* using log directory ‘/srv/hornik/tmp/CRAN_pretest/netmeta.Rcheck’ * using R Under development (unstable) (2025-01-19 r87600) * using platform: x86_64-pc-linux-gnu * R was compiled by Debian clang version 19.1.6 (1+b1) Debian flang-new version 19.1.6 (1+b1) * running under: Debian GNU/Linux trixie/sid * using session charset: UTF-8 * checking for file ‘netmeta/DESCRIPTION’ ... OK * this is package ‘netmeta’ version ‘3.0-1’ * package encoding: UTF-8 * checking CRAN incoming feasibility ... [5s/8s] NOTE Maintainer: ‘Guido Schwarzer ’ Possibly misspelled words in DESCRIPTION: Evrenoglou (39:200) Suggests or Enhances not in mainstream repositories: hasseDiagram * checking package namespace information ... OK * checking package dependencies ... INFO Package suggested but not available for checking: ‘hasseDiagram’ * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘netmeta’ can be installed ... [16s/16s] OK * checking package directory ... OK * checking for future file timestamps ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... [2s/2s] OK * checking whether the package can be loaded with stated dependencies ... [1s/1s] OK * checking whether the package can be unloaded cleanly ... [2s/2s] OK * checking whether the namespace can be loaded with stated dependencies ... [1s/1s] OK * checking whether the namespace can be unloaded cleanly ... [2s/2s] OK * checking loading without being on the library search path ... [2s/2s] OK * checking whether startup messages can be suppressed ... [2s/2s] OK * checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [38s/38s] OK * checking Rd files ... [1s/1s] OK * checking Rd metadata ... OK * checking Rd line widths ... OK * checking Rd cross-references ... NOTE Unknown package ‘hasseDiagram’ in Rd xrefs * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... [0s/0s] OK * checking data for ASCII and uncompressed saves ... OK * checking sizes of PDF files under ‘inst/doc’ ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... [100s/100s] ERROR Running examples in ‘netmeta-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: netposet > ### Title: Partial order of treatments in network meta-analysis > ### Aliases: netposet print.netposet > > ### ** Examples > > ## Not run: > ##D # Define order of treatments in depression dataset linde2015 > ##D # > ##D trts <- c("TCA", "SSRI", "SNRI", "NRI", > ##D "Low-dose SARI", "NaSSa", "rMAO-A", "Hypericum", "Placebo") > ##D > ##D # Outcome labels > ##D # > ##D outcomes <- c("Early response", "Early remission") > ##D > ##D # (1) Early response > ##D # > ##D p1 <- pairwise(treat = list(treatment1, treatment2, treatment3), > ##D event = list(resp1, resp2, resp3), n = list(n1, n2, n3), > ##D studlab = id, data = dat.linde2015, sm = "OR") > ##D # > ##D net1 <- netmeta(p1, common = FALSE, > ##D seq = trts, ref = "Placebo", small.values = "undesirable") > ##D > ##D # (2) Early remission > ##D # > ##D p2 <- pairwise(treat = list(treatment1, treatment2, treatment3), > ##D event = list(remi1, remi2, remi3), n = list(n1, n2, n3), > ##D studlab = id, data = dat.linde2015, sm = "OR") > ##D # > ##D net2 <- netmeta(p2, common = FALSE, > ##D seq = trts, ref = "Placebo", small.values = "undesirable") > ##D > ##D # Partial order of treatment rankings (two outcomes) > ##D # > ##D po <- netposet(netrank(net1), netrank(net2), outcomes = outcomes) > ##D > ##D # Hasse diagram > ##D # > ##D hasse(po) > ##D > ##D > ##D # > ##D # Outcome labels > ##D # > ##D outcomes <- c("Early response", "Early remission", > ##D "Lost to follow-up", "Lost to follow-up due to AEs", > ##D "Adverse events (AEs)") > ##D > ##D # (3) Loss to follow-up > ##D # > ##D p3 <- pairwise(treat = list(treatment1, treatment2, treatment3), > ##D event = list(loss1, loss2, loss3), n = list(n1, n2, n3), > ##D studlab = id, data = dat.linde2015, sm = "OR") > ##D # > ##D net3 <- netmeta(p3, common = FALSE, > ##D seq = trts, ref = "Placebo", small.values = "desirable") > ##D > ##D # (4) Loss to follow-up due to adverse events > ##D # > ##D p4 <- pairwise(treat = list(treatment1, treatment2, treatment3), > ##D event = list(loss.ae1, loss.ae2, loss.ae3), n = list(n1, n2, n3), > ##D studlab = id, data = subset(dat.linde2015, id != 55), sm = "OR") > ##D # > ##D net4 <- netmeta(p4, common = FALSE, > ##D seq = trts, ref = "Placebo", small.values = "desirable") > ##D > ##D # (5) Adverse events > ##D # > ##D p5 <- pairwise(treat = list(treatment1, treatment2, treatment3), > ##D event = list(ae1, ae2, ae3), n = list(n1, n2, n3), > ##D studlab = id, data = dat.linde2015, sm = "OR") > ##D # > ##D net5 <- netmeta(p5, common = FALSE, > ##D seq = trts, ref = "Placebo", small.values = "desirable") > ##D > ##D # Partial order of treatment rankings (all five outcomes) > ##D # > ##D po.ranks <- netposet(netrank(net1), netrank(net2), > ##D netrank(net3), netrank(net4), netrank(net5), outcomes = outcomes) > ##D > ##D # Same result > ##D # > ##D po.nets <- netposet(net1, net2, net3, net4, net5, > ##D outcomes = outcomes) > ##D # > ##D all.equal(po.ranks, po.nets) > ##D > ##D # Print matrix with P-scores (random effects model) > ##D # > ##D po.nets$P.random > ##D > ##D # Hasse diagram for all outcomes (random effects model) > ##D # > ##D hasse(po.ranks) > ##D > ##D # Hasse diagram for outcomes early response and early remission > ##D # > ##D po12 <- netposet(netrank(net1), netrank(net2), > ##D outcomes = outcomes[1:2]) > ##D hasse(po12) > ##D > ##D # Scatter plot > ##D # > ##D oldpar <- par(pty = "s") > ##D plot(po12) > ##D par(oldpar) > ## End(Not run) > > # Example using ranking matrix with P-scores > # > # Ribassin-Majed L, Marguet S, Lee A.W., et al. (2017): > # What is the best treatment of locally advanced nasopharyngeal > # carcinoma? An individual patient data network meta-analysis. > # Journal of Clinical Oncology, 35, 498-505 > # > outcomes <- c("OS", "PFS", "LC", "DC") > treatments <- c("RT", "IC-RT", "IC-CRT", "CRT", + "CRT-AC", "RT-AC", "IC-RT-AC") > # > # P-scores (from Table 1) > # > pscore.os <- c(15, 33, 63, 70, 96, 28, 45) / 100 > pscore.pfs <- c( 4, 46, 79, 52, 94, 36, 39) / 100 > pscore.lc <- c( 9, 27, 47, 37, 82, 58, 90) / 100 > pscore.dc <- c(16, 76, 95, 48, 72, 32, 10) / 100 > # > pscore.matrix <- data.frame(pscore.os, pscore.pfs, pscore.lc, pscore.dc) > rownames(pscore.matrix) <- treatments > colnames(pscore.matrix) <- outcomes > pscore.matrix OS PFS LC DC RT 0.15 0.04 0.09 0.16 IC-RT 0.33 0.46 0.27 0.76 IC-CRT 0.63 0.79 0.47 0.95 CRT 0.70 0.52 0.37 0.48 CRT-AC 0.96 0.94 0.82 0.72 RT-AC 0.28 0.36 0.58 0.32 IC-RT-AC 0.45 0.39 0.90 0.10 > # > po <- netposet(pscore.matrix) > po12 <- netposet(pscore.matrix[, 1:2]) > po RT IC-RT IC-CRT CRT CRT-AC RT-AC IC-RT-AC RT 0 0 0 0 0 0 0 IC-RT 1 0 0 0 0 0 0 IC-CRT 0 1 0 0 0 0 0 CRT 1 0 0 0 0 0 0 CRT-AC 0 0 0 1 0 1 0 RT-AC 1 0 0 0 0 0 0 IC-RT-AC 0 0 0 0 0 0 0 > po12 RT IC-RT IC-CRT CRT CRT-AC RT-AC IC-RT-AC RT 0 0 0 0 0 0 0 IC-RT 0 0 0 0 0 1 0 IC-CRT 0 1 0 0 0 0 1 CRT 0 1 0 0 0 0 1 CRT-AC 0 0 1 1 0 0 0 RT-AC 1 0 0 0 0 0 0 IC-RT-AC 0 0 0 0 0 1 0 > # > hasse(po) Error: Package 'hasseDiagram' missing. Please use the following R commands for installation: install.packages("BiocManager") BiocManager::install() BiocManager::install("Rgraphviz") install.packages("hasseDiagram") Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... [0s/0s] OK * checking PDF version of manual ... [6s/6s] OK * checking HTML version of manual ... [4s/4s] OK * checking for non-standard things in the check directory ... OK * checking for detritus in the temp directory ... OK * DONE Status: 1 ERROR, 2 NOTEs