Last updated on 2025-09-05 20:48:36 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2.6 | 12.63 | 238.31 | 250.94 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.2.6 | 9.34 | 184.91 | 194.25 | OK | |
| 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 | 16.00 | 238.00 | 254.00 | ERROR | |
| 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 |
Version: 1.2.6
Check: tests
Result: ERROR
Running ‘check_methods.R’ [4s/6s]
Running ‘check_mvord_data.R’ [2s/3s]
Running ‘check_mvord_errors.R’ [2s/3s]
Running ‘check_set_threshold_type.R’ [2s/3s]
Running ‘check_toy_example.R’ [3s/4s]
Running ‘check_transf_sigmas.R’ [2s/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-clang
Version: 1.2.6
Check: tests
Result: ERROR
Running 'check_methods.R' [3s]
Running 'check_mvord_data.R' [2s]
Running 'check_mvord_errors.R' [2s]
Running 'check_set_threshold_type.R' [2s]
Running 'check_toy_example.R' [2s]
Running 'check_transf_sigmas.R' [2s]
Running 'check_transf_thresholds.R' [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-windows-x86_64