* using log directory 'd:/Rcompile/CRANpkg/local/2.15/gbev.Rcheck'
* using R version 2.15.0 (2012-03-30)
* using platform: x86_64-pc-mingw32 (64-bit)
* using session charset: ISO8859-1
* checking for file 'gbev/DESCRIPTION' ... OK
* this is package 'gbev' version '0.1.1'
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... NOTE
As from R 2.14.0 all packages need a namespace.
One will be generated on installation, but it is better to handcraft a
NAMESPACE file: R CMD build will produce a suitable starting point.
CRAN requires a NAMESPACE file for all submissions.
* checking whether package 'gbev' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking for portable file names ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
* checking for unstated dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
File 'gbev/R/gbev.R':
  .First.lib calls:
    require(mvtnorm)

Package startup functions should not change the search path.
See section 'Good practice' in ?.onAttach.

gbev: warning in match.call(expand = FALSE): partial argument match of
  'expand' to 'expand.dots'
gbev: no visible binding for global variable 'response'
gbev: no visible binding for global variable 'var.names'
part.dep: no visible binding for global variable 'fit'
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... WARNING
'library' or 'require' call not declared from: 'lattice'
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking compiled code ... OK
* checking examples ...
** running examples for arch 'i386' ... ERROR
Running examples in 'gbev-Ex.R' failed
The error most likely occurred in:

> ### Name: gbev
> ### Title: Boosted regression trees with errors-in-variables
> ### Aliases: gbev
> ### Keywords: nonparametric tree
> 
> ### ** Examples
> 
> 
> ### Univariate regression example
> n<-500
> varX<-1
> varME<-0.25
> varNoise<-0.3^2
> 
> ### Data 
> x<-rnorm(n,sd=sqrt(varX))                              ### Error free covariate
> w<-x+rnorm(n,sd=sqrt(varME))                           ### Error contaminated version
> fx<-sin(pi*x/2)/(1+2*(x^2)*((2*as.numeric(x>=0)-1)+1)) ### True regression function  
> y<-fx+rnorm(n,sd=sqrt(varNoise))                       ### Response                           
> dat<-data.frame(y=y,w=w)
> 
> ### Measurement error model ####
> ###  
> ### The measurement error model is a list of the following components:
> ###
> ### SigmaX:    the covariance matrices of the mixture model for the error free covariates 
> ###            SigmaX[i,,] is the covariance matrix of the i-th mixture density
> ### mu:        the means of the mixture model for the error free covariates 
> ###            mu[i,] is the mean-vector of the i-th mixture density
> ### SigmaME:   the covariance matrix of the measurment error
> ### pComp:     the weights of the mixture distribution, pComp[i] is the weight of the 
> ###            i-th mixture density
> ### numComp:   the number of components in the mixture 
> ##
> p<-1
> pME<-1
> 
> numComp<-3                                    ## number of components in gaussian mixture for X-distribution
> SigmaME<-diag(varME,pME)
> SigmaJ<-array(dim=c(numComp,pME,pME))         
> mu<-array(dim=c(numComp,pME))
> pComp<-array(1/numComp,dim=c(numComp,1))
> for(i in 1:numComp)
+ {
+ SigmaJ[i,,]<-diag(varX,pME)
+ mu[i,]<-rep(0,pME)
+ }
> ### list required by "gbev" for measurement error model
> meModel<-list(SigmaX=SigmaJ,mu=mu,SigmaME=SigmaME,pComp=pComp,numComp=numComp)
> 
> 
> fit<-gbev(y~w,data=dat,
+           measErrorModel=meModel,     
+           method="L2",              ## Squared error loss
+           nboost=1000,              ## 1000 boosting iterations
+           lambda=5,                 ## regularization of regression tree
+           maxDepth=2,               ## maximum tree depth, 2 corresponds stumps
+           mc=2,                     ## number of monte-carlo samples per tree build 
+           minSplit=3,               ## minimum number of obs in node to split
+           minBucket=0,              ## minimum number of obs in nodes
+           sPoints=10,               ## number of sampled candidate split points
+           intermPred=5)             ## increments of iterations to store predictions 
> 
> ### 5-fold cross-validation
> hcv<-cvLoss(object=fit,k=5,random=FALSE,loss="L2")
> plot(hcv$iters,hcv$cvLoss,type="l")
> 
> hp<-part.dep(object=fit,varIndx=1,firstTree=1,lastTree=hcv$estIter)
> 
> x<-seq(-2,2,by=.02)
> fx<-sin(pi*x/2)/(1+2*(x^2)*((2*as.numeric(x>=0)-1)+1)) 
> points(x,fx,type="l",lty=5)
> 
> 
> 
> 
> ## Simulated binary regression example, 
> ## with: Y=I( X1*X2+X2*X3+X1*X3>0), with measurement error on X's
> n<-1000
> p<-3
> varX<-1     ## 
> varME<-0.5  ## measurement error variance
> 
> x<-rnorm(p*n)
> x<-matrix(x,ncol=p,nrow=n)
> ## add measurement error
> w<-x+matrix(rnorm(p*n,sd=sqrt(varME)),ncol=p,nrow=n)   
> 
> x<-x[,c(1:p)]*x[,c(2:p,1)]
> x<-apply(x,1,sum)
> threshold<-0
> y<-as.numeric(x>threshold)
> dat<-data.frame(y=y,w1=w[,1],w2=w[,2],w3=w[,3])  ##  must be modified if(p!=3)
> 
> 
> #### Measurement error model ######
> numComp<-1                               ##  Number of components in mixture 
> SigmaME<-diag(varME,p)                   ##  Covariance matrix of measurement error
> SigmaJ<-array(dim=c(numComp,p,p))        ##  Covariance matices for mixture
> mu<-array(dim=c(numComp,p))              ##  Mean vectors for mixture components
> pComp<-array(1/numComp,dim=c(numComp,1)) ##  Mixture probabilities
> for(i in 1:numComp)
+ {                                        ## filling in mixture model for X-distribution
+ SigmaJ[i,,]<-diag(varX,p)
+ mu[i,]<-rep(0,p)
+ }
> ## The list for measurement error model 
> meModel<-list(SigmaX=SigmaJ,mu=mu,SigmaME=SigmaME,pComp=pComp,numComp=numComp)
> 
> fit<-gbev(y~w1+w2+w3,data=dat,
+          measErrorModel=meModel,   
+          method="logLike",       ## loss function
+          nboost=1000,            ## number of boosting iterations
+          lambda=40,              ## regularization parameter used in regression tree
+          maxDepth=3,             ## maximum depth of regression tree 
+          minSplit=10,             ## minimum number of observations in  node to  split
+          minBucket=0,            ## minimum number in split node to allow split
+          sPoints=2,             ## number of sampled canditate split points 
+          mc=2,                   ## monte-carlo sample size used in each regression tree
+          intermPred=10)          ## Increments of iterations to store loss function
>         
> 
> ## plot loss function as function of iterations
> hp<-plotLoss(fit,loss="logLike",startIter=10)
> 
> ## bivariate partial dependence plot
> hdp<-part.dep(object=fit,varIndx=c(1,2),firstTree=1,
+ lastTree=1000,ngrid=50)
> dpp<-data.frame(x1=hdp$dat$x,x2=hdp$dat$y,prob=hdp$dat$z)
> library(lattice)
Error in library(lattice) : there is no package called 'lattice'
Execution halted
** running examples for arch 'x64' ... ERROR
Running examples in 'gbev-Ex.R' failed
The error most likely occurred in:

> ### Name: gbev
> ### Title: Boosted regression trees with errors-in-variables
> ### Aliases: gbev
> ### Keywords: nonparametric tree
> 
> ### ** Examples
> 
> 
> ### Univariate regression example
> n<-500
> varX<-1
> varME<-0.25
> varNoise<-0.3^2
> 
> ### Data 
> x<-rnorm(n,sd=sqrt(varX))                              ### Error free covariate
> w<-x+rnorm(n,sd=sqrt(varME))                           ### Error contaminated version
> fx<-sin(pi*x/2)/(1+2*(x^2)*((2*as.numeric(x>=0)-1)+1)) ### True regression function  
> y<-fx+rnorm(n,sd=sqrt(varNoise))                       ### Response                           
> dat<-data.frame(y=y,w=w)
> 
> ### Measurement error model ####
> ###  
> ### The measurement error model is a list of the following components:
> ###
> ### SigmaX:    the covariance matrices of the mixture model for the error free covariates 
> ###            SigmaX[i,,] is the covariance matrix of the i-th mixture density
> ### mu:        the means of the mixture model for the error free covariates 
> ###            mu[i,] is the mean-vector of the i-th mixture density
> ### SigmaME:   the covariance matrix of the measurment error
> ### pComp:     the weights of the mixture distribution, pComp[i] is the weight of the 
> ###            i-th mixture density
> ### numComp:   the number of components in the mixture 
> ##
> p<-1
> pME<-1
> 
> numComp<-3                                    ## number of components in gaussian mixture for X-distribution
> SigmaME<-diag(varME,pME)
> SigmaJ<-array(dim=c(numComp,pME,pME))         
> mu<-array(dim=c(numComp,pME))
> pComp<-array(1/numComp,dim=c(numComp,1))
> for(i in 1:numComp)
+ {
+ SigmaJ[i,,]<-diag(varX,pME)
+ mu[i,]<-rep(0,pME)
+ }
> ### list required by "gbev" for measurement error model
> meModel<-list(SigmaX=SigmaJ,mu=mu,SigmaME=SigmaME,pComp=pComp,numComp=numComp)
> 
> 
> fit<-gbev(y~w,data=dat,
+           measErrorModel=meModel,     
+           method="L2",              ## Squared error loss
+           nboost=1000,              ## 1000 boosting iterations
+           lambda=5,                 ## regularization of regression tree
+           maxDepth=2,               ## maximum tree depth, 2 corresponds stumps
+           mc=2,                     ## number of monte-carlo samples per tree build 
+           minSplit=3,               ## minimum number of obs in node to split
+           minBucket=0,              ## minimum number of obs in nodes
+           sPoints=10,               ## number of sampled candidate split points
+           intermPred=5)             ## increments of iterations to store predictions 
> 
> ### 5-fold cross-validation
> hcv<-cvLoss(object=fit,k=5,random=FALSE,loss="L2")
> plot(hcv$iters,hcv$cvLoss,type="l")
> 
> hp<-part.dep(object=fit,varIndx=1,firstTree=1,lastTree=hcv$estIter)
> 
> x<-seq(-2,2,by=.02)
> fx<-sin(pi*x/2)/(1+2*(x^2)*((2*as.numeric(x>=0)-1)+1)) 
> points(x,fx,type="l",lty=5)
> 
> 
> 
> 
> ## Simulated binary regression example, 
> ## with: Y=I( X1*X2+X2*X3+X1*X3>0), with measurement error on X's
> n<-1000
> p<-3
> varX<-1     ## 
> varME<-0.5  ## measurement error variance
> 
> x<-rnorm(p*n)
> x<-matrix(x,ncol=p,nrow=n)
> ## add measurement error
> w<-x+matrix(rnorm(p*n,sd=sqrt(varME)),ncol=p,nrow=n)   
> 
> x<-x[,c(1:p)]*x[,c(2:p,1)]
> x<-apply(x,1,sum)
> threshold<-0
> y<-as.numeric(x>threshold)
> dat<-data.frame(y=y,w1=w[,1],w2=w[,2],w3=w[,3])  ##  must be modified if(p!=3)
> 
> 
> #### Measurement error model ######
> numComp<-1                               ##  Number of components in mixture 
> SigmaME<-diag(varME,p)                   ##  Covariance matrix of measurement error
> SigmaJ<-array(dim=c(numComp,p,p))        ##  Covariance matices for mixture
> mu<-array(dim=c(numComp,p))              ##  Mean vectors for mixture components
> pComp<-array(1/numComp,dim=c(numComp,1)) ##  Mixture probabilities
> for(i in 1:numComp)
+ {                                        ## filling in mixture model for X-distribution
+ SigmaJ[i,,]<-diag(varX,p)
+ mu[i,]<-rep(0,p)
+ }
> ## The list for measurement error model 
> meModel<-list(SigmaX=SigmaJ,mu=mu,SigmaME=SigmaME,pComp=pComp,numComp=numComp)
> 
> fit<-gbev(y~w1+w2+w3,data=dat,
+          measErrorModel=meModel,   
+          method="logLike",       ## loss function
+          nboost=1000,            ## number of boosting iterations
+          lambda=40,              ## regularization parameter used in regression tree
+          maxDepth=3,             ## maximum depth of regression tree 
+          minSplit=10,             ## minimum number of observations in  node to  split
+          minBucket=0,            ## minimum number in split node to allow split
+          sPoints=2,             ## number of sampled canditate split points 
+          mc=2,                   ## monte-carlo sample size used in each regression tree
+          intermPred=10)          ## Increments of iterations to store loss function
>         
> 
> ## plot loss function as function of iterations
> hp<-plotLoss(fit,loss="logLike",startIter=10)
> 
> ## bivariate partial dependence plot
> hdp<-part.dep(object=fit,varIndx=c(1,2),firstTree=1,
+ lastTree=1000,ngrid=50)
> dpp<-data.frame(x1=hdp$dat$x,x2=hdp$dat$y,prob=hdp$dat$z)
> library(lattice)
Error in library(lattice) : there is no package called 'lattice'
Execution halted