CRAN Package Check Results for Package metaSEM

Last updated on 2020-04-25 01:53:07 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.2.3.1 16.44 156.03 172.47 OK
r-devel-linux-x86_64-debian-gcc 1.2.3.1 12.86 109.78 122.64 OK
r-devel-linux-x86_64-fedora-clang 1.2.3.1 198.39 OK
r-devel-linux-x86_64-fedora-gcc 1.2.3.1 191.28 OK
r-devel-windows-ix86+x86_64 1.2.3.1 42.00 148.00 190.00 OK
r-patched-linux-x86_64 1.2.3.1 16.85 138.35 155.20 OK
r-patched-osx-x86_64 1.2.3.1 OK
r-patched-solaris-x86 1.2.3.1 249.60 ERROR
r-release-linux-x86_64 1.2.3.1 16.02 126.31 142.33 OK
r-release-windows-ix86+x86_64 1.2.3.1 43.00 133.00 176.00 OK
r-release-osx-x86_64 1.2.3.1 OK
r-oldrel-windows-ix86+x86_64 1.2.3.1 22.00 171.00 193.00 OK
r-oldrel-osx-x86_64 1.2.3.1 OK

Check Details

Version: 1.2.3.1
Check: examples
Result: ERROR
    Running examples in ‘metaSEM-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: BCG
    > ### Title: Dataset on the Effectiveness of the BCG Vaccine for Preventing
    > ### Tuberculosis
    > ### Aliases: BCG
    > ### Keywords: datasets
    >
    > ### ** Examples
    >
    > data(BCG)
    >
    > ## Univariate meta-analysis on the log of the odds ratio
    > summary( meta(y=ln_OR, v=v_ln_OR, data=BCG,
    + x=cbind(scale(Latitude,scale=FALSE),
    + scale(Year,scale=FALSE))) )
    
    Call:
    meta(y = ln_OR, v = v_ln_OR, x = cbind(scale(Latitude, scale = FALSE),
     scale(Year, scale = FALSE)), data = BCG)
    
    95% confidence intervals: z statistic approximation (robust=FALSE)
    Coefficients:
     Estimate Std.Error lbound ubound z value Pr(>|z|)
    Intercept1 -0.7166884 NA NA NA NA NA
    Slope1_1 -0.0335019 NA NA NA NA NA
    Slope1_2 -0.0013515 0.0043422 -0.0098620 0.0071590 -0.3112 0.7556
    Tau2_1_1 0.0020944 0.0043420 -0.0064157 0.0106045 0.4824 0.6296
    
    Q statistic on the homogeneity of effect sizes: 163.1649
    Degrees of freedom of the Q statistic: 12
    P value of the Q statistic: 0
    
    Explained variances (R2):
     y1
    Tau2 (no predictor) 0.3025
    Tau2 (with predictors) 0.0021
    R2 0.9931
    
    Number of studies (or clusters): 13
    Number of observed statistics: 13
    Number of estimated parameters: 4
    Degrees of freedom: 9
    -2 log likelihood: 13.89208
    OpenMx status1: 6 ("0" or "1": The optimization is considered fine.
    Other values may indicate problems.)
    Warning in print.summary.meta(x) :
     OpenMx status1 is neither 0 or 1. You are advised to 'rerun' it again.
    
    >
    > ## Multivariate meta-analysis on the log of the odds
    > ## The conditional sampling covariance is 0
    > bcg <- meta(y=cbind(ln_Odd_V, ln_Odd_NV), data=BCG,
    + v=cbind(v_ln_Odd_V, cov_V_NV, v_ln_Odd_NV))
    > summary(bcg)
    
    Call:
    meta(y = cbind(ln_Odd_V, ln_Odd_NV), v = cbind(v_ln_Odd_V, cov_V_NV,
     v_ln_Odd_NV), data = BCG)
    
    95% confidence intervals: z statistic approximation (robust=FALSE)
    Coefficients:
     Estimate Std.Error lbound ubound z value Pr(>|z|)
    Intercept1 -4.833744 NA NA NA NA NA
    Intercept2 -4.095975 NA NA NA NA NA
    Tau2_1_1 1.431371 0.155074 1.127431 1.735310 9.2302 < 2.2e-16 ***
    Tau2_2_1 1.757327 0.034542 1.689626 1.825027 50.8755 < 2.2e-16 ***
    Tau2_2_2 2.407333 0.265609 1.886749 2.927916 9.0635 < 2.2e-16 ***
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    
    Q statistic on the homogeneity of effect sizes: 5270.386
    Degrees of freedom of the Q statistic: 24
    P value of the Q statistic: 0
    
    Heterogeneity indices (based on the estimated Tau2):
     Estimate
    Intercept1: I2 (Q statistic) 0.9887
    Intercept2: I2 (Q statistic) 0.9955
    
    Number of studies (or clusters): 13
    Number of observed statistics: 26
    Number of estimated parameters: 5
    Degrees of freedom: 21
    -2 log likelihood: 66.17587
    OpenMx status1: 6 ("0" or "1": The optimization is considered fine.
    Other values may indicate problems.)
    Warning in print.summary.meta(x) :
     OpenMx status1 is neither 0 or 1. You are advised to 'rerun' it again.
    
    >
    > plot(bcg)
    Warning in .solve(x = object$mx.fit@output$calculatedHessian, parameters = my.name) :
     Error in solving the Hessian matrix. Generalized inverse is used. The standard errors may not be trustworthy.
    
    Warning in sqrt(c(x[xind, xind], x[yind, yind])) : NaNs produced
    Error in if (scale[1] > 0) r <- r/scale[1] :
     missing value where TRUE/FALSE needed
    Calls: plot -> plot.meta -> points -> ellipse -> ellipse.default
    Execution halted
Flavor: r-patched-solaris-x86

Version: 1.2.3.1
Check: tests
Result: ERROR
     Running ‘testthat.R’ [0m/180m]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(metaSEM)
     Loading required package: OpenMx
     To take full advantage of multiple cores, use:
     mxOption(key='Number of Threads', value=parallel::detectCores()) #now
     Sys.setenv(OMP_NUM_THREADS=parallel::detectCores()) #before library(OpenMx)
     "SLSQP" is set as the default optimizer in OpenMx.
     mxOption(NULL, "Gradient algorithm") is set at "central".
     mxOption(NULL, "Optimality tolerance") is set at "6.3e-14".
     mxOption(NULL, "Gradient iterations") is set at "2".
     >
     > test_check("metaSEM")
    
     *** caught segfault ***
     address 6f756e69, cause 'memory not mapped'
Flavor: r-patched-solaris-x86