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
Examples with CPU (user + system) or elapsed time > 5s
                  user system elapsed
Gurusamy2011     5.926  0.224   6.685
Dong2013         4.631  0.082   5.260
netmetabin       4.651  0.043   6.011
netpairwise      4.412  0.027   6.194
netmeasures      4.293  0.087   5.398
forest.netmeta   4.083  0.015   5.312
netgraph.netmeta 4.006  0.059   5.806
invmat           3.949  0.012   5.280
netgraph         3.849  0.074   5.523
netmeta          3.810  0.016   5.351
netmatrix        3.776  0.007   5.155