CRAN Package Check Results for Package glmm

Last updated on 2025-09-14 04:49:37 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.4.5 11.29 142.95 154.24 OK
r-devel-linux-x86_64-debian-gcc 1.4.5 9.25 100.21 109.46 ERROR
r-devel-linux-x86_64-fedora-clang 1.4.5 241.33 OK
r-devel-linux-x86_64-fedora-gcc 1.4.5 230.01 OK
r-devel-windows-x86_64 1.4.5 18.00 188.00 206.00 OK
r-patched-linux-x86_64 1.4.5 9.50 138.55 148.05 OK
r-release-linux-x86_64 1.4.5 10.21 139.36 149.57 OK
r-release-macos-arm64 1.4.5 125.00 OK
r-release-macos-x86_64 1.4.5 176.00 OK
r-release-windows-x86_64 1.4.5 18.00 187.00 205.00 OK
r-oldrel-macos-arm64 1.4.5 107.00 OK
r-oldrel-macos-x86_64 1.4.5 193.00 OK
r-oldrel-windows-x86_64 1.4.5 23.00 249.00 272.00 OK

Check Details

Version: 1.4.5
Check: tests
Result: ERROR Running ‘BinBerTest.R’ [5s/15s] Comparing ‘BinBerTest.Rout’ to ‘BinBerTest.Rout.save’ ... OK Running ‘binomfamtest.R’ [1s/2s] Comparing ‘binomfamtest.Rout’ to ‘binomfamtest.Rout.save’ ... OK Running ‘cfamilies.R’ [1s/2s] Comparing ‘cfamilies.Rout’ to ‘cfamilies.Rout.save’ ... OK Running ‘coreTest.R’ [3s/12s] Running ‘distRandtests.R’ [1s/2s] Comparing ‘distRandtests.Rout’ to ‘distRandtests.Rout.save’ ... OK Running ‘elTest.R’ [3s/10s] Comparing ‘elTest.Rout’ to ‘elTest.Rout.save’ ... OK Running ‘families.R’ [1s/2s] Comparing ‘families.Rout’ to ‘families.Rout.save’ ... OK Running ‘familiesFiniteDiffs.R’ [1s/2s] Comparing ‘familiesFiniteDiffs.Rout’ to ‘familiesFiniteDiffs.Rout.save’ ... OK Running ‘matvecmult.R’ [1s/2s] Comparing ‘matvecmult.Rout’ to ‘matvecmult.Rout.save’ ... OK Running ‘mcseTest.R’ [3s/9s] Comparing ‘mcseTest.Rout’ to ‘mcseTest.Rout.save’ ... OK Running ‘objfunTest.R’ [3s/12s] Comparing ‘objfunTest.Rout’ to ‘objfunTest.Rout.save’ ... OK Running ‘salamFiniteDiffs.R’ [3s/12s] Comparing ‘salamFiniteDiffs.Rout’ to ‘salamFiniteDiffs.Rout.save’ ... OK Running ‘testpiecesBH.R’ [3s/9s] Comparing ‘testpiecesBH.Rout’ to ‘testpiecesBH.Rout.save’ ... OK Running ‘testt.R’ [1s/2s] Comparing ‘testt.Rout’ to ‘testt.Rout.save’ ... OK Running ‘weightsTest.R’ [4s/14s] Comparing ‘weightsTest.Rout’ to ‘weightsTest.Rout.save’ ... OK Running the tests in ‘tests/coreTest.R’ failed. Complete output: > library(glmm) Loading required package: trust Loading required package: mvtnorm Loading required package: Matrix Loading required package: parallel Loading required package: doParallel Loading required package: foreach Loading required package: iterators > data(BoothHobert) > clust <- makeCluster(2) > set.seed(1234) > out<-glmm(y~0+x1,list(y~0+z1),varcomps.names=c("z1"),data=BoothHobert, + family.glmm=bernoulli.glmm,m=50,doPQL=FALSE,debug=TRUE, cluster=clust) > > vars <- new.env(parent = emptyenv()) > debug<-out$debug > vars$m1 <- debug$m1 > m2 <- debug$m2 > m3 <- debug$m3 > vars$zeta <- 5 > vars$cl <- clust > registerDoParallel(vars$cl) #making cluster usable with foreach > vars$no_cores <- length(vars$cl) > vars$umat<-debug$umat > vars$newm <- nrow(vars$umat) > vars$u.star<-debug$u.star > vars$mod.mcml<-out$mod.mcml > vars$nu.pql <- debug$nu.pql > D.star.inv <- Dstarnotsparse <- vars$D.star <- as.matrix(debug$D.star) > > getEk<-glmm:::getEk > addVecs<-glmm:::addVecs > genRand<-glmm:::genRand > > vars$family.glmm<-out$family.glmm > vars$ntrials<- rep(1, length(out$y)) > beta.pql <- debug$beta.pql > > if(is.null(out$weights)){ + wts <- rep(1, length(out$y)) + } else{ + wts <- out$weights + } > > vars$wts<-wts > > simulate <- function(vars, Dstarnotsparse, m2, m3, beta.pql, D.star.inv){ + #generate m1 from t(0,D*) + if(vars$m1>0) genData<-rmvt(ceiling(vars$m1/vars$no_cores),sigma=Dstarnotsparse,df=vars$zeta,type=c("shifted")) + if(vars$m1==0) genData<-NULL + + #generate m2 from N(u*,D*) + if(m2>0) genData2<-genRand(vars$u.star,vars$D.star,ceiling(m2/vars$no_cores)) + if(m2==0) genData2<-NULL + + + #generate m3 from N(u*,(Z'c''(Xbeta*+zu*)Z+D*^{-1})^-1) + if(m3>0){ + Z=do.call(cbind,vars$mod.mcml$z) + eta.star<-as.vector(vars$mod.mcml$x%*%beta.pql+Z%*%vars$u.star) + if(vars$family.glmm$family.glmm=="bernoulli.glmm") {cdouble<-vars$family.glmm$cpp(eta.star)} + if(vars$family.glmm$family.glmm=="poisson.glmm"){cdouble<-vars$family.glmm$cpp(eta.star)} + if(vars$family.glmm$family.glmm=="binomial.glmm"){cdouble<-vars$family.glmm$cpp(eta.star, vars$ntrials)} + #still a vector + cdouble<-Diagonal(length(cdouble),cdouble) + wtsmat <- diag(vars$wts) + Sigmuh.inv<- t(Z)%*%cdouble%*%wtsmat%*%Z+D.star.inv + Sigmuh<-solve(Sigmuh.inv) + genData3<-genRand(vars$u.star,Sigmuh,ceiling(m3/vars$no_cores)) + } + if(m3==0) genData3<-NULL + + # #these are from distribution based on data + # if(distrib=="tee")genData<-genRand(sigma.gen,s.pql,mod.mcml$z,m1,distrib="tee",gamm) + # if(distrib=="normal")genData<-genRand(sigma.pql,s.pql,mod.mcml$z,m1,distrib="normal",gamm) + # #these are from standard normal + # ones<-rep(1,length(sigma.pql)) + # zeros<-rep(0,length(s.pql)) + # genData2<-genRand(ones,zeros,mod.mcml$z,m2,distrib="normal",gamm) + + umat<-rbind(genData,genData2,genData3) + m <- nrow(umat) + list(umat=umat, m=m, Sigmuh.inv=Sigmuh.inv) + } > > clusterSetRNGStream(vars$cl, 1234) > > clusterExport(vars$cl, c("vars", "Dstarnotsparse", "m2", "m3", "beta.pql", "D.star.inv", "simulate", "genRand"), envir = environment()) #installing variables on each core > noprint <- clusterEvalQ(vars$cl, umatparams <- simulate(vars=vars, Dstarnotsparse=Dstarnotsparse, m2=m2, m3=m3, beta.pql=beta.pql, D.star.inv=D.star.inv)) > > vars$nbeta <- 1 > vars$p1=vars$p2=vars$p3=1/3 > par<-c(6,1.5) > del<-rep(10^-8,2) > > objfun<-glmm:::objfun > > core2<-objfun(par=par, vars=vars) > > umats <- clusterEvalQ(vars$cl, umatparams$umat) > umat <- Reduce(rbind, umats) > > Sigmuh.invs <- clusterEvalQ(vars$cl, umatparams$Sigmuh.inv) > Sigmuh.inv <- Sigmuh.invs[[1]] > > stopCluster(clust) > > vars$cl <- makeCluster(1) Error in serverSocket(port = port) : creation of server socket failed: port 11755 cannot be opened Calls: makeCluster -> makePSOCKcluster -> serverSocket Execution halted Flavor: r-devel-linux-x86_64-debian-gcc