CRAN Package Check Results for Package arm

Last updated on 2026-09-05 22:49:00 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.15-3 14.02 90.41 104.43 OK
r-devel-linux-x86_64-debian-gcc 1.15-3 8.63 67.24 75.87 ERROR
r-devel-linux-x86_64-fedora-clang 1.15-3 69.16 OK
r-devel-linux-x86_64-fedora-gcc 1.15-3 68.72 OK
r-devel-windows-x86_64 1.15-3 14.00 106.00 120.00 OK
r-patched-linux-x86_64 1.15-3 13.28 87.68 100.96 OK
r-release-linux-x86_64 1.15-3 13.57 87.25 100.82 OK
r-release-macos-arm64 1.15-3 3.00 23.00 26.00 OK
r-release-macos-x86_64 1.15-3 9.00 84.00 93.00 OK
r-release-windows-x86_64 1.15-3 14.00 103.00 117.00 OK
r-oldrel-macos-arm64 1.15-3 3.00 23.00 26.00 OK
r-oldrel-macos-x86_64 1.15-3 9.00 100.00 109.00 OK
r-oldrel-windows-x86_64 1.15-3 19.00 133.00 152.00 OK

Check Details

Version: 1.15-3
Check: examples
Result: ERROR Running examples in ‘arm-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: standardize > ### Title: Function for Standardizing Regression Predictors by Centering > ### and Dividing by 2 sd's > ### Aliases: standardize standardize,lm-method standardize,glm-method > ### standardize,merMod-method standardize,polr-method > ### Keywords: manip models methods > > ### ** Examples > > # Set up the fake data > n <- 100 > x <- rnorm (n, 2, 1) > x1 <- rnorm (n) > x1 <- (x1-mean(x1))/(2*sd(x1)) # standardization > x2 <- rbinom (n, 1, .5) > b0 <- 1 > b1 <- 1.5 > b2 <- 2 > y <- rbinom (n, 1, invlogit(b0+b1*x1+b2*x2)) > y2 <- sample(1:5, n, replace=TRUE) > M1 <- glm (y ~ x, family=binomial(link="logit")) > display(M1) glm(formula = y ~ x, family = binomial(link = "logit")) coef.est coef.se (Intercept) 1.37 0.66 x 0.07 0.29 --- n = 100, k = 2 residual deviance = 94.2, null deviance = 94.3 (difference = 0.1) > M1.1 <- glm (y ~ rescale(x), family=binomial(link="logit")) > display(M1.1) glm(formula = y ~ rescale(x), family = binomial(link = "logit")) coef.est coef.se (Intercept) 1.52 0.26 rescale(x) 0.12 0.52 --- n = 100, k = 2 residual deviance = 94.2, null deviance = 94.3 (difference = 0.1) > M1.2 <- standardize(M1) > display(M1.2) glm(formula = y ~ z.x, family = binomial(link = "logit")) coef.est coef.se (Intercept) 1.52 0.26 z.x 0.12 0.52 --- n = 100, k = 2 residual deviance = 94.2, null deviance = 94.3 (difference = 0.1) > # M1.1 & M1.2 should be the same > M2 <- polr(ordered(y2) ~ x) > display(M2) Re-fitting to get Hessian polr(formula = ordered(y2) ~ x) coef.est coef.se x -0.19 0.20 1|2 -2.32 0.55 2|3 -1.11 0.49 3|4 -0.11 0.47 4|5 0.88 0.48 --- n = 100, k = 5 (including 4 intercepts) residual deviance = 317.3, null deviance is not computed by polr > M2.1 <- polr(ordered(y2) ~ rescale(x)) > display(M2.1) Re-fitting to get Hessian polr(formula = ordered(y2) ~ rescale(x)) coef.est coef.se rescale(x) -0.34 0.37 1|2 -1.92 0.30 2|3 -0.72 0.21 3|4 0.29 0.20 4|5 1.27 0.24 --- n = 100, k = 5 (including 4 intercepts) residual deviance = 317.3, null deviance is not computed by polr > M2.2 <- standardize(M2.1) > display(M2.2) Re-fitting to get Hessian polr(formula = ordered(y2) ~ rescale(z.x)) coef.est coef.se rescale(z.x) -0.34 0.37 1|2 -1.92 0.30 2|3 -0.72 0.21 3|4 0.29 0.20 4|5 1.27 0.24 --- n = 100, k = 5 (including 4 intercepts) residual deviance = 317.3, null deviance is not computed by polr > # M2.1 & M2.2 should be the same > form <- y ~ x1 + x2 # input formula as an object > M3 <- glm(form, family=binomial) > M3.1 <- standardize(M3) Error in standardize.default(call = call, unchanged = unchanged, standardize.y = standardize.y, : Error: The object x1 must be either numeric or a factor. Calls: standardize -> standardize -> .local -> standardize.default Execution halted Flavor: r-devel-linux-x86_64-debian-gcc