• using R version 4.4.2 Patched (2025-01-18 r87609)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-14 (Debian 14.2.0-12) 14.2.0     GNU Fortran (Debian 14.2.0-12) 14.2.0
• running under: Debian GNU/Linux trixie/sid
• using session charset: UTF-8
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• this is package ‘netmeta’ version ‘2.9-0’
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• checking whether package ‘netmeta’ can be installed ... OK See the install log for details.
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[201s/258s] 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 # Use depression dataset > ##D # > ##D data(Linde2015) > ##D > ##D # Define order of treatments > ##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 = 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 = 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 = 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(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 = 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
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• DONE

Status: 1 ERROR, 2 NOTEs