CRAN Package Check Results for Package ggeffects

Last updated on 2019-11-09 08:48:34 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.12.0 9.43 347.73 357.16 ERROR
r-devel-linux-x86_64-debian-gcc 0.12.0 8.53 257.11 265.64 OK
r-devel-linux-x86_64-fedora-clang 0.13.0 402.13 NOTE
r-devel-linux-x86_64-fedora-gcc 0.13.0 410.98 NOTE
r-devel-windows-ix86+x86_64 0.13.0 4.00 6.00 10.00 ERROR
r-patched-linux-x86_64 0.12.0 9.50 324.59 334.09 OK
r-patched-solaris-x86 0.13.0 528.70 NOTE
r-release-linux-x86_64 0.12.0 9.82 330.43 340.25 OK
r-release-windows-ix86+x86_64 0.12.0 33.00 455.00 488.00 OK
r-release-osx-x86_64 0.12.0 OK
r-oldrel-windows-ix86+x86_64 0.12.0 20.00 437.00 457.00 OK
r-oldrel-osx-x86_64 0.12.0 OK

Check Details

Version: 0.12.0
Check: tests
Result: ERROR
     Running 'testthat.R' [136s/158s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     4 term additive + random effectGu & Wahba 4 term additive model
    
     Maximum number of PQL iterations: 20
     -- 1. Error: (unknown) (@test-gamm.R#10) --------------------------------------
     WEIRD RETURN VALUE: 0x4
     Backtrace:
     1. mgcv::gamm(...)
     2. mgcv::extract.lme.cov2(ret$lme, mf, n.sr + 1)
     4. mgcv::tensor.prod.model.matrix(X)
    
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     Can't compute marginal effects, 'effects::Effect()' returned an error.
    
     Reason: no applicable method for 'vcov' applied to an object of class "lmRob"
     You may try 'ggpredict()' or 'ggemmeans()'.
    
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: Can't handle an object of class "lmRob"
     Use help("models", package = "emmeans") for information on supported models.
     You may try 'ggpredict()' or 'ggeffect()'.
    
     Can't compute marginal effects, 'effects::Effect()' returned an error.
    
     Reason: object 'neg_c_7d' not found
     You may try 'ggpredict()' or 'ggemmeans()'.
    
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: Can't handle an object of class "glmrob"
     Use help("models", package = "emmeans") for information on supported models.
     You may try 'ggpredict()' or 'ggeffect()'.
    
    
     # Predicted values of Total score BARTHEL INDEX
     # x = average number of hours of care per week
    
     # c161sex = Male
     # c172code = [1] low level of education
     x predicted std.error conf.low conf.high
     0 73.954 2.347 69.354 78.554
     45 62.556 2.208 58.228 66.883
     85 52.424 2.310 47.896 56.951
     170 30.893 3.085 24.847 36.939
    
     # c161sex = Female
     # c172code = [1] low level of education
     x predicted std.error conf.low conf.high
     0 74.996 1.831 71.406 78.585
     45 63.597 1.603 60.456 66.738
     85 53.465 1.702 50.130 56.800
     170 31.934 2.606 26.827 37.042
    
     # c161sex = Male
     # c172code = [2] intermediate level of education
     x predicted std.error conf.low conf.high
     0 74.673 1.845 71.055 78.290
     45 63.274 1.730 59.883 66.665
     85 53.142 1.911 49.397 56.887
     170 31.611 2.872 25.982 37.241
    
     # c161sex = Female
     # c172code = [2] intermediate level of education
     x predicted std.error conf.low conf.high
     0 75.714 1.225 73.313 78.115
     45 64.315 0.968 62.418 66.213
     85 54.183 1.209 51.815 56.552
     170 32.653 2.403 27.943 37.362
    
     # c161sex = Male
     # c172code = [3] high level of education
     x predicted std.error conf.low conf.high
     0 75.391 2.220 71.040 79.741
     45 63.992 2.176 59.727 68.258
     85 53.860 2.364 49.226 58.494
     170 32.330 3.257 25.946 38.713
    
     # c161sex = Female
     # c172code = [3] high level of education
     x predicted std.error conf.low conf.high
     0 76.432 1.809 72.887 79.977
     45 65.034 1.712 61.679 68.388
     85 54.902 1.910 51.158 58.646
     170 33.371 2.895 27.697 39.045
    
     Adjusted for:
     * neg_c_7 = 11.84
    
    
     # Predicted values of Total score BARTHEL INDEX
     # x = average number of hours of care per week
    
     # c161sex = Male
     # neg_c_7 = 11.8
     x predicted std.error conf.low conf.high
     0 74.739 1.847 71.119 78.358
     45 63.340 1.731 59.948 66.732
     85 53.208 1.911 49.464 56.953
     170 31.678 2.871 26.050 37.305
    
     # c161sex = Female
     # neg_c_7 = 11.8
     x predicted std.error conf.low conf.high
     0 75.780 1.225 73.379 78.182
     45 64.382 0.967 62.487 66.276
     85 54.250 1.206 51.885 56.614
     170 32.719 2.400 28.014 37.424
    
     # c161sex = Male
     # neg_c_7 = 15.7
     x predicted std.error conf.low conf.high
     0 65.780 2.165 61.536 70.023
     45 54.381 1.980 50.501 58.261
     85 44.249 2.064 40.203 48.295
     170 22.718 2.861 17.110 28.326
    
     # c161sex = Female
     # neg_c_7 = 15.7
     x predicted std.error conf.low conf.high
     0 66.821 1.590 63.704 69.938
     45 55.422 1.268 52.936 57.908
     85 45.290 1.347 42.649 47.931
     170 23.760 2.336 19.182 28.338
    
     # c161sex = Male
     # neg_c_7 = 8
     x predicted std.error conf.low conf.high
     0 83.468 1.910 79.724 87.213
     45 72.070 1.892 68.362 75.777
     85 61.938 2.130 57.762 66.113
     170 40.407 3.128 34.277 46.537
    
     # c161sex = Female
     # neg_c_7 = 8
     x predicted std.error conf.low conf.high
     0 84.510 1.408 81.749 87.270
     45 73.111 1.327 70.510 75.712
     85 62.979 1.608 59.827 66.131
     170 41.448 2.747 36.065 46.832
    
     Adjusted for:
     * c172code = 1.97
    
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: Can't handle an object of class "lmrob"
     Use help("models", package = "emmeans") for information on supported models.
     You may try 'ggpredict()' or 'ggeffect()'.
    
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: Can't handle an object of class "logistf"
     Use help("models", package = "emmeans") for information on supported models.
     You may try 'ggpredict()' or 'ggeffect()'.
    
     Can't compute marginal effects, 'effects::Effect()' returned an error.
    
     Reason: object 'neg_c_7d' not found
     You may try 'ggpredict()' or 'ggemmeans()'.
    
     Error: Confidence intervals could not be computed.
     * Reason: "`Type` does not have enough factor levels. Try to remove `[Tower]`."
     * Source: .safe_se_from_vcov(model, fitfram, typical, terms, model.class, type, vcov.fun, vcov.type, vcov.args, condition, interval)
     Error: Confidence intervals could not be computed.
     * Reason: "`Type` does not have enough factor levels. Try to remove `[Terrace]`."
     * Source: .safe_se_from_vcov(model, fitfram, typical, terms, model.class, type, vcov.fun, vcov.type, vcov.args, condition, interval)
     Error: Confidence intervals could not be computed.
     * Reason: "`Type` does not have enough factor levels. Try to remove `[Tower]`."
     * Source: .safe_se_from_vcov(model, fitfram, typical, terms, model.class, type, vcov.fun, vcov.type, vcov.args, condition, interval)
     Error: Confidence intervals could not be computed.
     * Reason: "`Type` does not have enough factor levels. Try to remove `[Terrace]`."
     * Source: .safe_se_from_vcov(model, fitfram, typical, terms, model.class, type, vcov.fun, vcov.type, vcov.args, condition, interval)
     Error: Confidence intervals could not be computed.
     * Reason: "`Type` does not have enough factor levels. Try to remove `[Tower]`."
     * Source: .safe_se_from_vcov(model, fitfram, typical, terms, model.class, type, vcov.fun, vcov.type, vcov.args, condition, interval)
     Can't compute marginal effects, 'effects::Effect()' returned an error.
    
     Reason: object does not have variance-covariance matrix
     You may try 'ggpredict()' or 'ggemmeans()'.
    
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: Can't handle an object of class "rq"
     Use help("models", package = "emmeans") for information on supported models.
     You may try 'ggpredict()' or 'ggeffect()'.
    
     Can't compute marginal effects, 'effects::Effect()' returned an error.
    
     Reason: Invalid operation on a survival time
     You may try 'ggpredict()' or 'ggemmeans()'.
    
     Can't compute marginal effects, 'effects::Effect()' returned an error.
    
     Reason: Invalid operation on a survival time
     You may try 'ggpredict()' or 'ggemmeans()'.
    
     VGLM linear loop 1 : deviance = 5.181115
     VGLM linear loop 2 : deviance = 5.129147
     VGLM linear loop 3 : deviance = 5.129141
     VGLM linear loop 4 : deviance = 5.129141
     == testthat results ===========================================================
     [ OK: 190 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 1 ]
     1. Error: (unknown) (@test-gamm.R#10)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.13.0
Check: dependencies in R code
Result: NOTE
    Namespace in Imports field not imported from: ‘utils’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86

Version: 0.13.0
Check: whether package can be installed
Result: ERROR
    Installation failed.
Flavor: r-devel-windows-ix86+x86_64