 388.829832, 8.608040),
+ X1 = c(1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0),
+ X2 = c(0.49507310, -0.08883055, 0.12926013, 0.14710474, -0.51448308, -0.13046076, 2.08608211, -0.57426685, -0.90878741,
+ 1.23110221, 1.67853057, 1.55074616, -2.18659493, 1.41014900, 0.58587594, 0.04134293, -1.99370526, 0.09277679,
+ -0.76394655, -1.28839675, 0.41072563, -1.27177378, 0.08823336, 0.20571840, -0.82168230, 0.33441449, 0.28506237,
+ 0.04591112, -0.16366695, -1.45927411, -0.10830177, -1.29651305, 0.66361581, 2.00878945, -0.32495902, -1.13028382,
+ 0.74934344, -0.54082773, -0.22820110, -0.67195420, 0.57810617, 1.16271770, -0.97158214, 0.85486460, 0.93789922,
+ -0.80378906, 0.55046386, -0.03277486, 1.10168928, -1.47985902),
+ X3 = c(0.2357360, 0.9084012, 0.9510478, 0.9426464, 0.5130887, 0.9366523, 0.2776905, 0.8125779, 0.8742008, 0.7971722, 0.3337451,
+ 0.3999461, 0.8996933, 0.4550159, 0.4777070, 0.5786287, 0.9578764, 0.6710153, 0.6726491, 0.7067857, 0.9986008, 0.9106748,
+ 0.1215141, 0.9760379, 0.8532801, 0.5979620, 0.9679438, 0.8752609, 0.4202729, 0.6799168, 0.9755574, 0.9049099, 0.9187066,
+ 0.8170819, 0.5400875, 0.8945737, 0.1026229, 0.9990335, 0.7742958, 0.8731023, 0.3292357, 0.7081538, 0.6131705, 0.8676843,
+ 0.7707079, 0.9935447, 0.8271532, 0.7889369, 0.6950691, 0.5327706),
+ Sex = c("Female", "Male", "Female", "Male", "Male", "Male", "Female", "Male", "Male", "Female", "Male", "Female",
+ "Female", "Male", "Female", "Male", "Female", "Female", "Female", "Male", "Female", "Male", "Male", "Male",
+ "Male", "Female", "Female", "Male", "Female", "Male", "Female", "Female", "Male", "Female", "Male", "Female",
+ "Male", "Male", "Female", "Female", "Female", "Male", "Male", "Male", "Female", "Female", "Female", "Female",
+ "Male", "Female"),
+ Race = c("White", "Black", "Other", "White", "White", "Other", "Other", "White", "White", "White", "White", "Black", "Other", "White",
+ "White", "Other", "White", "Other", "Other", "Black", "Other", "Black", "Other", "Black", "Other", "Black", "Other", "Other",
+ "White", "White", "Other", "White", "Black", "Black", "Other", "Other", "White", "Other", "White", "Black", "Other", "Black",
+ "Black", "White", "Other", "Other", "Other", "White", "White", "Other"),
+ V_star = c(0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0))
>
> data$Race <- as.factor(data$Race)
> data$Sex <- as.factor(data$Sex)
>
> t1 <- tryCatch(LnRegMisrepEM(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
> stopifnot(
+ t1$message == "formula must be 'log(response) ~ terms'. See 'Details'"
+ )
>
>
> t2 <- tryCatch(LnRegMisrepEM(formula = log(y) ~ X1 + X2 + X3 + Sex + Race + V_star,
+ v_star = "V_star",
+ data = data,
+ lambda = c(0.6,0.4),
+ epsilon = 1e-08,
+ maxit = 10000,
+ maxrestarts = 20),
+ error = function(x) x )
>
> stopifnot(
+ t2$message == "object 'y' not found"
+ )
>
>
>
> t3 <- tryCatch(LnRegMisrepEM(formula = log(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(
+ t3$message == "variable V_Star not present in dataframe"
+ )
>
>
> data$V_star <- ifelse(data$V_star == 1, yes = "yes", no = "no")
>
> t4 <- tryCatch(LnRegMisrepEM(formula = log(Y) ~ X1 + X2 + X3 + Sex + Race + V_star,
+ v_star = "V_star",
+ data = data,
+ lambda = c(0.6,0.4),
+ epsilon = 1e-08,
+ maxit = 10000,
+ maxrestarts = 20),
+ error = function(x) x )
>
>
> # v* variable is type character (yes and no)
> stopifnot(
+ t4$message == "v_star variable must be of class 'factor' or 'numeric'"
+ )
>
> data$V_star <- ifelse(data$V_star == "yes", yes = 1, no = 0)
>
>
> data$V_star[10] <- -1
>
>
> t5 <- tryCatch(LnRegMisrepEM(formula = log(Y) ~ X1 + X2 + X3 + Sex + Race + V_star,
+ v_star = "V_star",
+ data = data,
+ lambda = c(0.6,0.4),
+ epsilon = 1e-08,
+ maxit = 10000,
+ maxrestarts = 20),
+ error = function(x) x )
>
> # v* variable must be binary
> stopifnot(
+ t5$message == "v_star variable must contain two unique values"
+ )
>
> data$V_star[10] <- 0
>
>
> data$V_star <- ifelse(data$V_star == 1, yes = 1, no = 2)
>
> t6 <- tryCatch(LnRegMisrepEM(formula = log(Y) ~ X1 + X2 + X3 + Sex + Race + V_star,
+ v_star = "V_star",
+ data = data,
+ lambda = c(0.6,0.4),
+ epsilon = 1e-08,
+ maxit = 10000,
+ maxrestarts = 20),
+ error = function(x) x )
>
> # v* must be binary, but more specifically 0/1;
> stopifnot(
+ t6$message == "v_star variable must be coded with ones and zeroes"
+ )
>
>
>
> data$V_star <- ifelse(data$V_star == 1, yes = 1, no = 0)
>
> t7 <- tryCatch(LnRegMisrepEM(formula = log(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(
+ t7$message == "Lambda vector must sum to one"
+ )
>
>
> t8 <- tryCatch(LnRegMisrepEM(formula = log(Y) ~ X1 + X2 + X3 + Sex + Race + V_star,
+ v_star = "V_star",
+ data = data,
+ lambda = c(1/3, 1/3, 1/3),
+ epsilon = 1e-08,
+ maxit = 10000,
+ maxrestarts = 20),
+ error = function(x) x )
>
> # Inappropriately specified lambda argument
> stopifnot(
+ t8$message == "Lambda vector must contain two elements"
+ )
>
>
>
> data$X4 <- data$X2*0.3
>
> t9 <- tryCatch(LnRegMisrepEM(formula = log(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(
+ t9$message == "Linear dependencies exist in the covariates"
+ )
>
>
> t10 <- tryCatch(LnRegMisrepEM(formula = log(Y) ~ X1 + X2 + X3 + Sex + Race,
+ v_star = "V_star",
+ data = data,
+ lambda = c(0.6, 0.4),
+ epsilon = 1e-08,
+ maxit = 10000,
+ maxrestarts = 20),
+ error = function(x) x )
>
> # V_star variable is absent from formula argument
> stopifnot(
+ t10$message == "v_star variable must be specified in 'formula'"
+ )
>
>
> # EM algorithm should fail to converge within the specified number of attempts
> t11 <- tryCatch(
+ capture.output(LnRegMisrepEM(formula = log(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(
+ t11$message == "NOT CONVERGENT! Failed to converge after 1 attempts"
+ )
>
>
>
> # On the first attempt, fails to converge, and restarts with new mixing props.
> # Succeeds on the second attempt.
> msg <- capture.output(
+ t12 <- LnRegMisrepEM(formula = log(Y) ~ X1 + X2 + X3 + Sex + Race + V_star,
+ v_star = "V_star",
+ data = data,
+ lambda = c(0.6, 0.4),
+ epsilon = 1e-08,
+ maxit = 15,
+ maxrestarts = 4, verb = TRUE),
+ type = "message"
+ )
Warning message:
In sqrt(diag(cov.pars.estimates)) : NaNs produced
>
> stopifnot(
+ any(msg == "Warning: Failed to converge. Restarting with new mixing proportions")
+ )
>
>
> # This should succeed;
> msg <- capture.output(
+ t13 <- LnRegMisrepEM(formula = log(Y) ~ X1 + X2 + X3 + Sex + Race + V_star,
+ v_star = "V_star",
+ data = data,
+ lambda = c(0.6, 0.4),
+ epsilon = 1e-08,
+ maxit = 10000,
+ maxrestarts = 20),
+ type = "message"
+ )
>
> # Output validation;
>
> # Output should be a list
> stopifnot(
+ is.list(t13)
+ )
>
> # With 14 elements
> stopifnot(
+ length(t13) == 14
+ )
>
> # Fisher information matrix should be symmetric
> stopifnot(
+ isSymmetric(t13$cov.estimates)
+ )
>
>
> # The returned list should have elements with the following names
> # and types
> stopifnot(
+ all.equal(lapply(t13, 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), tolerance = 1.5e-7 )
+ )
>
>
> # Verifying the function can correctly calculate things;
> stopifnot(
+ all.equal(t13$lambda, 0.2752233, tolerance = 1.5e-7 )
+ )
>
> stopifnot(
+ all.equal(as.numeric(t13$params), c(0.38461784, 1.28998781, 2.23671321, 0.00136344, 3.43169080, 0.86482989, -0.87270674, -0.07989459,
+ 1.97303766), tolerance = 1.5e-7 )
+ )
>
> stopifnot(
+ all.equal( t13$loglik, -319.4672, tolerance = 1.5e-7 )
+ )
>
> stopifnot(
+ all.equal( t13$posterior, c(9.999942e-01, 9.998469e-01, 5.496038e-12, 9.998779e-01, 9.999986e-01, 5.904974e-09, 3.102086e-06,
+ 9.999998e-01, 1.000000e+00, 9.999999e-01, 9.999998e-01, 4.037337e-05, 7.963463e-07, 1.392101e-01,
+ 5.387680e-05, 9.999987e-01, 3.315679e-06, 1.000000e+00, 1.000000e+00, 3.769621e-02, 1.000000e+00,
+ 1.000000e+00, 2.844998e-09, 9.999996e-01, 7.450721e-07, 5.085421e-10, 4.511226e-04, 1.000000e+00,
+ 9.191679e-02, 9.972969e-01, 9.999983e-01, 9.997590e-01, 9.999999e-01, 1.050171e-08, 7.063420e-07,
+ 9.999869e-01, 8.462061e-07, 1.071525e-04, 9.999998e-01, 6.965697e-05, 9.999988e-01, 9.995615e-01,
+ 1.000000e+00, 1.176610e-06, 4.660242e-03, 1.000000e+00, 9.999999e-01, 9.998967e-01, 1.020901e-03,
+ 9.999994e-01), tolerance = 1.5e-7 )
+ )
>
>
> stopifnot(
+ all.equal( t13$all.loglik, c(-340.5320, -325.7930, -321.3185, -320.3784, -320.0802, -319.8437, -319.7085, -319.6393, -319.5830, -319.5346,
+ -319.4999, -319.4805, -319.4718, -319.4687, -319.4677, -319.4674, -319.4673, -319.4673, -319.4672, -319.4672,
+ -319.4672, -319.4672, -319.4672, -319.4672), tolerance = 1.5e-7 )
+ )
>
>
> stopifnot(
+ all.equal(t13$cov.estimates,
+ matrix(data = c(5.429249e-03, -2.887250e-04, -0.001407336, 8.894204e-06, -1.316386e-04, 0.001447803, -0.0001801113, 0.0001724132, -0.0007954676, 0.0001690426,
+ -2.887250e-04, 1.768530e-03, 0.002022002, -1.978731e-06, 8.232676e-05, -0.001401457, 0.0001295077, -0.0002436579, 0.0006128995, -0.0001726792,
+ -1.407336e-03, 2.022002e-03, 0.077937106, -5.957053e-03, -6.482111e-03, -0.060002084, -0.0106227091, -0.0225706061, -0.0218159265, -0.0098135914,
+ 8.894204e-06, -1.978731e-06, -0.005957053, 1.468143e-02, 9.544467e-04, -0.001405178, -0.0017195335, 0.0009178124, 0.0021212952, 0.0010653068,
+ -1.316386e-04, 8.232676e-05, -0.006482111, 9.544467e-04, 4.629199e-03, 0.005663381, 0.0012697822, 0.0025368665, 0.0027705039, -0.0008591309,
+ 1.447803e-03, -1.401457e-03, -0.060002084, -1.405178e-03, 5.663381e-03, 0.070883845, 0.0025822106, 0.0050719148, 0.0054058102, 0.0043546425,
+ -1.801113e-04, 1.295077e-04, -0.010622709, -1.719534e-03, 1.269782e-03, 0.002582211, 0.0151821526, 0.0045337746, 0.0015999026, 0.0001551407,
+ 1.724132e-04, -2.436579e-04, -0.022570606, 9.178124e-04, 2.536866e-03, 0.005071915, 0.0045337746, 0.0243451893, 0.0165216203, -0.0003004559,
+ -7.954676e-04, 6.128995e-04, -0.021815927, 2.121295e-03, 2.770504e-03, 0.005405810, 0.0015999026, 0.0165216203, 0.0289331944, 0.0009597426,
+ 1.690426e-04, -1.726792e-04, -0.009813591, 1.065307e-03, -8.591309e-04, 0.004354642, 0.0001551407, -0.0003004559, 0.0009597426, 0.0129483355),
+ ncol = 10, nrow = 10, byrow = TRUE, dimnames = list( c("lambda", names(t13$params)), c("lambda", names(t13$params)) ) ), tolerance = 1.5e-7
+ )
+ )
>
> stopifnot(
+ all.equal(as.numeric(t13$std.error), c(0.07368344, 0.04205390, 0.27917218, 0.12116695, 0.06803822, 0.26624020, 0.12321588, 0.15602945, 0.17009760, 0.11379075), tolerance = 1.5e-7)
+ )
>
> stopifnot(
+ all.equal(as.numeric(t13$t.values), c(4.62076060, 18.45976267, 0.02003933, 12.88945387, 7.01881857, -5.59321807, -0.46969848, 17.33917394), tolerance = 1.5e-7)
+ )
>
>
> stopifnot(
+ all.equal(as.numeric(t13$p.values), c(3.930521e-05, 3.541604e-21, 9.841116e-01, 7.954660e-16, 1.765708e-08, 1.761973e-06, 6.411210e-01, 3.304182e-20), tolerance = 1.5e-7)
+ )
>
> stopifnot(
+ all.equal(as.numeric(t13$ICs), c(658.9345, 664.5755, 678.0547), tolerance = 1.5e-7)
+ )
>
>
> stopifnot(
+ t13$ft == "LnRegMisrepEM"
+ )
>
> stopifnot(
+ class(t13$formula) == "formula"
+ )
>
> stopifnot(
+ t13$v_star_name == "V_star"
+ )
>
>
>
> # Test S3 method for summarizing misrepEM objects;
> stopifnot(
+ class(summary(t12)) == "summary.misrepEM"
+ )
Error in if (0 <= coefficients[i, 4] & coefficients[i, 4] < 0.001) { :
missing value where TRUE/FALSE needed
Calls: stopifnot -> summary -> summary.misrepEM
Execution halted
Flavor: r-patched-linux-x86_64