Last updated on 2019-05-01 01:51:52 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.0-0 | 35.98 | 256.54 | 292.52 | OK | |
| r-devel-linux-x86_64-debian-gcc | 2.0-0 | 32.33 | 174.52 | 206.85 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 2.0-0 | 356.94 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0-0 | 357.75 | OK | |||
| r-patched-linux-x86_64 | 2.0-0 | 33.94 | 254.09 | 288.03 | OK | |
| r-patched-solaris-x86 | 2.0-0 | 437.90 | OK | |||
| r-release-linux-x86_64 | 2.0-0 | 33.82 | 256.67 | 290.49 | OK | |
| r-release-windows-ix86+x86_64 | 2.0-0 | 69.00 | 333.00 | 402.00 | OK | |
| r-release-osx-x86_64 | 2.0-0 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 2.0-0 | 59.00 | 313.00 | 372.00 | OK | |
| r-oldrel-osx-x86_64 | 2.0-0 | OK |
Version: 2.0-0
Check: examples
Result: ERROR
Running examples in ‘metafor-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: dat.pritz1997
> ### Title: Studies on the Effectiveness of Hyperdynamic Therapy for
> ### Treating Cerebral Vasospasm
> ### Aliases: dat.pritz1997
> ### Keywords: datasets
>
> ### ** Examples
>
> ### load data
> dat <- get(data(dat.pritz1997))
>
> ### 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 in rma(yi, vi, weights = ni, method = "FE", data = dat) :
There are outcomes with non-positive sampling variances.
Warning in rma(yi, vi, weights = ni, method = "FE", data = dat) :
Cannot compute Q-test, I^2, or H^2 with non-positive sampling variances.
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 cr.lb cr.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 cr.lb cr.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 t(b2.QE) %*% chol2inv(chol.h) : non-conformable arguments
Calls: rma.glmm
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 2.0-0
Check: tests
Result: ERROR
Running ‘testthat.R’ [31s/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 2.0-0). 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: jackson2014: SS
Checking analysis example: konstantopoulos2011: ............................S.....SS
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: .............................S.....................S..............S
Checking analysis example: stijnen2010: ............S.......SS............S......S
Checking analysis example: vanhouwelingen1993: SSS
Checking analysis example: vanhouwelingen2002: ...........SS...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: escalc() function: ................................................................
Checking misc: computations of fit statistics: .......................
Checking misc: fsn() function: .........
Checking misc: funnel() functions: S
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: permutest() function: SSS
Checking misc: plot() function: SSS
Checking misc: predict() function: ......
Checking misc: regtest() and ranktest() functions: ........
Checking misc: residuals() function: .............1
Checking misc: proper handling of errors in rma(): ......
Checking misc: rma.glmm() function: ............
Checking misc: proper handling of missing values: 2
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 misc: robust() function: ......
Checking misc: .setlab() function: S
Checking misc: to.long() function: ..............
Checking misc: transformation functions: .......................
Checking misc: update() function: ....3
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: SSS
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: meta-analytic scatterplot: S
Checking plots example: normal QQ plots: SSS.
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#15) - On CRAN
2. confint() gives correct results for example 1 in Jackson et al. (2014). (@test_analysis_example_jackson2014.r#7) - On CRAN
3. confint() gives correct results for example 2 in Jackson et al. (2014). (@test_analysis_example_jackson2014.r#47) - On CRAN
4. profiling works for the three-level random-effects model (multilevel parameterization). (@test_analysis_example_konstantopoulos2011.r#109) - On CRAN
5. profiling works for the three-level random-effects model (multivariate parameterization). (@test_analysis_example_konstantopoulos2011.r#140) - On CRAN
6. BLUPs are calculated correctly for the three-level random-effects model (multilevel parameterization). (@test_analysis_example_konstantopoulos2011.r#155) - On CRAN
7. results are correct for example 1. (@test_analysis_example_law2016.r#20) - On CRAN
8. results are correct for example 2. (@test_analysis_example_law2016.r#93) - On CRAN
9. back-transformations work as intended for individual studies and the model estimate. (@test_analysis_example_miller1978.r#78) - On CRAN
10. results are correct for the random-effects model. (@test_analysis_example_raudenbush1985.r#38) - On CRAN
11. results are correct for the mixed-effects model. (@test_analysis_example_raudenbush1985.r#89) - On CRAN
12. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#132) - On CRAN
13. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#267) - On CRAN
14. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#360) - On CRAN
15. results for the binomial-normal normal are correct (measure=='PLO') (@test_analysis_example_stijnen2010.r#38) - On CRAN
16. results for the conditional logistic model with exact likelihood are correct (measure=='OR') (@test_analysis_example_stijnen2010.r#81) - On CRAN
17. results for the conditional logistic model with approximate likelihood are correct (measure=='OR') (@test_analysis_example_stijnen2010.r#99) - On CRAN
18. results for the Poisson-normal model are correct (measure=='IRLN') (@test_analysis_example_stijnen2010.r#151) - On CRAN
19. results for the Poisson-normal model are correct (measure=='IRR') (@test_analysis_example_stijnen2010.r#194) - On CRAN
20. the log likelihood plot can be created. (@test_analysis_example_vanhouwelingen1993.r#12) - On CRAN
21. results of the fixed-effects conditional logistic model are correct. (@test_analysis_example_vanhouwelingen1993.r#23) - On CRAN
22. results of the random-effects conditional logistic model are correct. (@test_analysis_example_vanhouwelingen1993.r#48) - On CRAN
23. profile plot for tau^2 can be drawn. (@test_analysis_example_vanhouwelingen2002.r#56) - On CRAN
24. forest plot of observed log(OR)s and corresponding BLUPs can be drawn. (@test_analysis_example_vanhouwelingen2002.r#70) - On CRAN
25. L'Abbe plot can be drawn. (@test_analysis_example_vanhouwelingen2002.r#109) - On CRAN
26. CI is correct for the profile likelihood method. (@test_analysis_example_viechtbauer2007a.r#73) - On CRAN
27. CI is correct for the parametric bootstrap method. (@test_analysis_example_viechtbauer2007a.r#110) - On CRAN
28. CI is correct for the non-parametric bootstrap method. (@test_analysis_example_viechtbauer2007a.r#135) - On CRAN
29. results are correct for the mixed-effects model. (@test_analysis_example_viechtbauer2007b.r#76) - On CRAN
30. log likelihood plot can be drawn. (@test_analysis_example_yusuf1985.r#13) - On CRAN
31. funnel() works correctly. (@test_misc_funnel.r#7) - On CRAN
32. rma.peto(), rma.mh(), and rma.glmm() handle outcome1 never occurring properly. (@test_misc_handling_of_edge_cases_due_to_zeros.r#21) - On CRAN
33. rma.peto(), rma.mh(), and rma.glmm() handle outcome2 never occurring properly. (@test_misc_handling_of_edge_cases_due_to_zeros.r#43) - On CRAN
34. permutest() gives correct results for a random-effects model. (@test_misc_permutest.r#13) - On CRAN
35. permutest() gives correct results for a mixed-effects model. (@test_misc_permutest.r#43) - On CRAN
36. permutest() gives correct results for example in Follmann & Proschan (1999). (@test_misc_permutest.r#67) - On CRAN
37. plot can be drawn for rma(). (@test_misc_plot_rma.r#5) - Empty test
38. plot can be drawn for rma.mh(). (@test_misc_plot_rma.r#17) - Empty test
39. plot can be drawn for rma.peto(). (@test_misc_plot_rma.r#25) - Empty test
40. .setlab() works correctly together with forest(). (@test_misc_setlab.r#8) - Empty test
41. plot can be drawn. (@test_plots_baujat_plot.r#7) - Empty test
42. plot can be drawn. (@test_plots_caterpillar_plot.r#7) - Empty test
43. plot can be drawn. (@test_plots_contour-enhanced_funnel_plot.r#7) - Empty test
44. plot can be drawn. (@test_plots_cumulative_forest_plot.r#7) - Empty test
45. plot can be drawn. (@test_plots_cumulative_forest_plot.r#43) - Empty test
46. plot can be drawn. (@test_plots_cumulative_forest_plot.r#76) - Empty test
47. plot can be drawn. (@test_plots_forest_plot_with_subgroups.r#7) - Empty test
48. plot can be drawn. (@test_plots_funnel_plot_variations.r#7) - Empty test
49. plot can be drawn. (@test_plots_funnel_plot_with_trim_and_fill.r#7) - Empty test
50. plot can be drawn. (@test_plots_gosh.r#7) - Empty test
51. plot can be drawn. (@test_plots_labbe_plot.r#7) - Empty test
52. plot can be drawn. (@test_plots_meta-analytic_scatterplot.r#7) - Empty test
53. plot can be drawn for rma(). (@test_plots_normal_qq_plots.r#7) - Empty test
54. plot can be drawn for rma.mh(). (@test_plots_normal_qq_plots.r#43) - Empty test
55. plot can be drawn for rma.peto(). (@test_plots_normal_qq_plots.r#56) - Empty test
56. plot can be drawn. (@test_plots_plot_of_cumulative_results.r#7) - Empty test
57. plot can be drawn. (@test_plots_plot_of_influence_diagnostics.r#7) - Empty test
58. plot can be drawn. (@test_plots_radial_plot.r#7) - Empty test
══ Failed ══════════════════════════════════════════════════════════════════════
── 1. Error: residuals are correct for rma.glmm(). (@test_misc_residuals.r#63)
non-conformable arguments
1: rma.glmm(measure = "OR", ai = tpos, bi = tneg, ci = cpos, di = cneg, data = dat.bcg,
subset = 1:6) at testthat/test_misc_residuals.r:63
── 2. Error: rma.glmm() handles NAs correctly. (@test_misc_rma_handling_nas.r#22
non-conformable arguments
1: expect_warning(res <- rma.glmm(measure = "PLO", xi = xi, ni = ni, mods = ~mod1, data = dat)) at testthat/test_misc_rma_handling_nas.r:22
2: quasi_capture(enquo(object), label, capture_warnings)
3: .capture(act$val <- eval_bare(get_expr(.quo), get_env(.quo)), ...)
4: withCallingHandlers(code, warning = function(condition) {
out$push(condition)
invokeRestart("muffleWarning")
})
5: eval_bare(get_expr(.quo), get_env(.quo))
6: rma.glmm(measure = "PLO", xi = xi, ni = ni, mods = ~mod1, data = dat)
── 3. Error: update() works for rma.glmm(). (@test_misc_update.r#42) ──────────
non-conformable arguments
1: rma.glmm(measure = "OR", ai = tpos, bi = tneg, ci = cpos, di = cneg, data = dat.bcg,
method = "FE") at testthat/test_misc_update.r:42
══ DONE ════════════════════════════════════════════════════════════════════════
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc