CRAN Package Check Results for Package metafor

Last updated on 2022-01-23 05:57:56 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 3.0-2 56.21 516.76 572.97 OK
r-devel-linux-x86_64-debian-gcc 3.0-2 54.03 393.15 447.18 OK
r-devel-linux-x86_64-fedora-clang 3.0-2 688.61 OK
r-devel-linux-x86_64-fedora-gcc 3.0-2 667.00 OK
r-devel-windows-x86_64-new-UL 3.0-2 230.00 534.00 764.00 OK
r-devel-windows-x86_64-new-TK 3.0-2 ERROR
r-patched-linux-x86_64 3.0-2 68.52 496.09 564.61 OK
r-release-linux-x86_64 3.0-2 59.09 498.14 557.23 OK
r-release-macos-arm64 3.0-2 NOTE
r-release-macos-x86_64 3.0-2 OK
r-release-windows-ix86+x86_64 3.0-2 99.00 546.00 645.00 OK
r-oldrel-macos-x86_64 3.0-2 OK
r-oldrel-windows-ix86+x86_64 3.0-2 101.00 559.00 660.00 OK

Check Details

Version: 3.0-2
Check: package dependencies
Result: NOTE
    Packages suggested but not available for checking: 'lme4', 'nloptr'
Flavor: r-devel-windows-x86_64-new-TK

Version: 3.0-2
Check: Rd cross-references
Result: NOTE
    Packages unavailable to check Rd xrefs: 'lme4', 'nloptr'
Flavor: r-devel-windows-x86_64-new-TK

Version: 3.0-2
Check: examples
Result: ERROR
    Running examples in 'metafor-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: dat.pritz1997
    > ### Title: Studies on the Effectiveness of Hyperdynamic Therapy for
    > ### Treating Cerebral Vasospasm
    > ### Aliases: dat.pritz1997
    > ### Keywords: datasets
    >
    > ### ** Examples
    >
    > ### copy data into 'dat' and examine data
    > dat <- dat.pritz1997
    > dat
     study authors xi ni
    1 1 Giannotta et al. 16 17
    2 2 Haraguchi and Ebina 10 12
    3 3 Swift and Solomon 4 8
    4 4 Kassell et al. 43 58
    5 5 Tanabe et al. 10 10
    6 6 Awad et al. 25 42
    7 7 Finn et al. 13 14
    8 8 Hadeishi et al. 12 12
    9 9 Otsubo et al. 22 41
    10 10 Muizelaar and Becker 4 5
    11 11 Rosenstein et al. 5 6
    12 12 Levy et al. 18 23
    13 13 Shimoda et al. 58 68
    14 14 Solomon et al. 6 10
    >
    > ### computation of "weighted average" in Zhou et al. (1999), Table IV
    > dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat, add=0)
    > theta.hat <- sum(dat$ni * dat$yi) / sum(dat$ni)
    > se.theta.hat <- sqrt(sum(dat$ni^2 * dat$vi) / sum(dat$ni)^2)
    > ci.lb <- theta.hat - 1.96 * se.theta.hat
    > ci.ub <- theta.hat + 1.96 * se.theta.hat
    > round(c(estimate = theta.hat, se = se.theta.hat, ci.lb = ci.lb, ci.ub = ci.ub), 4)
    estimate se ci.lb ci.ub
     0.7546 0.0224 0.7106 0.7986
    >
    > ### this is identical to a FE model with sample size weights
    > rma(yi, vi, weights=ni, method="FE", data=dat)
    Warning: There are outcomes with non-positive sampling variances.
    Warning: Cannot compute Q-test, I^2, or H^2 when there are non-positive sampling variances in the data.
    
    Fixed-Effects Model (k = 14)
    
    Model Results:
    
    estimate se zval pval ci.lb ci.ub
     0.7546 0.0224 33.6491 <.0001 0.7106 0.7986 ***
    
    ---
    Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
    >
    > ### random-effects model with raw proportions
    > dat <- escalc(measure="PR", xi=xi, ni=ni, data=dat)
    > res <- rma(yi, vi, data=dat)
    > predict(res)
    
     pred se ci.lb ci.ub pi.lb pi.ub
     0.7968 0.0423 0.7138 0.8797 0.5306 1.0629
    
    >
    > ### random-effects model with logit transformed proportions
    > dat <- escalc(measure="PLO", xi=xi, ni=ni, data=dat)
    > res <- rma(yi, vi, data=dat)
    > predict(res, transf=transf.ilogit)
    
     pred ci.lb ci.ub pi.lb pi.ub
     0.7575 0.6605 0.8337 0.4661 0.9179
    
    >
    > ### mixed-effects logistic regression model
    > res <- rma.glmm(measure="PLO", xi=xi, ni=ni, data=dat)
    Error in rma.glmm(measure = "PLO", xi = xi, ni = ni, data = dat) :
     Please install the 'lme4' package to fit this model.
    Execution halted
Flavor: r-devel-windows-x86_64-new-TK

Version: 3.0-2
Check: tests
Result: ERROR
     Running 'testthat.R'
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > ### to also run skip_on_cran() tests, uncomment:
     > #Sys.setenv(NOT_CRAN="true")
     >
     > library(testthat)
     > library(metafor)
     Loading required package: Matrix
    
     Loading the 'metafor' package (version 3.0-2). For an
     introduction to the package please type: help(metafor)
    
     > test_check("metafor", reporter="summary")
     analysis_example_berkey1995:
     Checking analysis example: berkey1995: ...........
     analysis_example_berkey1998:
     Checking analysis example: berkey1998: ..............
     analysis_example_dersimonian2007:
     Checking analysis example: dersimonian2007: S
     analysis_example_gleser2009:
     Checking analysis example: gleser2009: .......................
     analysis_example_henmi2010:
     Checking analysis example: henmi2010: .......
     analysis_example_ishak2007:
     Checking analysis example: ishak2007: .......................
     analysis_example_jackson2014:
     Checking analysis example: jackson2014: SS
     analysis_example_konstantopoulos2011:
     Checking analysis example: konstantopoulos2011: .............................S......SSS
     analysis_example_law2016:
     Checking analysis example: law2016: SS
     analysis_example_lipsey2001:
     Checking analysis example: lipsey2001: .........................
     analysis_example_miller1978:
     Checking analysis example: miller1978: ...........S
     analysis_example_morris2008:
     Checking analysis example: morris2008: ..............
     analysis_example_normand1999:
     Checking analysis example: normand1999: ..............................
     analysis_example_raudenbush1985:
     Checking analysis example: raudenbush1985: ..........S.............S
     analysis_example_raudenbush2009:
     Checking analysis example: raudenbush2009: ..................
     analysis_example_rothman2008:
     Checking analysis example: rothman2008: .............................S.....................S..............S
     analysis_example_stijnen2010:
     Checking analysis example: stijnen2010: ............S.......SS............S......S
     analysis_example_vanhouwelingen1993:
     Checking analysis example: vanhouwelingen1993: SSS
     analysis_example_vanhouwelingen2002:
     Checking analysis example: vanhouwelingen2002: ..............S.S....S.....................
     analysis_example_viechtbauer2005:
     Checking analysis example: viechtbauer2005: ........
     analysis_example_viechtbauer2007a:
     Checking analysis example: viechtbauer2007a: .....S...SS
     analysis_example_viechtbauer2007b:
     Checking analysis example: viechtbauer2007b: ............S
     analysis_example_yusuf1985:
     Checking analysis example: yusuf1985: S.....
     misc_aggregate:
     Checking misc: aggregate() function: ......
     misc_anova:
     Checking misc: anova() function: .............
     misc_confint:
     Checking misc: confint() function: ......
     misc_dfround:
     Checking misc: dfround() function: ..
     misc_diagnostics_rma.mv:
     Checking misc: model diagnostic functions for rma.mv(): SS
     misc_escalc:
     Checking misc: escalc() function: ...........................................................................................................
     misc_fitstats:
     Checking misc: computations of fit statistics: .......................
     misc_formula:
     Checking misc: formula() function: .......
     misc_fsn:
     Checking misc: fsn() function: .........
     misc_funnel:
     Checking misc: funnel() functions: .S
     misc_handling_nas:
     Checking misc: handling of NAs: ......................................................................................
     misc_handling_of_edge_cases_due_to_zeros:
     Checking misc: handling of edge cases due to zeros: .......S.......S
     misc_influence:
     Checking misc: influence() and related functions: .........................
     misc_list_rma:
     Checking misc: head.list.rma() and tail.list.rma() functions: ....
     misc_matreg:
     Checking misc: matreg() function: ....
     misc_metan_vs_rma.mh_with_dat.bcg:
     Checking misc: rma.mh() against metan with 'dat.bcg': .....................
     misc_metan_vs_rma.peto_with_dat.bcg:
     Checking misc: rma.peto() against metan with 'dat.bcg': ........
     misc_metan_vs_rma.uni_with_dat.bcg:
     Checking misc: rma.uni() against metan with 'dat.bcg': .............................................
     misc_pdfs:
     Checking misc: pdfs of various measures: .....
     misc_permutest:
     Checking misc: permutest() function: SSS
     misc_plot_rma:
     Checking misc: plot() function: ...
     misc_predict:
     Checking misc: predict() function: ...................
     misc_pub_bias:
     Checking misc: regtest() and ranktest() functions: ........
     misc_replmiss:
     Checking misc: replmiss() function: ...
     misc_reporter:
     Checking misc: reporter() function: S
     misc_residuals:
     Checking misc: residuals() function: .....................S
     misc_rma_error_handling:
     Checking misc: proper handling of errors in rma(): ......
     misc_rma_glmm:
     Checking misc: rma.glmm() function: 1SS
     misc_rma_handling_nas:
     Checking misc: proper handling of missing values: S
     misc_rma_ls:
     Checking misc: location-scale models: ......................................................................
     misc_rma_mv:
     Checking misc: rma.mv() function: .....................2
     misc_rma_uni:
     Checking misc: rma() function: ..............
     misc_rma_uni_ls:
     Checking misc: rma() function with location-scale models: ...............
     misc_rma_vs_direct_computation:
     Checking misc: rma.uni() against direct computations: .....
     misc_rma_vs_lm:
     Checking tip: rma() results match up with those from lm(): ........
     misc_robust:
     Checking misc: robust() function: ......
     misc_selmodel:
     Checking misc: selmodel() function: .S.S.S.S
     misc_setlab:
     Checking misc: .setlab() function: .
     misc_tes:
     Checking misc: tes() function: ..........
     misc_to_long_table_wide:
     Checking misc: to.long() function: ......................
     misc_transf:
     Checking misc: transformation functions: .......................
     misc_update:
     Checking misc: update() function: ....S
     misc_vcov:
     Checking misc: vcov() function: ........
     misc_vec2mat:
     Checking misc: vec2mat() function: ....
     misc_vif:
     Checking misc: vif() function: ...
     misc_weights:
     Checking misc: weights() function: ..........................
     plots_baujat_plot:
     Checking plots example: Baujat plot: .S
     plots_caterpillar_plot:
     Checking plots example: Caterpillar plot: .S
     plots_contour-enhanced_funnel_plot:
     Checking plots example: contour-enhanced funnel plot: .S
     plots_cumulative_forest_plot:
     Checking plots example: cumulative forest plot: .S.S.S
     plots_forest_plot_with_subgroups:
     Checking plots example: forest plot with subgroups: .S
     plots_funnel_plot_variations:
     Checking plots example: funnel plot variations: .S
     plots_funnel_plot_with_trim_and_fill:
     Checking plots example: funnel plot with trim and fill: .S
     plots_gosh:
     Checking plots example: GOSH plot: .S
     plots_labbe_plot:
     Checking plots example: L'Abbe plot: .S
     plots_llplot:
     Checking plots example: Likelihood plot: .S
     plots_meta-analytic_scatterplot:
     Checking plots example: meta-analytic scatterplot: .S
     plots_normal_qq_plots:
     Checking plots example: normal QQ plots: .S.S.S.
     plots_plot_of_cumulative_results:
     Checking plots example: plot of cumulative results: .S
     plots_plot_of_influence_diagnostics:
     Checking plots example: plot of influence diagnostics: .S
     plots_radial_plot:
     Checking plots example: radial (Galbraith) plot: .S
     tips_regression_with_rma:
     Checking tip: rma() results match up with those from lm(): ...........
     tips_rma_vs_lm_and_lme:
     Checking tip: rma() results match up with those from lm() and lme(): ..........
    
     ══ Skipped ═════════════════════════════════════════════════════════════════════
     1. results are correct for the CLASP example. (test_analysis_example_dersimonian2007.r:17:4) - Reason: On CRAN
    
     2. confint() gives correct results for example 1 in Jackson et al. (2014). (test_analysis_example_jackson2014.r:9:4) - Reason: On CRAN
    
     3. confint() gives correct results for example 2 in Jackson et al. (2014). (test_analysis_example_jackson2014.r:49:4) - Reason: On CRAN
    
     4. profiling works for the three-level random-effects model (multilevel parameterization). (test_analysis_example_konstantopoulos2011.r:113:4) - Reason: On CRAN
    
     5. profiling works for the three-level random-effects model (multivariate parameterization). (test_analysis_example_konstantopoulos2011.r:147:4) - Reason: On CRAN
    
     6. BLUPs are calculated correctly for the three-level random-effects model (multilevel parameterization). (test_analysis_example_konstantopoulos2011.r:163:4) - Reason: On CRAN
    
     7. results are correct when allowing for different tau^2 per district. (test_analysis_example_konstantopoulos2011.r:179:4) - Reason: On CRAN
    
     8. results are correct for example 1. (test_analysis_example_law2016.r:9:4) - Reason: On CRAN
    
     9. results are correct for example 2. (test_analysis_example_law2016.r:84:4) - Reason: On CRAN
    
     10. back-transformations work as intended for individual studies and the model estimate. (test_analysis_example_miller1978.r:80:4) - Reason: On CRAN
    
     11. results are correct for the random-effects model. (test_analysis_example_raudenbush1985.r:40:4) - Reason: On CRAN
    
     12. results are correct for the mixed-effects model. (test_analysis_example_raudenbush1985.r:96:4) - Reason: On CRAN
    
     13. results are correct for Mantel-Haenszel method. (test_analysis_example_rothman2008.r:133:4) - Reason: On CRAN
    
     14. results are correct for Mantel-Haenszel method. (test_analysis_example_rothman2008.r:269:4) - Reason: On CRAN
    
     15. results are correct for Mantel-Haenszel method. (test_analysis_example_rothman2008.r:363:4) - Reason: On CRAN
    
     16. results for the binomial-normal normal are correct (measure=='PLO') (test_analysis_example_stijnen2010.r:40:4) - Reason: On CRAN
    
     17. results for the conditional logistic model with exact likelihood are correct (measure=='OR') (test_analysis_example_stijnen2010.r:83:4) - Reason: On CRAN
    
     18. results for the conditional logistic model with approximate likelihood are correct (measure=='OR') (test_analysis_example_stijnen2010.r:101:4) - Reason: On CRAN
    
     19. results for the Poisson-normal model are correct (measure=='IRLN') (test_analysis_example_stijnen2010.r:153:4) - Reason: On CRAN
    
     20. results for the Poisson-normal model are correct (measure=='IRR') (test_analysis_example_stijnen2010.r:196:4) - Reason: On CRAN
    
     21. the log likelihood plot can be created. (test_analysis_example_vanhouwelingen1993.r:14:4) - Reason: On CRAN
    
     22. results of the fixed-effects conditional logistic model are correct. (test_analysis_example_vanhouwelingen1993.r:25:4) - Reason: On CRAN
    
     23. results of the random-effects conditional logistic model are correct. (test_analysis_example_vanhouwelingen1993.r:50:4) - Reason: On CRAN
    
     24. profile plot for tau^2 can be drawn. (test_analysis_example_vanhouwelingen2002.r:65:4) - Reason: On CRAN
    
     25. forest plot of observed log(OR)s and corresponding BLUPs can be drawn. (test_analysis_example_vanhouwelingen2002.r:80:4) - Reason: On CRAN
    
     26. L'Abbe plot can be drawn. (test_analysis_example_vanhouwelingen2002.r:119:4) - Reason: On CRAN
    
     27. CI is correct for the profile likelihood method. (test_analysis_example_viechtbauer2007a.r:75:4) - Reason: On CRAN
    
     28. CI is correct for the parametric bootstrap method. (test_analysis_example_viechtbauer2007a.r:112:4) - Reason: On CRAN
    
     29. CI is correct for the non-parametric bootstrap method. (test_analysis_example_viechtbauer2007a.r:150:4) - Reason: On CRAN
    
     30. results are correct for the mixed-effects model. (test_analysis_example_viechtbauer2007b.r:78:4) - Reason: On CRAN
    
     31. log likelihood plot can be drawn. (test_analysis_example_yusuf1985.r:15:4) - Reason: On CRAN
    
     32. model diagnostic functions work with 'na.omit'. (test_misc_diagnostics_rma.mv.r:29:4) - Reason: On CRAN
    
     33. model diagnostic functions work with 'na.pass'. (test_misc_diagnostics_rma.mv.r:160:4) - Reason: On CRAN
    
     34. funnel() works correctly. (test_misc_funnel.r:11:4) - Reason: On CRAN
    
     35. rma.peto(), rma.mh(), and rma.glmm() handle outcome1 never occurring properly. (test_misc_handling_of_edge_cases_due_to_zeros.r:23:4) - Reason: On CRAN
    
     36. rma.peto(), rma.mh(), and rma.glmm() handle outcome2 never occurring properly. (test_misc_handling_of_edge_cases_due_to_zeros.r:45:4) - Reason: On CRAN
    
     37. permutest() gives correct results for a random-effects model. (test_misc_permutest.r:15:4) - Reason: On CRAN
    
     38. permutest() gives correct results for a mixed-effects model. (test_misc_permutest.r:58:4) - Reason: On CRAN
    
     39. permutest() gives correct results for example in Follmann & Proschan (1999). (test_misc_permutest.r:95:4) - Reason: On CRAN
    
     40. reporter() works correctly for 'rma.uni' objects. (test_misc_reporter.r:9:4) - Reason: On CRAN
    
     41. residuals are correct for rma.glmm(). (test_misc_residuals.r:86:4) - Reason: On CRAN
    
     42. rma.glmm() works correctly when using 'clogit' or 'clogistic'. (test_misc_rma_glmm.r:36:4) - Reason: On CRAN
    
     43. rma.glmm() works correctly when using 'nlminb' or 'minqa'. (test_misc_rma_glmm.r:54:4) - Reason: On CRAN
    
     44. rma.glmm() handles NAs correctly. (test_misc_rma_handling_nas.r:9:4) - Reason: On CRAN
    
     45. results are correct for a step function model. (test_misc_selmodel.r:11:4) - Reason: On CRAN
    
     46. results are correct for the beta function model. (test_misc_selmodel.r:41:4) - Reason: On CRAN
    
     47. results are correct for the various exponential function models. (test_misc_selmodel.r:86:4) - Reason: On CRAN
    
     48. results are correct for a pirori chosen step function models. (test_misc_selmodel.r:140:4) - Reason: On CRAN
    
     49. update() works for rma.glmm(). (test_misc_update.r:47:4) - Reason: On CRAN
    
     50. plot can be drawn. (test_plots_baujat_plot.r:11:4) - Reason: On CRAN
    
     51. plot can be drawn. (test_plots_caterpillar_plot.r:11:4) - Reason: On CRAN
    
     52. plot can be drawn. (test_plots_contour-enhanced_funnel_plot.r:11:4) - Reason: On CRAN
    
     53. plot can be drawn for 'rma.uni' object. (test_plots_cumulative_forest_plot.r:11:4) - Reason: On CRAN
    
     54. plot can be drawn for 'rma.mh' object. (test_plots_cumulative_forest_plot.r:42:4) - Reason: On CRAN
    
     55. plot can be drawn for 'rma.peto' object. (test_plots_cumulative_forest_plot.r:70:4) - Reason: On CRAN
    
     56. plot can be drawn. (test_plots_forest_plot_with_subgroups.r:11:4) - Reason: On CRAN
    
     57. plot can be drawn. (test_plots_funnel_plot_variations.r:11:4) - Reason: On CRAN
    
     58. plot can be drawn. (test_plots_funnel_plot_with_trim_and_fill.r:11:4) - Reason: On CRAN
    
     59. plot can be drawn. (test_plots_gosh.r:11:4) - Reason: On CRAN
    
     60. plot can be drawn. (test_plots_labbe_plot.r:11:4) - Reason: On CRAN
    
     61. plot can be drawn. (test_plots_llplot.r:11:4) - Reason: On CRAN
    
     62. plot can be drawn. (test_plots_meta-analytic_scatterplot.r:11:4) - Reason: On CRAN
    
     63. plot can be drawn for 'rma.uni' object. (test_plots_normal_qq_plots.r:11:4) - Reason: On CRAN
    
     64. plot can be drawn for 'rma.mh' object. (test_plots_normal_qq_plots.r:49:4) - Reason: On CRAN
    
     65. plot can be drawn for 'rma.peto' object. (test_plots_normal_qq_plots.r:64:4) - Reason: On CRAN
    
     66. plot can be drawn. (test_plots_plot_of_cumulative_results.r:11:4) - Reason: On CRAN
    
     67. plot can be drawn. (test_plots_plot_of_influence_diagnostics.r:11:4) - Reason: On CRAN
    
     68. plot can be drawn. (test_plots_radial_plot.r:11:4) - Reason: On CRAN
    
     ══ Failed ══════════════════════════════════════════════════════════════════════
     ── 1. Error (test_misc_rma_glmm.r:11:4): rma.glmm() works correctly for 'UM.RS'
     Error in `rma.glmm(measure = "OR", ai = ai, n1i = n1i, ci = ci, n2i = n2i,
     data = dat, model = "UM.RS", method = "FE")`: Please install the 'lme4' package to fit this model.
     Backtrace:
     1. testthat::expect_warning(...) test_misc_rma_glmm.r:11:3
     6. metafor::rma.glmm(...)
    
     ── 2. Error (test_misc_rma_mv.r:110:4): rma.mv() works correctly with different
     Error in `rma.mv(yi, vi, random = ~1 | trial, data = dat, control = list(optimizer = "nloptr"))`: Please install the 'nloptr' package to use this optimizer.
     Backtrace:
     1. metafor::rma.mv(yi, vi, random = ~1 | trial, data = dat, control = list(optimizer = "nloptr")) test_misc_rma_mv.r:110:3
    
     ══ DONE ════════════════════════════════════════════════════════════════════════
     Error: Test failures
     Execution halted
Flavor: r-devel-windows-x86_64-new-TK

Version: 3.0-2
Check: installed package size
Result: NOTE
     installed size is 5.1Mb
     sub-directories of 1Mb or more:
     R 2.0Mb
     help 1.8Mb
Flavor: r-release-macos-arm64