CRAN Package Check Results for Package rms

Last updated on 2026-10-24 05:51:25 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	8.1-1	43.42	306.30	349.72	OK
r-devel-linux-x86_64-debian-gcc	8.1-1	30.64	176.27	206.91	ERROR
r-devel-linux-x86_64-fedora-clang	8.1-1	34.00	231.41	265.41	OK
r-devel-linux-x86_64-fedora-gcc	8.1-1	32.00	186.05	218.05	OK
r-devel-windows-x86_64	8.1-1	50.00	380.00	430.00	OK
r-patched-linux-x86_64	8.1-1	48.18	295.51	343.69	OK
r-release-linux-x86_64	8.1-1	40.39	296.95	337.34	OK
r-release-macos-arm64	8.1-1	11.00	58.00	69.00	OK
r-release-macos-x86_64	8.1-1	33.00	305.00	338.00	OK
r-release-windows-x86_64	8.1-1	54.00	311.00	365.00	OK
r-oldrel-macos-arm64	8.1-1	11.00	62.00	73.00	OK
r-oldrel-macos-x86_64	8.1-1	40.00	415.00	455.00	OK
r-oldrel-windows-x86_64	8.1-1	66.00	405.00	471.00	OK

Check Details

Version: 8.1-1
Check: examples
Result: ERROR Running examples in ‘rms-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: cph > ### Title: Cox Proportional Hazards Model and Extensions > ### Aliases: cph Survival.cph Quantile.cph Mean.cph > ### Keywords: survival models nonparametric > > ### ** Examples > > # Simulate data from a population model in which the log hazard > # function is linear in age and there is no age x sex interaction > > require(survival) Loading required package: survival > require(ggplot2) Loading required package: ggplot2 > n <- 1000 > set.seed(731) > age <- 50 + 12*rnorm(n) > label(age) <- "Age" > sex <- factor(sample(c('Male','Female'), n, + rep=TRUE, prob=c(.6, .4))) > cens <- 15*runif(n) > h <- .02*exp(.04*(age-50)+.8*(sex=='Female')) > dt <- -log(runif(n))/h > label(dt) <- 'Follow-up Time' > e <- ifelse(dt <= cens,1,0) > dt <- pmin(dt, cens) > units(dt) <- "Year" > dd <- datadist(age, sex) > options(datadist='dd') > S <- Surv(dt,e) > > f <- cph(S ~ rcs(age,4) + sex, x=TRUE, y=TRUE) > cox.zph(f, "rank") # tests of PH chisq df p rcs(age, 4) 0.744 3 0.863 sex 2.859 1 0.091 GLOBAL 3.563 4 0.468 > anova(f) Wald Statistics Response: S Factor Chi-Square d.f. P age 57.75 3 <.0001 Nonlinear 8.17 2 0.0168 sex 18.75 1 <.0001 TOTAL 75.63 4 <.0001 > ggplot(Predict(f, age, sex)) # plot age effect, 2 curves for 2 sexes > survplot(f, sex) # time on x-axis, curves for x2 > res <- resid(f, "scaledsch") > time <- as.numeric(dimnames(res)[[1]]) > z <- loess(res[,4] ~ time, span=0.50) # residuals for sex > plot(time, fitted(z)) > lines(supsmu(time, res[,4]),lty=2) > plot(cox.zph(f,"identity")) #Easier approach for last few lines > # latex(f) > > > f <- cph(S ~ age + strat(sex), surv=TRUE) > g <- Survival(f) # g is a function > g(seq(.1,1,by=.1), stratum="sex=Male", type="poly") #could use stratum=2 [1] 0.999 0.996 0.994 0.993 0.992 0.992 0.992 0.992 0.992 0.991 > med <- Quantile(f) > plot(Predict(f, age, fun=function(x) med(lp=x))) #plot median survival > > # Fit a model that is quadratic in age, interacting with sex as strata > # Compare standard errors of linear predictor values with those from > # coxph > # Use more stringent convergence criteria to match with coxph > > f <- cph(S ~ pol(age,2)*strat(sex), x=TRUE, eps=1e-9, iter.max=20) Warning in coxph.control(eps = eps, toler.chol = tol, toler.inf = 1, iter.max = iter.max) : For numerical accuracy, tolerance should be < eps > coef(f) age age^2 age * sex=Male age^2 * sex=Male 0.100744 -0.000548 -0.020831 0.000276 > se <- predict(f, se.fit=TRUE)$se.fit > require(lattice) Loading required package: lattice > xyplot(se ~ age | sex, main='From cph') > a <- c(30,50,70) > comb <- data.frame(age=rep(a, each=2), + sex=rep(levels(sex), 3)) > > p <- predict(f, comb, se.fit=TRUE) > comb$yhat <- p$linear.predictors > comb$se <- p$se.fit > z <- qnorm(.975) > comb$lower <- p$linear.predictors - z*p$se.fit > comb$upper <- p$linear.predictors + z*p$se.fit > comb age sex yhat se lower upper 1 30 Female -0.8237 1.247 -3.268 1.621 2 30 Male -1.2000 0.677 -2.527 0.127 3 50 Female 0.3142 1.459 -2.545 3.174 4 50 Male -0.0368 0.943 -1.886 1.812 5 70 Female 1.0135 1.482 -1.891 3.918 6 70 Male 0.9088 0.911 -0.876 2.694 > > age2 <- age^2 > f2 <- coxph(S ~ (age + age2)*strata(sex)) > coef(f2) age age2 age:strata(sex)Male 0.100744 -0.000548 -0.020831 age2:strata(sex)Male 0.000276 > se <- predict(f2, se.fit=TRUE)$se.fit Error in a$specials[[i]] : subscript out of bounds Calls: predict -> predict.coxph -> delete.response -> off Execution halted Examples with CPU (user + system) or elapsed time > 5s user system elapsed contrast 5.115 0.098 6.158 Flavor: r-devel-linux-x86_64-debian-gcc