Last updated on 2025-09-14 07:51:05 CEST.
| Package | ERROR | OK |
|---|---|---|
| glmm | 1 | 12 |
| stableGR | 13 |
Current CRAN status: ERROR: 1, OK: 12
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
Current CRAN status: OK: 13