CRAN Package Check Results for Package metaSEM

Last updated on 2019-10-16 22:56:02 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.2.3 15.18 141.25 156.43 OK
r-devel-linux-x86_64-debian-gcc 1.2.3 13.44 99.96 113.40 OK
r-devel-linux-x86_64-fedora-clang 1.2.3 179.37 OK
r-devel-linux-x86_64-fedora-gcc 1.2.3 172.20 OK
r-devel-windows-ix86+x86_64 1.2.3 44.00 139.00 183.00 OK
r-patched-linux-x86_64 1.2.3 13.79 126.90 140.69 OK
r-patched-solaris-x86 1.2.3 222.90 ERROR
r-release-linux-x86_64 1.2.3 14.18 125.75 139.93 OK
r-release-windows-ix86+x86_64 1.2.3 28.00 143.00 171.00 OK
r-release-osx-x86_64 1.2.3 OK
r-oldrel-windows-ix86+x86_64 1.2.3 19.00 118.00 137.00 OK
r-oldrel-osx-x86_64 1.2.3 OK

Check Details

Version: 1.2.3
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 0.0026632 -0.0387217 -0.0282822 -12.5796 <2e-16 ***
    Slope1_2 -0.0013515 0.0048158 -0.0107903 0.0080873 -0.2806 0.7790
    Tau2_1_1 0.0020944 0.0064325 -0.0105131 0.0147019 0.3256 0.7447
    ---
    Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    
    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
Check: tests
Result: ERROR
     Running ‘testthat.R’ [0m/153m]
    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")
     Error in running mxModel:
     <simpleError: The job for model 'No predictor' exited abnormally with the error message: Non-conformable matrices in horizontal concatenation (cbind). First argument has 0 rows, and argument #2 has 3 rows.>
     ── 1. Failure: metaFIML() works correctly (@test_utilities.R#479) ─────────────
     vcov(fit1a) not equal to vcov(fit1b)[names1, names1].
     25/25 mismatches (average diff: NaN)
     [1] 2.26e-13 - NA == NA
     [2] 7.70e-12 - NA == NA
     [3] 1.32e-11 - NA == NA
     [4] 1.37e-11 - NA == NA
     [5] -4.06e-12 - NA == NA
     [6] 7.70e-12 - NA == NA
     [7] 2.62e-10 - NA == NA
     [8] 3.04e-11 - NA == NA
     [9] 4.13e-11 - NA == NA
     ...
    
     Error in running mxModel:
     <simpleError: The job for model 'No predictor' exited abnormally with the error message: Non-conformable matrices in horizontal concatenation (cbind). First argument has 4 rows, and argument #2 has 0 rows.>
     Error in running mxModel:
     <simpleError: The job for model 'Meta analysis with FIML' exited abnormally with the error message: Non-conformable matrices in horizontal concatenation (cbind). First argument has 0 rows, and argument #2 has 4 rows.>
     ── 2. Error: metaFIML() works correctly (@test_utilities.R#484) ───────────────
     The job for model 'Meta analysis with FIML' exited abnormally with the error message: Non-conformable matrices in horizontal concatenation (cbind). First argument has 0 rows, and argument #2 has 4 rows.
     1: metaFIML(y = r, v = r_v, x = JP_alpha, av = IDV, data = Jaramillo05) at testthat/test_utilities.R:484
     2: warning(print(mx.fit))
     3: withRestarts({
     .Internal(.signalCondition(cond, message, call))
     .Internal(.dfltWarn(message, call))
     }, muffleWarning = function() NULL)
     4: withOneRestart(expr, restarts[[1L]])
    
    
     *** caught segfault ***
     address fffffff7, cause 'memory not mapped'
    
     *** caught segfault ***
     address 121a5005, cause 'memory not mapped'
Flavor: r-patched-solaris-x86