CRAN Package Check Results for Package mice

Last updated on 2020-04-09 06:47:22 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 3.8.0 32.37 202.71 235.08 OK
r-devel-linux-x86_64-debian-gcc 3.8.0 23.09 153.47 176.56 OK
r-devel-linux-x86_64-fedora-clang 3.8.0 275.84 OK
r-devel-linux-x86_64-fedora-gcc 3.8.0 277.10 OK
r-devel-windows-ix86+x86_64 3.8.0 65.00 530.00 595.00 OK
r-devel-windows-ix86+x86_64-gcc8 3.8.0 54.00 361.00 415.00 OK
r-patched-linux-x86_64 3.8.0 24.58 194.61 219.19 OK
r-patched-solaris-x86 3.8.0 360.10 ERROR
r-release-linux-x86_64 3.8.0 29.11 181.01 210.12 OK
r-release-windows-ix86+x86_64 3.8.0 58.00 415.00 473.00 OK
r-release-osx-x86_64 3.8.0 OK
r-oldrel-windows-ix86+x86_64 3.8.0 67.00 466.00 533.00 OK
r-oldrel-osx-x86_64 3.8.0 OK

Check Details

Version: 3.8.0
Check: examples
Result: ERROR
    Running examples in ‘mice-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: mice.impute.2lonly.pmm
    > ### Title: Imputation at level 2 by predictive mean matching
    > ### Aliases: mice.impute.2lonly.pmm 2lonly.pmm
    >
    > ### ** Examples
    >
    >
    > ##################################################
    > # simulate some data
    > # x,y ... level 1 variables
    > # v,w ... level 2 variables
    >
    > G <- 250 # number of groups
    > n <- 20 # number of persons
    > beta <- .3 # regression coefficient
    > rho <- .30 # residual intraclass correlation
    > rho.miss <- .10 # correlation with missing response
    > missrate <- .50 # missing proportion
    > y1 <- rep( rnorm( G , sd = sqrt( rho ) ) , each=n ) + rnorm(G*n , sd = sqrt( 1 - rho ))
    > w <- rep( round( rnorm(G ) , 2 ) , each=n )
    > v <- rep( round( runif( G , 0 , 3 ) ) , each=n )
    > x <- rnorm( G*n )
    > y <- y1 + beta * x + .2 * w + .1 * v
    > dfr0 <- dfr <- data.frame( "group" = rep(1:G , each=n ) , "x" = x , "y" = y , "w" = w , "v" = v )
    > dfr[ rho.miss * x + rnorm( G*n , sd = sqrt( 1 - rho.miss ) ) < qnorm( missrate ) , "y" ] <- NA
    > dfr[ rep( rnorm(G) , each=n ) < qnorm( missrate ) , "w" ] <- NA
    > dfr[ rep( rnorm(G) , each=n ) < qnorm( missrate ) , "v" ] <- NA
    >
    > #....
    > # empty mice imputation
    > imp0 <- mice( as.matrix(dfr) , maxit=0 )
    > predM <- imp0$predictorMatrix
    > impM <- imp0$method
    >
    > #...
    > # multilevel imputation
    > predM1 <- predM
    > predM1[c("w","y","v"),"group"] <- -2
    > predM1["y","x"] <- 1 # fixed x effects imputation
    > impM1 <- impM
    > impM1[c("y","w","v")] <- c("2l.pan" , "2lonly.norm" , "2lonly.pmm" )
    >
    > # turn v into a categorical variable
    > dfr$v <- as.factor(dfr$v)
    > levels(dfr$v) <- LETTERS[1:4]
    >
    > # y ... imputation using pan
    > # w ... imputation at level 2 using norm
    > # v ... imputation at level 2 using pmm
    >
    > imp <- mice(dfr, m = 1, predictorMatrix = predM1 ,
    + method = impM1, maxit = 1, paniter = 500)
    
     iter imp variable
     1 1 y wError in eigen(cx, symmetric = TRUE) : infinite or missing values in 'x'
    Calls: mice -> sampler -> sampler.univ -> remove.lindep -> eigen
    Execution halted
Flavor: r-patched-solaris-x86