CRAN Package Check Results for Package norm2

Last updated on 2019-10-13 12:46:18 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 2.0.2 10.61 33.24 43.85 OK
r-devel-linux-x86_64-debian-gcc 2.0.2 10.06 26.53 36.59 OK
r-devel-linux-x86_64-fedora-clang 2.0.2 58.59 NOTE
r-devel-linux-x86_64-fedora-gcc 2.0.2 58.34 NOTE
r-devel-windows-ix86+x86_64 2.0.2 46.00 60.00 106.00 OK
r-patched-linux-x86_64 2.0.2 10.95 33.50 44.45 OK
r-patched-solaris-x86 2.0.2 85.70 ERROR
r-release-linux-x86_64 2.0.2 10.54 34.23 44.77 OK
r-release-windows-ix86+x86_64 2.0.2 32.00 87.00 119.00 OK
r-release-osx-x86_64 2.0.2 OK
r-oldrel-windows-ix86+x86_64 2.0.2 31.00 53.00 84.00 OK
r-oldrel-osx-x86_64 2.0.2 OK

Check Details

Version: 2.0.2
Check: compiled code
Result: NOTE
    File ‘norm2/libs/norm2.so’:
     Found no calls to: ‘R_registerRoutines’, ‘R_useDynamicSymbols’
    
    It is good practice to register native routines and to disable symbol
    search.
    
    See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 2.0.2
Check: examples
Result: ERROR
    Running examples in ‘norm2-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: emNorm
    > ### Title: EM algorithm for incomplete multivariate normal data
    > ### Aliases: emNorm emNorm.default emNorm.formula emNorm.norm
    > ### Keywords: multivariate NA
    >
    > ### ** Examples
    >
    > ## run EM for marijuana data with strict convergence criterion
    > data(marijuana)
    > result <- emNorm(marijuana, criterion=1e-06)
    
     *** caught segfault ***
    address 0, cause 'memory not mapped'
    
    Traceback:
     1: emNorm.default(marijuana, criterion = 1e-06)
     2: emNorm(marijuana, criterion = 1e-06)
    An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86

Version: 2.0.2
Check: tests
Result: ERROR
     Running ‘emTests.R’
     Running ‘impTests.R’
     Comparing ‘impTests.Rout’ to ‘impTests.Rout.save’ ... OK
     Running ‘logpostTests.R’
     Comparing ‘logpostTests.Rout’ to ‘logpostTests.Rout.save’ ... OK
     Running ‘mcmcTests.R’
     Comparing ‘mcmcTests.Rout’ to ‘mcmcTests.Rout.save’ ... OK
     Running ‘miInferenceTests.R’
     Comparing ‘miInferenceTests.Rout’ to ‘miInferenceTests.Rout.save’ ... OK
    Running the tests in ‘tests/emTests.R’ failed.
    Complete output:
     > library(norm2)
     >
     > ## run EM on fake data with no missing values
     > set.seed(1234)
     > simdata <- data.frame(
     + Y1=rnorm(6), Y2=rnorm(6), Y3=rnorm(6), X1=rnorm(6) )
     > emResult <- emNorm( cbind(Y1,Y2,Y3) ~ X1, data=simdata )
     > print( summary( emResult ) )
     Predictor (X) variables:
     Mean SD Observed Missing Pct.Missing
     (Intercept) 1.0000000 0.000000 6 0 0
     X1 0.2068512 1.178512 6 0 0
    
     Response (Y) variables:
     Mean SD Observed Missing Pct.Missing
     Y1 -0.2092854 1.2953550 6 0 0
     Y2 -0.6752401 0.2138685 6 0 0
     Y3 -0.2141319 0.6864124 6 0 0
    
     Missingness patterns for response (Y) variables
     (. denotes observed value, m denotes missing value)
     (variable names are displayed vertically)
     (rightmost column is the frequency):
     YYY
     123
     ... 6
    
     Method: EM
     Prior: "uniform"
     Convergence criterion: 1e-05
     Iterations: 2
     Converged: TRUE
     Max. rel. difference: 0
     -2 Loglikelihood: -8.3665
     -2 Log-posterior density: -8.3665
     Worst fraction missing information: 0
    
     Estimated coefficients (beta):
     Y1 Y2 Y3
     (Intercept) -0.3069981 -0.6785479 -0.2457194
     X1 0.4723816 0.0159912 0.1527063
    
     Estimated covariance matrix (sigma):
     Y1 Y2 Y3
     Y1 1.14001811 0.06789367 0.13397509
     Y2 0.06789367 0.03782049 0.03942553
     Y3 0.13397509 0.03942553 0.36564511
     >
     > ## impose missing values and run again
     > simdata$Y1[3] <- simdata$Y2[4] <- simdata$Y3[4] <- NA
     > emResult <- emNorm( cbind(Y1,Y2,Y3) ~ X1, data=simdata )
     > print( summary( emResult ) )
     Predictor (X) variables:
     Mean SD Observed Missing Pct.Missing
     (Intercept) 1.0000000 0.000000 6 0 0
     X1 0.2068512 1.178512 6 0 0
    
     Response (Y) variables:
     Mean SD Observed Missing Pct.Missing
     Y1 -0.4680307 1.2630567 5 1 16.66667
     Y2 -0.6322806 0.2081665 5 1 16.66667
     Y3 -0.2349012 0.7653216 5 1 16.66667
    
     Missingness patterns for response (Y) variables
     (. denotes observed value, m denotes missing value)
     (variable names are displayed vertically)
     (rightmost column is the frequency):
     YYY
     123
     ... 4
     .mm 1
     m.. 1
    
     Method: EM
     Prior: "uniform"
     Convergence criterion: 1e-05
     Iterations: 114
     Converged: TRUE
     Max. rel. difference: 9.8836e-06
     -2 Loglikelihood: -10.70465
     -2 Log-posterior density: -10.70465
     Worst fraction missing information: 0.9313
    
     Estimated coefficients (beta):
     Y1 Y2 Y3
     (Intercept) -1.0195738 -0.61863204 -0.1874030
     X1 0.5166042 -0.01611584 0.1214564
    
     Estimated covariance matrix (sigma):
     Y1 Y2 Y3
     Y1 1.80844106 -0.06800034 -0.75502100
     Y2 -0.06800034 0.03443290 0.05759078
     Y3 -0.75502100 0.05759078 0.41668488
     >
     > ## redundant Y-variable
     > simdata$Y3 <- simdata$Y1 + simdata$Y2
     > emResult <- emNorm( cbind(Y1,Y2,Y3) ~ X1, data=simdata )
    
     *** caught segfault ***
     address 0, cause 'memory not mapped'
    
     Traceback:
     1: emNorm.default(y, x = x, intercept = FALSE, iter.max = iter.max, criterion = criterion, estimate.worst = estimate.worst, starting.values = starting.values, prior = prior, prior.df = prior.df, prior.sscp = prior.sscp, ...)
     2: emNorm.formula(cbind(Y1, Y2, Y3) ~ X1, data = simdata)
     3: emNorm(cbind(Y1, Y2, Y3) ~ X1, data = simdata)
     An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86