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