Running ‘testthat.R’ [255s/312s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(CondIndTests)
  > 
  > test_check("CondIndTests")
  
  Method: KCI ; Setting 1 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 1 
  
  Method: InvariantEnvironmentPrediction ; Setting 1 
  
  Method: InvariantResidualDistributionTest ; Setting 1 
  
  Method: InvariantTargetPrediction ; Setting 1 
  
  Method: ResidualPredictionTest ; Setting 1 
  
  Method: KCI ; Setting 2 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 2 
  
  Method: InvariantEnvironmentPrediction ; Setting 2 
  
  Method: InvariantResidualDistributionTest ; Setting 2 
  
  Method: InvariantTargetPrediction ; Setting 2 
  Error in names(dat) <- object$term : 
    'names' attribute [1] must be the same length as the vector [0]
  
   *** reducing smoothing df
  Method: ResidualPredictionTest ; Setting 2 
  
  Method: KCI ; Setting 3 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 3 
  
  Method: InvariantEnvironmentPrediction ; Setting 3 
  
  Method: InvariantResidualDistributionTest ; Setting 3 
  
  Method: InvariantTargetPrediction ; Setting 3 
  
  Method: ResidualPredictionTest ; Setting 3 
  
  Method: KCI ; Setting 4 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 4 
  
  Method: InvariantEnvironmentPrediction ; Setting 4 
  
  Method: InvariantResidualDistributionTest ; Setting 4 
  
  Method: InvariantTargetPrediction ; Setting 4 
  Error in names(dat) <- object$term : 
    'names' attribute [1] must be the same length as the vector [0]
  
   *** reducing smoothing df
  Method: ResidualPredictionTest ; Setting 4 
  
  Method: KCI ; Setting 5 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 5 
  
  Method: InvariantEnvironmentPrediction ; Setting 5 
  
  Method: InvariantResidualDistributionTest ; Setting 5 
  
  Method: InvariantTargetPrediction ; Setting 5 
  
  Method: ResidualPredictionTest ; Setting 5 
  
  Method: KCI ; Setting 6 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 6 
  
  Method: InvariantEnvironmentPrediction ; Setting 6 
  
  Method: InvariantResidualDistributionTest ; Setting 6 
  
  Method: InvariantTargetPrediction ; Setting 6 
  Error in names(dat) <- object$term : 
    'names' attribute [1] must be the same length as the vector [0]
  
   *** reducing smoothing df
  Method: ResidualPredictionTest ; Setting 6 
  
  Method: KCI ; Setting 7 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 7 
  
  Method: InvariantEnvironmentPrediction ; Setting 7 
  
  Method: InvariantResidualDistributionTest ; Setting 7 
  
  Method: InvariantTargetPrediction ; Setting 7 
  
  Method: ResidualPredictionTest ; Setting 7 
  
  Method: KCI ; Setting 8 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 8 
  
  Method: InvariantEnvironmentPrediction ; Setting 8 
  
  Method: InvariantResidualDistributionTest ; Setting 8 
  
  Method: InvariantTargetPrediction ; Setting 8 
  Error in names(dat) <- object$term : 
    'names' attribute [1] must be the same length as the vector [0]
  
   *** reducing smoothing df
  Method: ResidualPredictionTest ; Setting 8 
  
  Method: KCI ; Setting 1 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 1 
  
  Method: InvariantEnvironmentPrediction ; Setting 1 
  
  Method: InvariantResidualDistributionTest ; Setting 1 
  
  Method: InvariantTargetPrediction ; Setting 1 
  
  Method: ResidualPredictionTest ; Setting 1 
  
  Method: KCI ; Setting 2 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 2 
  
  Method: InvariantEnvironmentPrediction ; Setting 2 
  
  Method: InvariantResidualDistributionTest ; Setting 2 
  
  Method: InvariantTargetPrediction ; Setting 2 
  
  Method: ResidualPredictionTest ; Setting 2 
  
  Method: KCI ; Setting 3 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 3 
  
  Method: InvariantEnvironmentPrediction ; Setting 3 
  
  Method: InvariantResidualDistributionTest ; Setting 3 
  
  Method: InvariantTargetPrediction ; Setting 3 
  
  Method: ResidualPredictionTest ; Setting 3 
  
  Method: KCI ; Setting 4 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 4 
  
  Method: InvariantEnvironmentPrediction ; Setting 4 
  
  Method: InvariantResidualDistributionTest ; Setting 4 
  
  Method: InvariantTargetPrediction ; Setting 4 
  
  Method: ResidualPredictionTest ; Setting 4 
  
  Method: KCI ; Setting 5 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 5 
  
  Method: InvariantEnvironmentPrediction ; Setting 5 
  
  Method: InvariantResidualDistributionTest ; Setting 5 
  
  Method: InvariantTargetPrediction ; Setting 5 
  
  Method: ResidualPredictionTest ; Setting 5 
  
  Method: KCI ; Setting 6 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 6 
  
  Method: InvariantEnvironmentPrediction ; Setting 6 
  
  Method: InvariantResidualDistributionTest ; Setting 6 
  
  Method: InvariantTargetPrediction ; Setting 6 
  Error in names(dat) <- object$term : 
    'names' attribute [1] must be the same length as the vector [0]
  
   *** reducing smoothing df
  Method: ResidualPredictionTest ; Setting 6 
  
  Method: KCI ; Setting 7 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 7 
  
  Method: InvariantEnvironmentPrediction ; Setting 7 
  
  Method: InvariantResidualDistributionTest ; Setting 7 
  
  Method: InvariantTargetPrediction ; Setting 7 
  
  Method: ResidualPredictionTest ; Setting 7 
  
  Method: KCI ; Setting 8 
  
  Method: InvariantConditionalQuantilePrediction ; Setting 8 
  
  Method: InvariantEnvironmentPrediction ; Setting 8 
  
  Method: InvariantResidualDistributionTest ; Setting 8 
  
  Method: InvariantTargetPrediction ; Setting 8 
  Error in names(dat) <- object$term : 
    'names' attribute [1] must be the same length as the vector [0]
  
   *** reducing smoothing df
  Method: ResidualPredictionTest ; Setting 8 
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error (test_output.R:130:11): Checks output type for InvariantTargetPrediction and setting 2 ──
  Error in `model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels)`: factor V1 has new levels -0.0208562477726917, -0.0235547282205754, -0.0973446564106039, -0.133588587310888, -0.153284494083905, -0.207578007909609, -0.213782931052866, -0.283000010545122, -0.283303465135162, -0.382392286417413, -0.41956709390976, -0.539035755231161, -0.659607545700844, -0.701668131490702, -0.813182488557432, -0.896057118536696, -0.912596622930015, -0.943029553156257, -1.13943388951983, -1.33647140779278, -1.61535379651566, -1.73199940347958, -1.94187717455006, 0.0253605634159716, 0.0448954678771692, 0.10272088207221, 0.160189756044092, 0.278836611211254, 0.321172613882429, 0.474652573776542, 0.707757727723782, 0.72988826637651, 0.795646477027457, 0.831654774239471, 0.949538241303685, 1.04789526218594, 1.10007539829706, 1.40929082452052, 1.74114236952371, 2.02481856846431
  Backtrace:
       █
    1. ├─testthat::expect_is(...) test_output.R:130:10
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─CondIndTests::CondIndTest(X, Y, Z, method = method)
    5.   ├─base::do.call(InvariantTargetPrediction, c(argsSet, parsMethod))
    6.   └─(function (Y, E, X, alpha = 0.05, verbose = FALSE, fitWithGam = TRUE, ...
    7.     └─CondIndTests:::gamTargetY(...)
    8.       └─CondIndTests:::getgamPredictions(...)
    9.         ├─stats::predict(ga, newdata = XTest)
   10.         ├─stats::predict(ga, newdata = XTest)
   11.         └─stats::predict.lm(ga, newdata = XTest)
   12.           ├─stats::model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels)
   13.           └─stats::model.frame.default(...)
  ── Error (test_output.R:130:11): Checks output type for InvariantTargetPrediction and setting 4 ──
  Error in `model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels)`: factor V1 has new levels -0.0611214772655199, -0.0928292732261513, -0.105247445634202, -0.255023647055969, -0.26486037495072, -0.270947298322676, -0.287118817275152, -0.347803566563267, -0.50699135329533, -0.527372179200788, -0.547269229805883, -0.702391597305726, -0.813002538267529, -0.866241289595862, -1.0338288374485, -1.10454337206559, -1.10556276219444, -1.10593524295094, -1.29831981818867, -1.38091798166473, -1.44840621326152, 0.036973113976512, 0.30100146754179, 0.328241869059011, 0.332906925595771, 0.428935491648456, 0.433146407475115, 0.43466846729207, 0.522464605943239, 0.557291183702868, 0.563549187516396, 0.715253262475903, 0.747671958348159, 0.827340419694513, 0.939010737167377, 0.96404817945466, 1.05722009159222, 1.14724627854776, 1.43633866500782, 1.94094718904907
  Backtrace:
       █
    1. ├─testthat::expect_is(...) test_output.R:130:10
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─CondIndTests::CondIndTest(X, Y, Z, method = method)
    5.   ├─base::do.call(InvariantTargetPrediction, c(argsSet, parsMethod))
    6.   └─(function (Y, E, X, alpha = 0.05, verbose = FALSE, fitWithGam = TRUE, ...
    7.     └─CondIndTests:::gamTargetY(...)
    8.       └─CondIndTests:::getgamPredictions(...)
    9.         ├─stats::predict(ga, newdata = XTest)
   10.         ├─stats::predict(ga, newdata = XTest)
   11.         └─stats::predict.lm(ga, newdata = XTest)
   12.           ├─stats::model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels)
   13.           └─stats::model.frame.default(...)
  ── Error (test_output.R:130:11): Checks output type for InvariantTargetPrediction and setting 6 ──
  Error in `model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels)`: factor V1 has new levels -0.0578395248734961, -0.104868118923689, -0.106674124183622, -0.256899285752152, -0.323379314760826, -0.330246522142463, -0.360057798740497, -0.441590954128072, -0.547846496939661, -0.578794044899409, -0.683477984744538, -0.76439862856721, -0.802749847447673, -0.849622410966138, -0.860636235109456, -0.963881324722789, -1.04330053764628, -1.08766215666057, -1.4809597327286, -1.61629382734672, -1.99272411635448, -2.28051666942078, 0.057982496963146, 0.0768272132160698, 0.0789508039169364, 0.121284535774645, 0.200231300721309, 0.257343043104895, 0.430610376623057, 0.508478418979306, 0.545258660582157, 0.560968942270465, 0.571195776705702, 0.806923450041495, 0.935542174708785, 0.990584805389954, 1.25404205131098, 1.44198171158741, 1.76121531291059, 1.84188271193839
  Backtrace:
       █
    1. ├─testthat::expect_is(...) test_output.R:130:10
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─CondIndTests::CondIndTest(X, Y, Z, method = method)
    5.   ├─base::do.call(InvariantTargetPrediction, c(argsSet, parsMethod))
    6.   └─(function (Y, E, X, alpha = 0.05, verbose = FALSE, fitWithGam = TRUE, ...
    7.     └─CondIndTests:::gamTargetY(...)
    8.       └─CondIndTests:::getgamPredictions(...)
    9.         ├─stats::predict(ga, newdata = XTest)
   10.         ├─stats::predict(ga, newdata = XTest)
   11.         └─stats::predict.lm(ga, newdata = XTest)
   12.           ├─stats::model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels)
   13.           └─stats::model.frame.default(...)
  ── Error (test_output.R:130:11): Checks output type for InvariantTargetPrediction and setting 8 ──
  Error in `model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels)`: factor V1 has new levels -0.0557004980500412, -0.0764957713810811, -0.109246545776959, -0.377586392359526, -0.400897311689022, -0.475866259418689, -0.613425997625633, -0.721759167718149, -0.791585220718916, -0.806136043000879, -0.810083154873641, -0.819878999859267, -0.877569380196432, -0.954376877568384, -1.13799285210712, -1.16247879058205, -1.30722632687622, -1.60301371496431, -1.69922571730377, -1.73698314984385, 0.0303879655491253, 0.0550788608167769, 0.131261751808353, 0.215043437721103, 0.221573714602765, 0.263987048423799, 0.292317667364457, 0.306346280941367, 0.372638560977915, 0.382219483235023, 0.436681180483308, 0.679543825286404, 0.784512630568672, 0.827172173195784, 0.992752243528321, 1.03329552611582, 1.23793320279249, 1.26128934653612, 1.63445907173965, 1.81166168055711
  Backtrace:
       █
    1. ├─testthat::expect_is(...) test_output.R:130:10
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─CondIndTests::CondIndTest(X, Y, Z, method = method)
    5.   ├─base::do.call(InvariantTargetPrediction, c(argsSet, parsMethod))
    6.   └─(function (Y, E, X, alpha = 0.05, verbose = FALSE, fitWithGam = TRUE, ...
    7.     └─CondIndTests:::gamTargetY(...)
    8.       └─CondIndTests:::getgamPredictions(...)
    9.         ├─stats::predict(ga, newdata = XTest)
   10.         ├─stats::predict(ga, newdata = XTest)
   11.         └─stats::predict.lm(ga, newdata = XTest)
   12.           ├─stats::model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels)
   13.           └─stats::model.frame.default(...)
  ── Error (test_output.R:130:11): Checks output type for InvariantTargetPrediction and setting 6 ──
  Error in `model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels)`: factor V1 has new levels -0.00250971169513185, -0.243306611317908, -0.2534553674698, -0.522648862256414, -0.585726054605465, -0.587770949328897, -0.606327294709291, -0.623701894396285, -0.648358173475725, -0.973710318504448, -1.12857416446032, -1.17866884144272, -1.348471719738, -1.58809502449108, -1.6083840045997, -1.68273502337023, -1.79380919174493, -2.26550494272381, 0.100445573466406, 0.131752932021193, 0.166198051569884, 0.296651244038402, 0.302054290311734, 0.324381012124555, 0.326123741311649, 0.424909536840421, 0.504495713561107, 0.709491375219637, 0.717899579297474, 1.03164533940083, 1.16953118214915, 1.1994407238927, 1.2266928077745, 1.26919035694719, 1.3279949776498, 1.35167402146439, 1.38466331277337, 1.50884946504327, 2.07826995066188, 3.09413251529024
  Backtrace:
       █
    1. ├─testthat::expect_is(...) test_output.R:130:10
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─CondIndTests::CondIndTest(X, Y, Z, method = method)
    5.   ├─base::do.call(InvariantTargetPrediction, c(argsSet, parsMethod))
    6.   └─(function (Y, E, X, alpha = 0.05, verbose = FALSE, fitWithGam = TRUE, ...
    7.     └─CondIndTests:::gamTargetY(...)
    8.       └─CondIndTests:::getgamPredictions(...)
    9.         ├─stats::predict(ga, newdata = XTest)
   10.         ├─stats::predict(ga, newdata = XTest)
   11.         └─stats::predict.lm(ga, newdata = XTest)
   12.           ├─stats::model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels)
   13.           └─stats::model.frame.default(...)
  ── Error (test_output.R:130:11): Checks output type for InvariantTargetPrediction and setting 8 ──
  Error in `model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels)`: factor V1 has new levels -0.00925520324058483, -0.105492147772155, -0.14682448520346, -0.298328000951139, -0.30502906768725, -0.342917657087566, -0.386191210769274, -0.502050651122342, -0.515950059828803, -0.540922544779331, -0.629340531094936, -0.723361630641226, -0.731609899569232, -0.772193769379475, -0.865159341973981, -0.91958139274149, -1.04610489754372, -1.24264101457432, -1.56800371042912, -1.95007729984143, -2.12804581548516, -2.35345033265095, 0.090821928214461, 0.143195467122359, 0.152776834812638, 0.186870318536647, 0.436821942633617, 0.550033581301153, 0.553546932245747, 0.563479435029805, 0.60450779937268, 0.696046718663821, 0.748799946818997, 1.01059625159145, 1.19029848263317, 1.19455039670587, 1.19985069313456, 1.3742750100287, 1.81535575195696, 2.42802999205948
  Backtrace:
       █
    1. ├─testthat::expect_is(...) test_output.R:130:10
    2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
    3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
    4. └─CondIndTests::CondIndTest(X, Y, Z, method = method)
    5.   ├─base::do.call(InvariantTargetPrediction, c(argsSet, parsMethod))
    6.   └─(function (Y, E, X, alpha = 0.05, verbose = FALSE, fitWithGam = TRUE, ...
    7.     └─CondIndTests:::gamTargetY(...)
    8.       └─CondIndTests:::getgamPredictions(...)
    9.         ├─stats::predict(ga, newdata = XTest)
   10.         ├─stats::predict(ga, newdata = XTest)
   11.         └─stats::predict.lm(ga, newdata = XTest)
   12.           ├─stats::model.frame(Terms, newdata, na.action = na.action, xlev = object$xlevels)
   13.           └─stats::model.frame.default(...)
  
  [ FAIL 6 | WARN 4 | SKIP 0 | PASS 90 ]
  Error: Test failures
  Execution halted