CRAN Package Check Results for Package ggeffects

Last updated on 2019-12-14 15:47:09 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.13.0 10.55 355.66 366.21 ERROR
r-devel-linux-x86_64-debian-gcc 0.13.0 8.60 262.36 270.96 ERROR
r-devel-linux-x86_64-fedora-clang 0.13.0 438.13 ERROR
r-devel-linux-x86_64-fedora-gcc 0.13.0 441.46 ERROR
r-devel-windows-ix86+x86_64 0.13.0 21.00 411.00 432.00 ERROR
r-devel-windows-ix86+x86_64-gcc8 0.13.0 22.00 485.00 507.00 OK
r-patched-linux-x86_64 0.13.0 10.03 314.80 324.83 ERROR
r-patched-solaris-x86 0.13.0 530.40 ERROR
r-release-linux-x86_64 0.13.0 9.06 313.84 322.90 ERROR
r-release-windows-ix86+x86_64 0.13.0 17.00 310.00 327.00 ERROR
r-release-osx-x86_64 0.13.0 NOTE
r-oldrel-windows-ix86+x86_64 0.13.0 11.00 294.00 305.00 OK
r-oldrel-osx-x86_64 0.13.0 NOTE

Check Details

Version: 0.13.0
Check: tests
Result: ERROR
     Running 'testthat.R' [137s/145s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     Warning: stack imbalance in '<-', 66 then 67
     -- 1. Failure: ggpredict (@test-geeglm.R#17) ----------------------------------
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     -- 2. Failure: ggemmeans (@test-geeglm.R#22) ----------------------------------
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     Warning: stack imbalance in '{', 62 then 63
     Warning: stack imbalance in 'if', 60 then 61
     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()'.
    
     == testthat results ===========================================================
     [ OK: 160 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 2 ]
     1. Failure: ggpredict (@test-geeglm.R#17)
     2. Failure: ggemmeans (@test-geeglm.R#22)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.13.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [99s/148s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     ── 1. Failure: ggpredict (@test-geeglm.R#17) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     ── 2. Failure: ggemmeans (@test-geeglm.R#22) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     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()'.
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 160 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 2 ]
     1. Failure: ggpredict (@test-geeglm.R#17)
     2. Failure: ggemmeans (@test-geeglm.R#22)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

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, r-release-osx-x86_64, r-oldrel-osx-x86_64

Version: 0.13.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [163s/460s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     ── 1. Failure: ggpredict (@test-geeglm.R#17) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     ── 2. Failure: ggemmeans (@test-geeglm.R#22) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     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()'.
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 160 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 2 ]
     1. Failure: ggpredict (@test-geeglm.R#17)
     2. Failure: ggemmeans (@test-geeglm.R#22)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.13.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [167s/393s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     ── 1. Failure: ggpredict (@test-geeglm.R#17) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     ── 2. Failure: ggemmeans (@test-geeglm.R#22) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     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()'.
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 160 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 2 ]
     1. Failure: ggpredict (@test-geeglm.R#17)
     2. Failure: ggemmeans (@test-geeglm.R#22)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.13.0
Check: tests
Result: ERROR
     Running 'testthat.R' [146s]
    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
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: unused argument (mode = "response")
     You may try 'ggpredict()' or 'ggeffect()'.
    
     -- 1. Failure: ggemmeans (@test-betareg.R#19) ---------------------------------
     p$predicted[1] not equal to 0.3122091.
     target is NULL, current is numeric
    
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: unused argument (mode = "response")
     You may try 'ggpredict()' or 'ggeffect()'.
    
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: unused argument (mode = "link")
     You may try 'ggpredict()' or 'ggeffect()'.
    
     -- 2. Failure: ggemmeans (@test-coxph.R#19) -----------------------------------
     p$predicted[1] not equal to 0.5622074.
     target is NULL, current is numeric
    
     Can't compute marginal effects, 'emmeans::emmeans()' returned an error.
    
     Reason: unused argument (mode = "link")
     You may try 'ggpredict()' or 'ggeffect()'.
    
     (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, 'emmeans::emmeans()' returned an error.
    
     Reason: unused argument (mode = "link")
     You may try 'ggpredict()' or 'ggeffect()'.
    
     -- 3. Failure: ggemmeans, survreg (@test-survreg.R#23) ------------------------
     pr$predicted[1] not equal to 1637.551.
     target is NULL, current is numeric
    
     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, 'emmeans::emmeans()' returned an error.
    
     Reason: unused argument (mode = "link")
     You may try 'ggpredict()' or 'ggeffect()'.
    
     -- 4. Failure: ggemmeans, tobit (@test-tobit.R#23) ----------------------------
     pr$predicted[1] not equal to -10.45399.
     target is NULL, current is numeric
    
     == testthat results ===========================================================
     [ OK: 158 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 4 ]
     1. Failure: ggemmeans (@test-betareg.R#19)
     2. Failure: ggemmeans (@test-coxph.R#19)
     3. Failure: ggemmeans, survreg (@test-survreg.R#23)
     4. Failure: ggemmeans, tobit (@test-tobit.R#23)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-windows-ix86+x86_64

Version: 0.13.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [123s/133s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     Warning: stack imbalance in '<-', 66 then 67
     ── 1. Failure: ggpredict (@test-geeglm.R#17) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     ── 2. Failure: ggemmeans (@test-geeglm.R#22) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     Warning: stack imbalance in '{', 62 then 63
     Warning: stack imbalance in 'if', 60 then 61
     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()'.
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 160 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 2 ]
     1. Failure: ggpredict (@test-geeglm.R#17)
     2. Failure: ggemmeans (@test-geeglm.R#22)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-linux-x86_64

Version: 0.13.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [208s/249s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     ── 1. Failure: ggpredict (@test-geeglm.R#17) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     ── 2. Failure: ggemmeans (@test-geeglm.R#22) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     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()'.
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 160 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 2 ]
     1. Failure: ggpredict (@test-geeglm.R#17)
     2. Failure: ggemmeans (@test-geeglm.R#22)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-solaris-x86

Version: 0.13.0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [123s/132s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     ── 1. Failure: ggpredict (@test-geeglm.R#17) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     ── 2. Failure: ggemmeans (@test-geeglm.R#22) ──────────────────────────────────
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     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()'.
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     [ OK: 160 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 2 ]
     1. Failure: ggpredict (@test-geeglm.R#17)
     2. Failure: ggemmeans (@test-geeglm.R#22)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-release-linux-x86_64

Version: 0.13.0
Check: tests
Result: ERROR
     Running 'testthat.R' [113s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(ggeffects)
     >
     > if (length(strsplit(packageDescription("ggeffects")$Version, "\\.")[[1]]) > 3) {
     + Sys.setenv("RunAllggeffectsTests" = "yes")
     + } else {
     + Sys.setenv("RunAllggeffectsTests" = "no")
     + }
     >
     > test_check("ggeffects")
     GAM s.wam loop 1: deviance = 66.42095
     GAM s.wam loop 2: deviance = 63.77252
     GAM s.wam loop 3: deviance = 63.25199
     GAM s.wam loop 4: deviance = 63.13399
     GAM s.wam loop 5: deviance = 63.11016
     GAM s.wam loop 6: deviance = 63.10748
     GAM s.wam loop 7: deviance = 63.10727
     GAM s.wam loop 8: deviance = 63.10725
     GAM s.wam loop 9: deviance = 63.10725
     (Intercept) tensionM tensionH
     36.38889 -10.00000 -14.72222
     Warning: stack imbalance in '<-', 66 then 67
     -- 1. Failure: ggpredict (@test-geeglm.R#17) ----------------------------------
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     -- 2. Failure: ggemmeans (@test-geeglm.R#22) ----------------------------------
     p$predicted[1] not equal to 35.35779.
     1/1 mismatches
     [1] 35.5 - 35.4 == 0.119
    
     Warning: stack imbalance in '{', 62 then 63
     Warning: stack imbalance in 'if', 60 then 61
     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()'.
    
     == testthat results ===========================================================
     [ OK: 160 | SKIPPED: 44 | WARNINGS: 725 | FAILED: 2 ]
     1. Failure: ggpredict (@test-geeglm.R#17)
     2. Failure: ggemmeans (@test-geeglm.R#22)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-release-windows-ix86+x86_64