CRAN Package Check Results for Package metaSEM

Last updated on 2020-01-20 01:50:26 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.2.3.1 17.39 156.68 174.07 OK
r-devel-linux-x86_64-debian-gcc 1.2.3.1 14.47 109.66 124.13 OK
r-devel-linux-x86_64-fedora-clang 1.2.3.1 199.00 OK
r-devel-linux-x86_64-fedora-gcc 1.2.3.1 188.34 OK
r-devel-windows-ix86+x86_64 1.2.3.1 43.00 148.00 191.00 OK
r-devel-windows-ix86+x86_64-gcc8 1.2.3.1 27.00 220.00 247.00 OK
r-patched-linux-x86_64 1.2.3.1 14.44 125.84 140.28 OK
r-patched-solaris-x86 1.2.3.1 236.80 ERROR
r-release-linux-x86_64 1.2.3.1 14.08 127.18 141.26 OK
r-release-windows-ix86+x86_64 1.2.3.1 39.00 186.00 225.00 OK
r-release-osx-x86_64 1.2.3.1 OK
r-oldrel-windows-ix86+x86_64 1.2.3.1 19.00 126.00 145.00 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 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 0.10000000 NA NA NA NA NA
    Intercept2 0.10000000 NA NA NA NA NA
    Tau2_1_1 0.10000000 0.00089175 0.09825221 0.10174779 112.14 < 2.2e-16 ***
    Tau2_2_1 0.10000000 0.00032821 0.09935672 0.10064328 304.69 < 2.2e-16 ***
    Tau2_2_2 0.10000000 0.00098492 0.09806959 0.10193041 101.53 < 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) 0.8593
    Intercept2: I2 (Q statistic) 0.9020
    
    Number of studies (or clusters): 13
    Number of observed statistics: 26
    Number of estimated parameters: 5
    Degrees of freedom: 21
    -2 log likelihood: 2493.203
    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’ [16s/28s]
    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")
     ── 1. Failure: metaFIML() works correctly (@test_utilities.R#478) ─────────────
     `coef1a` not equal to `coef1b`.
     5/5 mismatches (average diff: 0.142)
     [1] 0.100 - 0.01560 == 0.08440
     [2] 0.100 - 0.00391 == 0.09609
     [3] 0.692 - 0.59805 == 0.09415
     [4] 0.100 - -0.32955 == 0.42955
     [5] 0.870 - 0.86395 == 0.00605
    
     ── 2. Failure: metaFIML() works correctly (@test_utilities.R#480) ─────────────
     fit1a$mx.fit$output$Minus2LogLikelihood not equal to fit1b$output$Minus2LogLikelihood.
     1/1 mismatches
     [1] 111 - -155 == 266
    
     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 4 rows, and argument #2 has 0 rows.>
     ── 3. 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 4 rows, and argument #2 has 0 rows.
     Backtrace:
     1. metaSEM::metaFIML(y = r, v = r_v, x = JP_alpha, av = IDV, data = Jaramillo05)
    
     Error: C stack usage 212374676 is too close to the limit
     <simpleError: The job for model 'TSSEM1 Correlation' exited abnormally with the error message: User interrupt>
     ── 4. Error: Handling NA in diagonals in tssem1FEM() correctly (@test_utilities.
     The job for model 'TSSEM1 Correlation' exited abnormally with the error message: User interrupt
     Backtrace:
     1. metaSEM::tssem1(Cov = list(C1, C2, C3), n = c(50, 50, 50), method = "FEM")
     2. metaSEM::tssem1FEM(...)
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 84 | SKIPPED: 0 | WARNINGS: 1 | FAILED: 4 ]
     1. Failure: metaFIML() works correctly (@test_utilities.R#478)
     2. Failure: metaFIML() works correctly (@test_utilities.R#480)
     3. Error: metaFIML() works correctly (@test_utilities.R#484)
     4. Error: Handling NA in diagonals in tssem1FEM() correctly (@test_utilities.R#577)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-solaris-x86