CRAN Package Check Results for Package glmm

Last updated on 2020-06-15 06:49:05 CEST.

Additional issues

clang-ASAN gcc-ASAN

Check Details

Version: 1.4
Check: running tests for arch ‘i386’
Result: ERROR
     Running 'BinBerTest.R' [10s]
     Comparing 'BinBerTest.Rout' to 'BinBerTest.Rout.save' ... OK
     Running 'binomfamtest.R' [2s]
     Comparing 'binomfamtest.Rout' to 'binomfamtest.Rout.save' ... OK
     Running 'cfamilies.R' [2s]
     Comparing 'cfamilies.Rout' to 'cfamilies.Rout.save' ... OK
     Running 'coreTest.R' [9s]
     Comparing 'coreTest.Rout' to 'coreTest.Rout.save' ... OK
     Running 'distRandtests.R' [2s]
     Comparing 'distRandtests.Rout' to 'distRandtests.Rout.save' ... OK
     Running 'elTest.R' [7s]
     Comparing 'elTest.Rout' to 'elTest.Rout.save' ... OK
     Running 'families.R' [2s]
     Comparing 'families.Rout' to 'families.Rout.save' ... OK
     Running 'familiesFiniteDiffs.R' [2s]
     Comparing 'familiesFiniteDiffs.Rout' to 'familiesFiniteDiffs.Rout.save' ... OK
     Running 'matvecmult.R' [2s]
     Comparing 'matvecmult.Rout' to 'matvecmult.Rout.save' ... OK
     Running 'mcseTest.R' [9s]
     Comparing 'mcseTest.Rout' to 'mcseTest.Rout.save' ... OK
     Running 'objfunTest.R' [8s]
     Comparing 'objfunTest.Rout' to 'objfunTest.Rout.save' ... OK
     Running 'salamFiniteDiffs.R' [19s]
     Running 'testpiecesBH.R' [8s]
     Comparing 'testpiecesBH.Rout' to 'testpiecesBH.Rout.save' ... OK
     Running 'testt.R' [2s]
     Comparing 'testt.Rout' to 'testt.Rout.save' ... OK
     Running 'weightsTest.R' [11s]
     Comparing 'weightsTest.Rout' to 'weightsTest.Rout.save' ... OK
    Running the tests in 'tests/salamFiniteDiffs.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
     > clust <- makeCluster(1)
     > set.seed(1234)
     > data(salamander)
     > sal<-glmm(Mate~Cross,random=list(~0+Female,~0+Male),varcomps.names=c("F","M"), data=salamander,family.glmm=bernoulli.glmm,m=100,debug=TRUE,doPQL=FALSE, cluster=clust)
     >
     > vars <- new.env(parent = emptyenv())
     > debug<-sal$debug
     > vars$m1 <- debug$m1
     > m2 <- debug$m2
     > m3 <- debug$m3
     > vars$zeta <- 5
     > vars$cl <- sal$cluster
     > registerDoParallel(vars$cl) #making cluster usable with foreach
     > vars$no_cores <- length(vars$cl)
     > vars$mod.mcml<-sal$mod.mcml
     > vars$nu.pql <- debug$nu.pql
     > vars$umat<-debug$umat
     > vars$newm <- nrow(vars$umat)
     > vars$u.star<-debug$u.star
     > D.star.inv <- Dstarnotsparse <- vars$D.star <- as.matrix(debug$D.star)
     >
     > getEk<-glmm:::getEk
     > addVecs<-glmm:::addVecs
     > genRand<-glmm:::genRand
     >
     > vars$family.glmm<-sal$family.glmm
     > vars$ntrials<-1
     > beta.pql <- debug$beta.pql
     >
     > simulate <- function(vars, Dstarnotsparse, m2, m3, beta.pql, D.star.inv){
     + #generate m1 from t(0,D*)
     + if(vars$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$ntrials)}
     + #still a vector
     + cdouble<-Diagonal(length(cdouble),cdouble)
     + Sigmuh.inv<- t(Z)%*%cdouble%*%Z+D.star.inv
     + Sigmuh<-solve(Sigmuh.inv)
     + genData3<-genRand(vars$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 <- 4
     > vars$p1=vars$p2=vars$p3=1/3
     > beta<-rep(0,4)
     > nu<-rep(2,2)
     > par<-c(beta,nu)
     > del<-rep(10^-6,6)
     >
     > objfun<-glmm:::objfun
     >
     > cache<-new.env(parent = emptyenv())
     >
     > ltheta<-objfun(par, cache, vars=vars)
     >
     > lthetadel<-objfun(par+del, cache, vars=vars)
     Error in { :
     task 1 failed - "NA/NaN/Inf in foreign function call (arg 28)"
     Calls: objfun -> %dopar% -> <Anonymous>
     Execution halted
Flavor: r-oldrel-windows-ix86+x86_64