CRAN Package Check Results for Package StagedChoiceSplineMix

Last updated on 2020-05-25 01:49:05 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0.0 3.08 33.90 36.98 ERROR
r-devel-linux-x86_64-debian-gcc 1.0.0 3.04 26.57 29.61 ERROR
r-devel-linux-x86_64-fedora-clang 1.0.0 56.38 ERROR
r-devel-linux-x86_64-fedora-gcc 1.0.0 46.30 ERROR
r-devel-windows-ix86+x86_64 1.0.0 10.00 48.00 58.00 ERROR
r-patched-linux-x86_64 1.0.0 3.51 31.79 35.30 OK
r-patched-solaris-x86 1.0.0 63.70 OK
r-release-linux-x86_64 1.0.0 3.68 31.49 35.17 OK
r-release-osx-x86_64 1.0.0 OK
r-release-windows-ix86+x86_64 1.0.0 8.00 60.00 68.00 OK
r-oldrel-osx-x86_64 1.0.0 OK
r-oldrel-windows-ix86+x86_64 1.0.0 6.00 54.00 60.00 OK

Check Details

Version: 1.0.0
Check: examples
Result: ERROR
    Running examples in 'StagedChoiceSplineMix-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: StagedChoiceSplineMix
    > ### Title: Performs iterations between an EM algorithm for a mixture of
    > ### two-stage logistic regressions with fixed candidate knots and knot
    > ### movements
    > ### Aliases: StagedChoiceSplineMix
    >
    > ### ** Examples
    >
    > #######################################
    > ###### 1. Generate data (simdata) #####
    > set.seed(77)
    > k<-3
    >
    > betab<-matrix(c(1.5,2.0,0.3,-0.2,-0.1,0.6,-2.5,0.5,1,3,-1,1,2,-0.5,-1.5),5,k,byrow=TRUE)
    > betaw<-matrix(c(-2.0,-1,0.3,-0.3,-0.2,0.2,-3,-2,0,3,2,-1.5,3,2,-1),5,k,byrow=TRUE)
    > sp1.knots<-matrix(c(8,14,4,11,5,15,5,12,5,13,7,14),3,4,byrow=TRUE)
    > lambda<-c(0.3,0.3,0.4)
    >
    > # Large data set
    > #n_id<-700
    > #nobsb<-200
    >
    > # Small data set
    > n_id<-100
    > nobsb<-100
    >
    > nb<-n_id*nobsb
    >
    > idb <- rep(1:n_id, each=nobsb)
    >
    > xb.1<-rbinom(nb,size=1,prob=0.7)
    > xb.com<-cbind(1,xb.1)
    > xb.sp1<-runif((nb),-2,2)
    > xb<-cbind(xb.com,xb.sp1)
    >
    > sp1.mat.b<- matrix(double(20*nb),ncol=20)
    > sp1.mat.b[,1]<-xb.sp1
    > sp1.quan<-quantile(xb.sp1,c(0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,
    + 0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95))
    >
    > for (i in 1:19){
    + sp1.mat.b[,(i+1)]<-xb.sp1-sp1.quan[i]
    + }
    > sp1.mat.b[sp1.mat.b<0]<-0
    > sp1.mat.b[,1]<-xb.sp1
    >
    > xb.class<-list()
    > for (i in 1:k){
    + xb.class[[i]]<-cbind(xb.com,sp1.mat.b[,1],sp1.mat.b[,(sp1.knots[i,1:2])+1])
    + }
    >
    > xbetab<-matrix(double(nb*k),ncol=k)
    > for (i in 1:k){
    + xbetab[,i]<-data.matrix(xb.class[[i]])%*%betab[,i]
    + }
    >
    > num.idb<-as.vector(ddply(data.frame(idb),"idb",count)[,-1])
    >
    > w<-sample(c(1:k), n_id, replace = TRUE, prob = lambda)
    > wb<-rep(w,num.idb)
    > yb.temp<-matrix(double(nb*k),ncol=k)
    >
    > for (i in 1:k){
    + yb.temp[,i]<-rbinom((nb), size = 1, prob = (1/(1+exp(-xbetab[, i]))))
    + }
    > yb<-sapply(1:nb, function(i) yb.temp[i,wb[i]])
    >
    > idw<-idb[yb==1]
    > nw<-length(idw)
    > sp1.mat.w<-sp1.mat.b[yb==1,]
    > xw.com<-xb.com[yb==1,]
    > xw<-xb[yb==1,]
    >
    > xw.class<-list()
    > for (i in 1:k){
    + xw.class[[i]]<-cbind(xw.com,sp1.mat.w[,1],sp1.mat.w[,(sp1.knots[i,3:4])+1])
    + }
    >
    > xbetaw<-matrix(double(nw*k),ncol=k)
    > for (i in 1:k){
    + xbetaw[,i]<-data.matrix(xw.class[[i]])%*%betaw[,i]
    + }
    >
    > num.idw<-as.vector(ddply(data.frame(idw),"idw",count)[,-1])
    > ww<-wb[yb==1]
    >
    > yw.temp<-matrix(double(nw*k),ncol=k)
    > for (i in 1:k){
    + yw.temp[,i]<-rbinom((nw), size = 1, prob = (1/(1+exp(-xbetaw[, i]))))
    + }
    > yw<-sapply(1:nw, function(i) yw.temp[i,ww[i]])
    >
    > yb.aug<-cbind(1:length(yb),yb)
    > yw.aug<-cbind(which(yb==1),yw)
    >
    > colnames(yb.aug)[1]<-"num"
    > colnames(yw.aug)[1]<-"num"
    >
    > ybyw<-merge(yb.aug,yw.aug, all = TRUE)[,-1]
    >
    > simdata<-cbind(idb,ybyw,xb.1,sp1.mat.b[,1])
    > simdata[is.na(simdata)]<-""
    > simdata[,3]<-as.integer(simdata[,3])
    > colnames(simdata)[1]<-"userid"
    > colnames(simdata)[2]<-"browsed"
    > colnames(simdata)[3]<-"wrote"
    > colnames(simdata)[4]<-"x1"
    > colnames(simdata)[5]<-"sp1"
    >
    > ########################################
    > ##### 2. Run StagedChoiceSplineMix #####
    > ## number of latent classes
    > set.seed(66)
    > k<-3
    >
    > ## starting points: true parameters used in the data generation (optional)
    > betab<-matrix(c(1.5,2.0,0.3,-0.2,-0.1,0.6,-2.5,0.5,1,3,-1,1,2,-0.5,-1.5),5,k,byrow=TRUE)
    > betaw<-matrix(c(-2.0,-1,0.3,-0.3,-0.2,0.2,-3,-2,0,3,2,-1.5,3,2,-1),5,k,byrow=TRUE)
    > lambda<-c(0.3,0.3,0.4)
    >
    > ## number of random multiple starting points to try given a knot configuration
    > nst<-1
    >
    > ## vector of the columns of spline variables in the data set (required)
    > sp.cols<-5
    >
    > # vector of the numbers of candidate knots for splined variables (optional)
    > num.knots<-19
    >
    > ## true knot configuration used in the data generation
    > sp1.knots<-matrix(c(8,14,4,11,5,15,5,12,5,13,7,14),3,4,byrow=TRUE)
    >
    > ## list of knot configuration of spline variables (optional)
    > sp.knots<-list(sp1.knots)
    >
    > ## Run "StagedChoiceSplineMix"
    > out<-StagedChoiceSplineMix(data=simdata, M=1, sp.cols=sp.cols, num.knots, sp.knots,
    + betab, betaw, lambda, k=k, nst=nst, epsilon=1e-06, maxit=500, maxrestarts=100, maxer=20)
    
    *************************************************************
    *** Knot configuration: 1
    *************************************************************
    Number of errors within a knot configuration: 0
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 1
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 2
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 3
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 4
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 5
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 6
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 7
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 8
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 9
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 10
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 11
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 12
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 13
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 14
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 15
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 16
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 17
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 18
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Number of errors within a knot configuration: 19
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     'max' not meaningful for factors
    Error in if (ll.old <= bestst$loglik) { : argument is of length zero
    Calls: StagedChoiceSplineMix
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Version: 1.0.0
Check: examples
Result: ERROR
    Running examples in ‘StagedChoiceSplineMix-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: StagedChoiceSplineMix
    > ### Title: Performs iterations between an EM algorithm for a mixture of
    > ### two-stage logistic regressions with fixed candidate knots and knot
    > ### movements
    > ### Aliases: StagedChoiceSplineMix
    >
    > ### ** Examples
    >
    > #######################################
    > ###### 1. Generate data (simdata) #####
    > set.seed(77)
    > k<-3
    >
    > betab<-matrix(c(1.5,2.0,0.3,-0.2,-0.1,0.6,-2.5,0.5,1,3,-1,1,2,-0.5,-1.5),5,k,byrow=TRUE)
    > betaw<-matrix(c(-2.0,-1,0.3,-0.3,-0.2,0.2,-3,-2,0,3,2,-1.5,3,2,-1),5,k,byrow=TRUE)
    > sp1.knots<-matrix(c(8,14,4,11,5,15,5,12,5,13,7,14),3,4,byrow=TRUE)
    > lambda<-c(0.3,0.3,0.4)
    >
    > # Large data set
    > #n_id<-700
    > #nobsb<-200
    >
    > # Small data set
    > n_id<-100
    > nobsb<-100
    >
    > nb<-n_id*nobsb
    >
    > idb <- rep(1:n_id, each=nobsb)
    >
    > xb.1<-rbinom(nb,size=1,prob=0.7)
    > xb.com<-cbind(1,xb.1)
    > xb.sp1<-runif((nb),-2,2)
    > xb<-cbind(xb.com,xb.sp1)
    >
    > sp1.mat.b<- matrix(double(20*nb),ncol=20)
    > sp1.mat.b[,1]<-xb.sp1
    > sp1.quan<-quantile(xb.sp1,c(0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,
    + 0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95))
    >
    > for (i in 1:19){
    + sp1.mat.b[,(i+1)]<-xb.sp1-sp1.quan[i]
    + }
    > sp1.mat.b[sp1.mat.b<0]<-0
    > sp1.mat.b[,1]<-xb.sp1
    >
    > xb.class<-list()
    > for (i in 1:k){
    + xb.class[[i]]<-cbind(xb.com,sp1.mat.b[,1],sp1.mat.b[,(sp1.knots[i,1:2])+1])
    + }
    >
    > xbetab<-matrix(double(nb*k),ncol=k)
    > for (i in 1:k){
    + xbetab[,i]<-data.matrix(xb.class[[i]])%*%betab[,i]
    + }
    >
    > num.idb<-as.vector(ddply(data.frame(idb),"idb",count)[,-1])
    >
    > w<-sample(c(1:k), n_id, replace = TRUE, prob = lambda)
    > wb<-rep(w,num.idb)
    > yb.temp<-matrix(double(nb*k),ncol=k)
    >
    > for (i in 1:k){
    + yb.temp[,i]<-rbinom((nb), size = 1, prob = (1/(1+exp(-xbetab[, i]))))
    + }
    > yb<-sapply(1:nb, function(i) yb.temp[i,wb[i]])
    >
    > idw<-idb[yb==1]
    > nw<-length(idw)
    > sp1.mat.w<-sp1.mat.b[yb==1,]
    > xw.com<-xb.com[yb==1,]
    > xw<-xb[yb==1,]
    >
    > xw.class<-list()
    > for (i in 1:k){
    + xw.class[[i]]<-cbind(xw.com,sp1.mat.w[,1],sp1.mat.w[,(sp1.knots[i,3:4])+1])
    + }
    >
    > xbetaw<-matrix(double(nw*k),ncol=k)
    > for (i in 1:k){
    + xbetaw[,i]<-data.matrix(xw.class[[i]])%*%betaw[,i]
    + }
    >
    > num.idw<-as.vector(ddply(data.frame(idw),"idw",count)[,-1])
    > ww<-wb[yb==1]
    >
    > yw.temp<-matrix(double(nw*k),ncol=k)
    > for (i in 1:k){
    + yw.temp[,i]<-rbinom((nw), size = 1, prob = (1/(1+exp(-xbetaw[, i]))))
    + }
    > yw<-sapply(1:nw, function(i) yw.temp[i,ww[i]])
    >
    > yb.aug<-cbind(1:length(yb),yb)
    > yw.aug<-cbind(which(yb==1),yw)
    >
    > colnames(yb.aug)[1]<-"num"
    > colnames(yw.aug)[1]<-"num"
    >
    > ybyw<-merge(yb.aug,yw.aug, all = TRUE)[,-1]
    >
    > simdata<-cbind(idb,ybyw,xb.1,sp1.mat.b[,1])
    > simdata[is.na(simdata)]<-""
    > simdata[,3]<-as.integer(simdata[,3])
    > colnames(simdata)[1]<-"userid"
    > colnames(simdata)[2]<-"browsed"
    > colnames(simdata)[3]<-"wrote"
    > colnames(simdata)[4]<-"x1"
    > colnames(simdata)[5]<-"sp1"
    >
    > ########################################
    > ##### 2. Run StagedChoiceSplineMix #####
    > ## number of latent classes
    > set.seed(66)
    > k<-3
    >
    > ## starting points: true parameters used in the data generation (optional)
    > betab<-matrix(c(1.5,2.0,0.3,-0.2,-0.1,0.6,-2.5,0.5,1,3,-1,1,2,-0.5,-1.5),5,k,byrow=TRUE)
    > betaw<-matrix(c(-2.0,-1,0.3,-0.3,-0.2,0.2,-3,-2,0,3,2,-1.5,3,2,-1),5,k,byrow=TRUE)
    > lambda<-c(0.3,0.3,0.4)
    >
    > ## number of random multiple starting points to try given a knot configuration
    > nst<-1
    >
    > ## vector of the columns of spline variables in the data set (required)
    > sp.cols<-5
    >
    > # vector of the numbers of candidate knots for splined variables (optional)
    > num.knots<-19
    >
    > ## true knot configuration used in the data generation
    > sp1.knots<-matrix(c(8,14,4,11,5,15,5,12,5,13,7,14),3,4,byrow=TRUE)
    >
    > ## list of knot configuration of spline variables (optional)
    > sp.knots<-list(sp1.knots)
    >
    > ## Run "StagedChoiceSplineMix"
    > out<-StagedChoiceSplineMix(data=simdata, M=1, sp.cols=sp.cols, num.knots, sp.knots,
    + betab, betaw, lambda, k=k, nst=nst, epsilon=1e-06, maxit=500, maxrestarts=100, maxer=20)
    
    *************************************************************
    *** Knot configuration: 1
    *************************************************************
    Number of errors within a knot configuration: 0
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 1
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 2
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 3
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 4
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 5
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 6
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 7
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 8
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 9
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 10
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 11
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 12
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 13
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 14
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 15
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 16
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 17
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 18
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Number of errors within a knot configuration: 19
    Error in Summary.factor(structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
     ‘max’ not meaningful for factors
    Error in if (ll.old <= bestst$loglik) { : argument is of length zero
    Calls: StagedChoiceSplineMix
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64