CRAN Package Check Results for Package mvord

Last updated on 2025-09-05 09:49:34 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.2.6 12.08 275.76 287.84 OK
r-devel-linux-x86_64-debian-gcc 1.2.6 8.60 157.20 165.80 ERROR
r-devel-linux-x86_64-fedora-clang 1.2.6 456.99 OK
r-devel-linux-x86_64-fedora-gcc 1.2.6 472.02 OK
r-devel-windows-x86_64 1.2.6 14.00 269.00 283.00 OK
r-patched-linux-x86_64 1.2.6 13.08 262.43 275.51 OK
r-release-linux-x86_64 1.2.6 11.31 260.86 272.17 OK
r-release-macos-arm64 1.2.6 132.00 OK
r-release-macos-x86_64 1.2.6 308.00 OK
r-release-windows-x86_64 1.2.6 32.00 275.00 307.00 OK
r-oldrel-macos-arm64 1.2.6 129.00 OK
r-oldrel-macos-x86_64 1.2.6 332.00 OK
r-oldrel-windows-x86_64 1.2.6 21.00 328.00 349.00 OK

Check Details

Version: 1.2.6
Check: tests
Result: ERROR Running ‘check_methods.R’ [3s/3s] Running ‘check_mvord_data.R’ [2s/2s] Running ‘check_mvord_errors.R’ [2s/2s] Running ‘check_set_threshold_type.R’ [2s/3s] Running ‘check_toy_example.R’ [2s/3s] Running ‘check_transf_sigmas.R’ [1s/2s] Running ‘check_transf_thresholds.R’ [2s/2s] Running the tests in ‘tests/check_toy_example.R’ failed. Complete output: > library(mvord) Loading required package: minqa Loading required package: BB Loading required package: ucminf Loading required package: dfoptim > library(MASS) > > > #data(data_toy_example) > tolerance <- 1e-6 > > mult.obs <- 2 > sigma <- matrix(c(1,0.8,0.8,1), ncol = 2) > betas <- list(c(0.8,-0.5), + c(0.8,-0.5)) > thresholds <- list(c(-1,1),c(-1,1)) > nobs <- 100 > suppressWarnings(RNGversion("3.5.0")) > set.seed(2017) > errors <- mvrnorm(n = nobs, mu = rep(0, mult.obs), Sigma = sigma) > > X1 <- rnorm(nobs, 0, 1) > X2 <- rnorm(nobs, 0, 1) > > pred <- cbind(X1, X2) > > y <- sapply(1:mult.obs, function(j) pred %*% betas[[j]] + errors[, j], simplify = "array") > y.ord <- sapply(1:mult.obs, function(j) cut(y[, , j], c(min(y[, , j]) - 1, + c(thresholds[[j]]), max(y[, , j]) + 1), + labels = FALSE), simplify = "array") > > predictors.fixed <- lapply(1:mult.obs, function(j) pred) > y <- as.data.frame(y.ord) > > for(i in 1:mult.obs){ + y[, i] <- factor(y[, i], levels = sort(unique(y[, i])), + ordered = TRUE) + } > > > > > data_toy_example <- cbind.data.frame(y, predictors.fixed[[1]]) > colnames(data_toy_example) <- c("Y1","Y2", "X1", "X2") > w <- c(rep(1/20, 20), rep(1/30, 30), rep(1/20, 20), rep(1/30, 30)) > > > > # convert data_toy_example into long format > df <- cbind.data.frame("i" = rep(1:100,2), + "j" = rep(1:2,each = 100), + "Y" = c(data_toy_example$Y1, data_toy_example$Y2), + "X1" = rep(data_toy_example$X1,2), + "X2" = rep(data_toy_example$X2,2), + "f1" = factor(sample(rep(data_toy_example$Y2,2)), + ordered = FALSE), + "f2" = factor(rep(data_toy_example$Y1,2), ordered = FALSE), + w = rep(w,2)) > df$X3 <- cut(df$X2, c(-Inf, -0.2, 0.2, Inf)) > > > > # library(ROI) > # ROI_solver <- function(starting.values, objFun, control){ > # n <- length(starting.values) > # op <- OP(objective = F_objective(objFun, n = n), > # bounds = V_bound(li = seq_len(n), lb = rep.int(-Inf, n))) > # optRes <- ROI_solve(op, solver = "nlminb", > # control = c(list(start = starting.values), > # control)) > # list(optpar = optRes$solution, > # objective = optRes$objval) # a vector of length equal to number of parameters to optimize > # } > # > # > # > > # Test MMO() ---- > > ## Coef constraints as vector ---- > > res <- mvord(formula = MMO(Y) ~ 0 + X1 + X2, + data = df, + link = mvprobit(), + error.structure = cor_general(~1), + threshold.constraints = c(1,1), + coef.constraints = c(1,1), + contrasts = list(f1 = function(x) + contr.treatment(nlevels(df$f1), base = 1), + f2 = "contr.sum"), + control= mvord.control(solver="BFGS",se=TRUE)) Warning message: In mvord(formula = MMO(Y) ~ 0 + X1 + X2, data = df, link = mvprobit(), : Variables f1 and f2 are absent, the contrasts will be ignored. > options(digits = 22) > > res.summary <- summary(res, short = FALSE) Call: mvord(formula = MMO(Y) ~ 0 + X1 + X2, data = df, error.structure = cor_general(~1), link = mvprobit(), coef.constraints = c(1, 1), threshold.constraints = c(1, 1), contrasts = list(f1 = function(x) contr.treatment(nlevels(df$f1), base = 1), f2 = "contr.sum"), control = mvord.control(solver = "BFGS", se = TRUE)) Formula: MMO(Y) ~ 0 + X1 + X2 link threshold nsubjects ndim logPL CLAIC CLBIC fevals mvprobit flexible 100 2 -134.91 280.34 294.06 32 Thresholds: Estimate Std. Error z value 1 1|2 -0.96257386663499888702 0.16613952738603396386 -5.7937700000000003087 1 2|3 1.03347036873238828925 0.15004482537615168591 6.8877399999999999736 2 1|2 -0.96257386663499888702 0.16613952738603396386 -5.7937700000000003087 2 2|3 1.03347036873238828925 0.15004482537615168591 6.8877399999999999736 Pr(>|z|) 1 1|2 6.8825e-09 *** 1 2|3 5.6684e-12 *** 2 1|2 6.8825e-09 *** 2 2|3 5.6684e-12 *** --- Signif. codes: 0 '***' 0.001000000000000000020817 '**' 0.01000000000000000020817 '*' 0.05000000000000000277556 '.' 0.1000000000000000055511 ' ' 1 Coefficients: Estimate Std. Error z value X1 1 0.63801035062442590373 0.13576747301354935571 4.6992900000000004113 X2 1 -0.42672524816265555714 0.13643665802445156809 -3.1276399999999999757 Pr(>|z|) X1 1 2.6107e-06 *** X2 1 0.0017621 ** --- Signif. codes: 0 '***' 0.001000000000000000020817 '**' 0.01000000000000000020817 '*' 0.05000000000000000277556 '.' 0.1000000000000000055511 ' ' 1 Error Structure: Estimate Std. Error z value corr 1 2 0.854266728221043991631 0.062440889990424582046 13.681210000000000093 Pr(>|z|) corr 1 2 < 2.22e-16 *** --- Signif. codes: 0 '***' 0.001000000000000000020817 '**' 0.01000000000000000020817 '*' 0.05000000000000000277556 '.' 0.1000000000000000055511 ' ' 1 > # paste(format(res.summary$thresholds$Estimate), collapse = ",") > # paste(format(res.summary$coefficients$Estimate), collapse = ",") > # paste(format(res.summary$error.structure$Estimate), collapse = ",") > mvord:::check(all.equal(res.summary$thresholds$Estimate, c(-0.96257386663519672876, 1.03347036873223707687, -0.96257386663519672876, 1.03347036873223707687), tolerance = tolerance)) > mvord:::check(all.equal(res.summary$coefficients$Estimate, c(0.63801035062404309883, -0.42672524816263474046), tolerance = tolerance)) > mvord:::check(all.equal(res.summary$error.structure$Estimate, c(0.85426672822122684536), tolerance = tolerance)) > mvord:::check(all.equal(res.summary$thresholds$`Std. Error`, c(0.16613952738605441972, 0.15004482537617935822, 0.16613952738605441972, 0.15004482537617935822), tolerance = tolerance)) > mvord:::check(all.equal(res.summary$coefficients$`Std. Error`, c(0.13576747301357894315, 0.13643665802446261481), tolerance = tolerance)) > mvord:::check(all.equal(res.summary$error.structure$`Std. Error`, c(0.062440889990360744222), tolerance = tolerance)) > mvord:::check(all.equal(logLik(res)[[1]], -134.90867383086322207, tolerance = tolerance)) > mvord:::check(all.equal(AIC(res), 280.3436634512001433, tolerance = tolerance)) > mvord:::check(all.equal(BIC(res), 294.05508548271637892, tolerance = tolerance)) Error in mvord:::check(all.equal(BIC(res), 294.055085482716, tolerance = tolerance)) : ..1 is not TRUE Calls: <Anonymous> -> stopifnot Execution halted Flavor: r-devel-linux-x86_64-debian-gcc