Running ‘testthat.R’ [29s/43s]
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 'metafor' package (version 2.1-0). For an overview 
  and introduction to the package please type: help(metafor).
  > test_check("metafor", reporter="summary")
  Checking analysis example: berkey1995: ...........
  Checking analysis example: berkey1998: ..............
  Checking analysis example: dersimonian2007: S
  Checking analysis example: gleser2009: .......................
  Checking analysis example: henmi2010: .......
  Checking analysis example: ishak2007: .......................
  Checking analysis example: jackson2014: SS
  Checking analysis example: konstantopoulos2011: .............................S......SSS
  Checking analysis example: law2016: SS
  Checking analysis example: lipsey2001: .........................
  Checking analysis example: miller1978: ...........S
  Checking analysis example: morris2008: ..............
  Checking analysis example: normand1999: ..............................
  Checking analysis example: raudenbush1985: ..........S.............S
  Checking analysis example: raudenbush2009: ..................
  Checking analysis example: rothman2008: .1...........................S.2...................S.3............S
  Checking analysis example: stijnen2010: ............S.......SS............S......S
  Checking analysis example: vanhouwelingen1993: SSS
  Checking analysis example: vanhouwelingen2002: ............S.S....S.....................
  Checking analysis example: viechtbauer2005: ........
  Checking analysis example: viechtbauer2007a: .....S...SS
  Checking analysis example: viechtbauer2007b: ............S
  Checking analysis example: yusuf1985: S.....
  Checking misc: anova() function: ...........
  Checking misc: confint() function: ......
  Checking misc: model diagnostic functions for rma.mv(): SS
  Checking misc: escalc() function: ...............................................................................................
  Checking misc: computations of fit statistics: .......................
  Checking misc: fsn() function: .........
  Checking misc: funnel() functions: .S
  Checking misc: handling of NAs: ...............................................................................
  Checking misc: handling of edge cases due to zeros: .......S.......S
  Checking misc: influence() and related functions: .......................
  Checking misc: head.list.rma() and tail.list.rma() functions: ....
  Checking misc: rma.mh() against metan with 'dat.bcg': .....................
  Checking misc: rma.peto() against metan with 'dat.bcg': ........
  Checking misc: rma.uni() against metan with 'dat.bcg': .............................................
  Checking misc: pdfs of various measures: .....
  Checking misc: permutest() function: SSS
  Checking misc: plot() function: ...
  Checking misc: predict() function: ................
  Checking misc: regtest() and ranktest() functions: ........
  Checking misc: residuals() function: .....................S
  Checking misc: proper handling of errors in rma(): ......
  Checking misc: rma.glmm() function: ............SS
  Checking misc: proper handling of missing values: S
  Checking misc: rma.mv() function: ..........................
  Checking misc: rma() function: ..............
  Checking misc: rma() function with location-scale models: ...............
  Checking misc: rma.uni() against direct computations: .....
  Checking tip: rma() results match up with those from lm(): ........
  Checking misc: robust() function: ......
  Checking misc: .setlab() function: .
  Checking misc: to.long() function: ..............
  Checking misc: transformation functions: .......................
  Checking misc: update() function: ....S
  Checking misc: vcov() function: ........
  Checking misc: weights() function: ..........................
  Checking plots example: Baujat plot: .S
  Checking plots example: Caterpillar plot: .S
  Checking plots example: contour-enhanced funnel plot: .S
  Checking plots example: cumulative forest plot: .S.S.S
  Checking plots example: forest plot with subgroups: .S
  Checking plots example: funnel plot variations: .S
  Checking plots example: funnel plot with trim and fill: .S
  Checking plots example: GOSH plot: .S
  Checking plots example: L'Abbe plot: .S
  Checking plots example: Likelihood plot: .S
  Checking plots example: meta-analytic scatterplot: .S
  Checking plots example: normal QQ plots: .S.S.S.
  Checking plots example: plot of cumulative results: .S
  Checking plots example: plot of influence diagnostics: .S
  Checking plots example: radial (Galbraith) plot: .S
  Checking tip: rma() results match up with those from lm(): ............
  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) - Reason: On CRAN
  
  2. confint() gives correct results for example 1 in Jackson et al. (2014). (@test_analysis_example_jackson2014.r#9) - Reason: On CRAN
  
  3. confint() gives correct results for example 2 in Jackson et al. (2014). (@test_analysis_example_jackson2014.r#49) - Reason: On CRAN
  
  4. profiling works for the three-level random-effects model (multilevel parameterization). (@test_analysis_example_konstantopoulos2011.r#113) - Reason: On CRAN
  
  5. profiling works for the three-level random-effects model (multivariate parameterization). (@test_analysis_example_konstantopoulos2011.r#146) - Reason: On CRAN
  
  6. BLUPs are calculated correctly for the three-level random-effects model (multilevel parameterization). (@test_analysis_example_konstantopoulos2011.r#162) - Reason: On CRAN
  
  7. results are correct when allowing for different tau^2 per district. (@test_analysis_example_konstantopoulos2011.r#178) - Reason: On CRAN
  
  8. results are correct for example 1. (@test_analysis_example_law2016.r#22) - Reason: On CRAN
  
  9. results are correct for example 2. (@test_analysis_example_law2016.r#95) - Reason: On CRAN
  
  10. back-transformations work as intended for individual studies and the model estimate. (@test_analysis_example_miller1978.r#80) - Reason: On CRAN
  
  11. results are correct for the random-effects model. (@test_analysis_example_raudenbush1985.r#40) - Reason: On CRAN
  
  12. results are correct for the mixed-effects model. (@test_analysis_example_raudenbush1985.r#96) - Reason: On CRAN
  
  13. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#134) - Reason: On CRAN
  
  14. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#269) - Reason: On CRAN
  
  15. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#362) - Reason: On CRAN
  
  16. results for the binomial-normal normal are correct (measure=='PLO') (@test_analysis_example_stijnen2010.r#40) - Reason: On CRAN
  
  17. results for the conditional logistic model with exact likelihood are correct (measure=='OR') (@test_analysis_example_stijnen2010.r#83) - Reason: On CRAN
  
  18. results for the conditional logistic model with approximate likelihood are correct (measure=='OR') (@test_analysis_example_stijnen2010.r#101) - Reason: On CRAN
  
  19. results for the Poisson-normal model are correct (measure=='IRLN') (@test_analysis_example_stijnen2010.r#153) - Reason: On CRAN
  
  20. results for the Poisson-normal model are correct (measure=='IRR') (@test_analysis_example_stijnen2010.r#196) - Reason: On CRAN
  
  21. the log likelihood plot can be created. (@test_analysis_example_vanhouwelingen1993.r#14) - Reason: On CRAN
  
  22. results of the fixed-effects conditional logistic model are correct. (@test_analysis_example_vanhouwelingen1993.r#25) - Reason: On CRAN
  
  23. results of the random-effects conditional logistic model are correct. (@test_analysis_example_vanhouwelingen1993.r#50) - Reason: On CRAN
  
  24. profile plot for tau^2 can be drawn. (@test_analysis_example_vanhouwelingen2002.r#60) - Reason: On CRAN
  
  25. forest plot of observed log(OR)s and corresponding BLUPs can be drawn. (@test_analysis_example_vanhouwelingen2002.r#76) - Reason: On CRAN
  
  26. L'Abbe plot can be drawn. (@test_analysis_example_vanhouwelingen2002.r#117) - Reason: On CRAN
  
  27. CI is correct for the profile likelihood method. (@test_analysis_example_viechtbauer2007a.r#75) - Reason: On CRAN
  
  28. CI is correct for the parametric bootstrap method. (@test_analysis_example_viechtbauer2007a.r#112) - Reason: On CRAN
  
  29. CI is correct for the non-parametric bootstrap method. (@test_analysis_example_viechtbauer2007a.r#150) - Reason: On CRAN
  
  30. results are correct for the mixed-effects model. (@test_analysis_example_viechtbauer2007b.r#78) - Reason: On CRAN
  
  31. log likelihood plot can be drawn. (@test_analysis_example_yusuf1985.r#15) - Reason: On CRAN
  
  32. model diagnostic functions work with 'na.omit'. (@test_misc_diagnostics_rma.mv.r#29) - Reason: On CRAN
  
  33. model diagnostic functions work with 'na.pass'. (@test_misc_diagnostics_rma.mv.r#160) - Reason: On CRAN
  
  34. funnel() works correctly. (@test_misc_funnel.r#11) - 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) - 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) - Reason: On CRAN
  
  37. permutest() gives correct results for a random-effects model. (@test_misc_permutest.r#15) - Reason: On CRAN
  
  38. permutest() gives correct results for a mixed-effects model. (@test_misc_permutest.r#58) - Reason: On CRAN
  
  39. permutest() gives correct results for example in Follmann & Proschan (1999). (@test_misc_permutest.r#95) - Reason: On CRAN
  
  40. residuals are correct for rma.glmm(). (@test_misc_residuals.r#86) - Reason: On CRAN
  
  41. rma.glmm() works correctly when using 'clogit' or 'clogistic'. (@test_misc_rma_glmm.r#36) - Reason: On CRAN
  
  42. rma.glmm() works correctly when using 'nlminb' or 'minqa'. (@test_misc_rma_glmm.r#54) - Reason: On CRAN
  
  43. rma.glmm() handles NAs correctly. (@test_misc_rma_handling_nas.r#9) - Reason: On CRAN
  
  44. update() works for rma.glmm(). (@test_misc_update.r#39) - Reason: On CRAN
  
  45. plot can be drawn. (@test_plots_baujat_plot.r#11) - Reason: On CRAN
  
  46. plot can be drawn. (@test_plots_caterpillar_plot.r#11) - Reason: On CRAN
  
  47. plot can be drawn. (@test_plots_contour-enhanced_funnel_plot.r#11) - Reason: On CRAN
  
  48. plot can be drawn for 'rma.uni' object. (@test_plots_cumulative_forest_plot.r#11) - Reason: On CRAN
  
  49. plot can be drawn for 'rma.mh' object. (@test_plots_cumulative_forest_plot.r#49) - Reason: On CRAN
  
  50. plot can be drawn for 'rma.peto' object. (@test_plots_cumulative_forest_plot.r#84) - Reason: On CRAN
  
  51. plot can be drawn. (@test_plots_forest_plot_with_subgroups.r#11) - Reason: On CRAN
  
  52. plot can be drawn. (@test_plots_funnel_plot_variations.r#11) - Reason: On CRAN
  
  53. plot can be drawn. (@test_plots_funnel_plot_with_trim_and_fill.r#11) - Reason: On CRAN
  
  54. plot can be drawn. (@test_plots_gosh.r#11) - Reason: On CRAN
  
  55. plot can be drawn. (@test_plots_labbe_plot.r#11) - Reason: On CRAN
  
  56. plot can be drawn. (@test_plots_llplot.r#11) - Reason: On CRAN
  
  57. plot can be drawn. (@test_plots_meta-analytic_scatterplot.r#11) - Reason: On CRAN
  
  58. plot can be drawn for 'rma.uni' object. (@test_plots_normal_qq_plots.r#11) - Reason: On CRAN
  
  59. plot can be drawn for 'rma.mh' object. (@test_plots_normal_qq_plots.r#49) - Reason: On CRAN
  
  60. plot can be drawn for 'rma.peto' object. (@test_plots_normal_qq_plots.r#64) - Reason: On CRAN
  
  61. plot can be drawn. (@test_plots_plot_of_cumulative_results.r#11) - Reason: On CRAN
  
  62. plot can be drawn. (@test_plots_plot_of_influence_diagnostics.r#11) - Reason: On CRAN
  
  63. plot can be drawn. (@test_plots_radial_plot.r#11) - Reason: On CRAN
  
  ══ Failed ══════════════════════════════════════════════════════════════════════
  ── 1. Failure: the to.long() function works. (@test_analysis_example_rothman2008
  `tmp` not equivalent to `expected`.
  Component "age": target is character, current is factor
  Component "out1": target is character, current is factor
  Component "out2": target is character, current is factor
  
  ── 2. Failure: the to.long() function works. (@test_analysis_example_rothman2008
  `tmp` not equivalent to `expected`.
  Component "age": target is character, current is factor
  Component "events": target is character, current is factor
  Component "ptime": target is character, current is factor
  
  ── 3. Failure: the to.long() function works. (@test_analysis_example_rothman2008
  `tmp` not equivalent to `expected`.
  Component "age": target is character, current is factor
  Component "out1": target is character, current is factor
  Component "out2": target is character, current is factor
  
  ══ DONE ════════════════════════════════════════════════════════════════════════
  I believe in you!
  Error: Test failures
  Execution halted