CRAN Package Check Results for Package OptimClassifier

Last updated on 2020-12-10 17:48:25 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.1.5 6.29 63.38 69.67 ERROR
r-devel-linux-x86_64-debian-gcc 0.1.5 6.04 49.97 56.01 ERROR
r-devel-linux-x86_64-fedora-clang 0.1.5 87.27 ERROR
r-devel-linux-x86_64-fedora-gcc 0.1.5 78.98 ERROR
r-devel-windows-ix86+x86_64 0.1.5 10.00 87.00 97.00 ERROR
r-patched-linux-x86_64 0.1.5 6.28 59.67 65.95 OK
r-patched-solaris-x86 0.1.5 121.70 OK
r-release-linux-x86_64 0.1.5 6.61 58.25 64.86 OK
r-release-macos-x86_64 0.1.5 OK
r-release-windows-ix86+x86_64 0.1.5 11.00 64.00 75.00 OK
r-oldrel-macos-x86_64 0.1.5 OK
r-oldrel-windows-ix86+x86_64 0.1.5 11.00 82.00 93.00 OK

Check Details

Version: 0.1.5
Check: tests
Result: ERROR
     Running 'testthat.R' [10s/11s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(OptimClassifier)
     >
     > test_check("OptimClassifier")
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
     rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
     model = FALSE, x = FALSE, y = FALSE, cp = 0)
     n= 482
    
     CP nsplit rel error xerror xstd
     1 0.66515837 0 1.0000000 1.0000000 0.04949948
     2 0.02262443 1 0.3348416 0.3348416 0.03581213
     3 0.01357466 4 0.2669683 0.3438914 0.03620379
     4 0.01131222 5 0.2533937 0.3438914 0.03620379
    
     Variable importance
     X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
     36 16 16 13 10 6 2 1 1 1
    
     Node number 1: 482 observations, complexity param=0.6651584
     predicted class=0 expected loss=0.4585062 P(node) =1
     class counts: 261 221
     probabilities: 0.541 0.459
     left son=2 (219 obs) right son=3 (263 obs)
     Primary splits:
     X8 splits as LR, improve=119.25990, (0 missing)
     X10 < 2.5 to the left, improve= 52.61262, (0 missing)
     X9 splits as LR, improve= 47.76803, (0 missing)
     X14 < 396 to the left, improve= 32.46584, (0 missing)
     X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
     Surrogate splits:
     X9 splits as LR, agree=0.701, adj=0.342, (0 split)
     X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
     X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
     X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
     X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
    
     Node number 2: 219 observations
     predicted class=0 expected loss=0.07305936 P(node) =0.4543568
     class counts: 203 16
     probabilities: 0.927 0.073
    
     Node number 3: 263 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.2205323 P(node) =0.5456432
     class counts: 58 205
     probabilities: 0.221 0.779
     left son=6 (99 obs) right son=7 (164 obs)
     Primary splits:
     X9 splits as LR, improve=11.902240, (0 missing)
     X10 < 0.5 to the left, improve=11.902240, (0 missing)
     X14 < 216.5 to the left, improve=10.195680, (0 missing)
     X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
     X13 < 72.5 to the right, improve= 6.568284, (0 missing)
     Surrogate splits:
     X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
     X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
     X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
     X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
     X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
    
     Node number 6: 99 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.4141414 P(node) =0.2053942
     class counts: 41 58
     probabilities: 0.414 0.586
     left son=12 (61 obs) right son=13 (38 obs)
     Primary splits:
     X13 < 111 to the right, improve=6.520991, (0 missing)
     X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
     X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
     X14 < 388.5 to the left, improve=3.403553, (0 missing)
     X3 < 2.52 to the left, improve=2.599301, (0 missing)
     Surrogate splits:
     X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
     X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
     X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
     X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
     X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
    
     Node number 7: 164 observations
     predicted class=1 expected loss=0.1036585 P(node) =0.340249
     class counts: 17 147
     probabilities: 0.104 0.896
    
     Node number 12: 61 observations, complexity param=0.02262443
     predicted class=0 expected loss=0.442623 P(node) =0.126556
     class counts: 34 27
     probabilities: 0.557 0.443
     left son=24 (49 obs) right son=25 (12 obs)
     Primary splits:
     X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
     X14 < 126 to the left, improve=3.7856330, (0 missing)
     X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
     X3 < 9.625 to the right, improve=1.0257110, (0 missing)
     X2 < 24.5 to the right, improve=0.9861812, (0 missing)
     Surrogate splits:
     X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
     X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
    
     Node number 13: 38 observations
     predicted class=1 expected loss=0.1842105 P(node) =0.07883817
     class counts: 7 31
     probabilities: 0.184 0.816
    
     Node number 24: 49 observations, complexity param=0.01357466
     predicted class=0 expected loss=0.3469388 P(node) =0.1016598
     class counts: 32 17
     probabilities: 0.653 0.347
     left son=48 (34 obs) right son=49 (15 obs)
     Primary splits:
     X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
     X13 < 150 to the left, improve=2.3016430, (0 missing)
     X14 < 126 to the left, improve=2.2040820, (0 missing)
     X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
     X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
     Surrogate splits:
     X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
     X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
     X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
    
     Node number 25: 12 observations
     predicted class=1 expected loss=0.1666667 P(node) =0.02489627
     class counts: 2 10
     probabilities: 0.167 0.833
    
     Node number 48: 34 observations
     predicted class=0 expected loss=0.2352941 P(node) =0.07053942
     class counts: 26 8
     probabilities: 0.765 0.235
    
     Node number 49: 15 observations
     predicted class=1 expected loss=0.4 P(node) =0.03112033
     class counts: 6 9
     probabilities: 0.400 0.600
    
     -- ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ----------
     Error: arrange() failed at implicit mutate() step.
     x Could not create a temporary column for `..1`.
     i `..1` is `get(column)`.
     Backtrace:
     x
     1. +-OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
     2. | \-OptimClassifier:::OrderModels(summary_models, criteria)
     3. | +-base::ifelse(...)
     4. | +-dplyr::arrange(summary_table, get(column))
     5. | \-dplyr:::arrange.data.frame(summary_table, get(column))
     6. | \-dplyr:::arrange_rows(.data, dots)
     7. | +-base::withCallingHandlers(...)
     8. | +-dplyr::transmute(new_data_frame(.data), !!!quosures)
     9. | \-dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
     10. | +-dplyr::mutate(.data, ..., .keep = "none")
     11. | \-dplyr:::mutate.data.frame(.data, ..., .keep = "none")
     12. | \-dplyr:::mutate_cols(.data, ...)
     13. | +-base::withCallingHandlers(...)
     14. | \-mask$eval_all_mutate(dots[[i]])
     15. +-base::get(column)
     16. +-base::.handleSimpleError(...)
     17. | \-dplyr:::h(simpleError(msg, call))
     18. | \-rlang::abort(...)
     19. | \-rlang:::signal_abort(cnd)
     20. | \-base::signalCondition(cnd)
     21. \-(function (cnd) ...
    
     -- Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit --------------
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     -- Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit --------------
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     7 successful models have been tested and 21 thresholds evaluated
    
     Model rmse Threshold success_rate ti_error tii_error
     1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
     2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
     3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
     4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
     5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
     6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
     7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     [1] "\n"-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
    
     # weights: 37
     initial value 314.113022
     iter 10 value 305.860086
     iter 20 value 305.236595
     iter 30 value 305.199531
     final value 305.199440
     converged
     # weights: 73
     initial value 331.204920
     iter 10 value 277.703814
     iter 20 value 264.464583
     iter 30 value 235.346147
     iter 40 value 213.675019
     iter 50 value 204.512635
     iter 60 value 187.308074
     iter 70 value 147.436155
     iter 80 value 134.805399
     iter 90 value 128.131335
     iter 100 value 127.540544
     final value 127.539685
     converged
     # weights: 109
     initial value 313.022965
     iter 10 value 293.213546
     iter 20 value 276.666037
     iter 30 value 272.763270
     iter 40 value 271.860014
     iter 50 value 271.852731
     final value 271.852717
     converged
     # weights: 145
     initial value 342.699156
     iter 10 value 266.493159
     iter 20 value 248.027579
     iter 30 value 202.156187
     iter 40 value 181.633139
     iter 50 value 165.106980
     iter 60 value 146.805052
     iter 70 value 131.481835
     iter 80 value 118.560985
     iter 90 value 113.589950
     iter 100 value 111.521717
     iter 110 value 109.315739
     iter 120 value 108.445079
     iter 130 value 107.625920
     iter 140 value 106.688656
     iter 150 value 105.653618
     iter 160 value 105.376674
     iter 170 value 105.364509
     final value 105.364086
     converged
     # weights: 181
     initial value 431.964891
     iter 10 value 268.804053
     iter 20 value 257.332978
     iter 30 value 249.853001
     iter 40 value 185.335385
     iter 50 value 153.782864
     iter 60 value 139.784881
     iter 70 value 126.922475
     iter 80 value 123.252145
     iter 90 value 120.348628
     iter 100 value 116.840543
     iter 110 value 112.093661
     iter 120 value 106.440800
     iter 130 value 105.233650
     iter 140 value 105.222728
     final value 105.198137
     converged
     # weights: 217
     initial value 364.112551
     iter 10 value 274.040840
     iter 20 value 260.171164
     iter 30 value 252.654051
     iter 40 value 245.185007
     iter 50 value 239.105471
     iter 60 value 225.547657
     iter 70 value 208.619569
     iter 80 value 196.251825
     iter 90 value 188.803731
     iter 100 value 187.520201
     iter 110 value 180.509639
     iter 120 value 173.309374
     iter 130 value 162.696568
     iter 140 value 158.906941
     iter 150 value 157.467273
     iter 160 value 157.011874
     iter 170 value 156.566607
     iter 180 value 154.830641
     iter 190 value 154.706559
     iter 200 value 154.475990
     iter 210 value 153.619213
     iter 220 value 149.786054
     iter 230 value 147.795144
     iter 240 value 147.083513
     iter 250 value 145.964459
     iter 260 value 145.865523
     iter 270 value 145.820803
     iter 280 value 145.792567
     iter 290 value 145.721832
     iter 300 value 145.610319
     iter 310 value 145.570264
     iter 320 value 145.522248
     iter 330 value 145.455157
     iter 340 value 145.350623
     iter 350 value 145.244467
     iter 360 value 145.203358
     iter 370 value 145.184359
     iter 380 value 145.176312
     iter 390 value 145.169123
     iter 400 value 145.154012
     iter 410 value 145.147479
     iter 420 value 145.127146
     iter 430 value 145.091879
     iter 440 value 145.074548
     iter 450 value 145.026713
     iter 460 value 145.016682
     iter 470 value 145.010994
     iter 480 value 144.949362
     iter 490 value 144.786727
     iter 500 value 144.760616
     final value 144.760616
     stopped after 500 iterations
     6 models have been tested with differents levels of hidden layers
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 4 0.3236595 1 0.5950413 0.4049587 0
     2 5 0.3279188 1 0.5950413 0.4049587 0
     3 2 0.3613537 1 0.5950413 0.4049587 0
     4 6 0.4056531 1 0.5950413 0.4049587 0
     5 3 0.4793713 1 0.5950413 0.4049587 0
     6 1 0.5048849 1 0.5950413 0.4049587 06 successful models have been tested
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 4 0.3236595 1 0.5950413 0.4049587 0
     2 5 0.3279188 1 0.5950413 0.4049587 0
     3 2 0.3613537 1 0.5950413 0.4049587 0
     4 6 0.4056531 1 0.5950413 0.4049587 0
     5 3 0.4793713 1 0.5950413 0.4049587 0
     6 1 0.5048849 1 0.5950413 0.4049587 0
     4 successful kernels have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
     == testthat results ===========================================================
     ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
    
     [ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
     Error: Test failures
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.1.5
Check: tests
Result: ERROR
     Running ‘testthat.R’ [9s/14s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(OptimClassifier)
     >
     > test_check("OptimClassifier")
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
     rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
     model = FALSE, x = FALSE, y = FALSE, cp = 0)
     n= 482
    
     CP nsplit rel error xerror xstd
     1 0.66515837 0 1.0000000 1.0000000 0.04949948
     2 0.02262443 1 0.3348416 0.3348416 0.03581213
     3 0.01357466 4 0.2669683 0.3438914 0.03620379
     4 0.01131222 5 0.2533937 0.3438914 0.03620379
    
     Variable importance
     X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
     36 16 16 13 10 6 2 1 1 1
    
     Node number 1: 482 observations, complexity param=0.6651584
     predicted class=0 expected loss=0.4585062 P(node) =1
     class counts: 261 221
     probabilities: 0.541 0.459
     left son=2 (219 obs) right son=3 (263 obs)
     Primary splits:
     X8 splits as LR, improve=119.25990, (0 missing)
     X10 < 2.5 to the left, improve= 52.61262, (0 missing)
     X9 splits as LR, improve= 47.76803, (0 missing)
     X14 < 396 to the left, improve= 32.46584, (0 missing)
     X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
     Surrogate splits:
     X9 splits as LR, agree=0.701, adj=0.342, (0 split)
     X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
     X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
     X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
     X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
    
     Node number 2: 219 observations
     predicted class=0 expected loss=0.07305936 P(node) =0.4543568
     class counts: 203 16
     probabilities: 0.927 0.073
    
     Node number 3: 263 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.2205323 P(node) =0.5456432
     class counts: 58 205
     probabilities: 0.221 0.779
     left son=6 (99 obs) right son=7 (164 obs)
     Primary splits:
     X9 splits as LR, improve=11.902240, (0 missing)
     X10 < 0.5 to the left, improve=11.902240, (0 missing)
     X14 < 216.5 to the left, improve=10.195680, (0 missing)
     X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
     X13 < 72.5 to the right, improve= 6.568284, (0 missing)
     Surrogate splits:
     X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
     X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
     X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
     X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
     X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
    
     Node number 6: 99 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.4141414 P(node) =0.2053942
     class counts: 41 58
     probabilities: 0.414 0.586
     left son=12 (61 obs) right son=13 (38 obs)
     Primary splits:
     X13 < 111 to the right, improve=6.520991, (0 missing)
     X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
     X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
     X14 < 388.5 to the left, improve=3.403553, (0 missing)
     X3 < 2.52 to the left, improve=2.599301, (0 missing)
     Surrogate splits:
     X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
     X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
     X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
     X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
     X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
    
     Node number 7: 164 observations
     predicted class=1 expected loss=0.1036585 P(node) =0.340249
     class counts: 17 147
     probabilities: 0.104 0.896
    
     Node number 12: 61 observations, complexity param=0.02262443
     predicted class=0 expected loss=0.442623 P(node) =0.126556
     class counts: 34 27
     probabilities: 0.557 0.443
     left son=24 (49 obs) right son=25 (12 obs)
     Primary splits:
     X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
     X14 < 126 to the left, improve=3.7856330, (0 missing)
     X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
     X3 < 9.625 to the right, improve=1.0257110, (0 missing)
     X2 < 24.5 to the right, improve=0.9861812, (0 missing)
     Surrogate splits:
     X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
     X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
    
     Node number 13: 38 observations
     predicted class=1 expected loss=0.1842105 P(node) =0.07883817
     class counts: 7 31
     probabilities: 0.184 0.816
    
     Node number 24: 49 observations, complexity param=0.01357466
     predicted class=0 expected loss=0.3469388 P(node) =0.1016598
     class counts: 32 17
     probabilities: 0.653 0.347
     left son=48 (34 obs) right son=49 (15 obs)
     Primary splits:
     X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
     X13 < 150 to the left, improve=2.3016430, (0 missing)
     X14 < 126 to the left, improve=2.2040820, (0 missing)
     X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
     X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
     Surrogate splits:
     X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
     X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
     X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
    
     Node number 25: 12 observations
     predicted class=1 expected loss=0.1666667 P(node) =0.02489627
     class counts: 2 10
     probabilities: 0.167 0.833
    
     Node number 48: 34 observations
     predicted class=0 expected loss=0.2352941 P(node) =0.07053942
     class counts: 26 8
     probabilities: 0.765 0.235
    
     Node number 49: 15 observations
     predicted class=1 expected loss=0.4 P(node) =0.03112033
     class counts: 6 9
     probabilities: 0.400 0.600
    
     ── ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ──────────
     Error: arrange() failed at implicit mutate() step.
     ✖ Could not create a temporary column for `..1`.
     ℹ `..1` is `get(column)`.
     Backtrace:
     █
     1. ├─OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
     2. │ └─OptimClassifier:::OrderModels(summary_models, criteria)
     3. │ ├─base::ifelse(...)
     4. │ ├─dplyr::arrange(summary_table, get(column))
     5. │ └─dplyr:::arrange.data.frame(summary_table, get(column))
     6. │ └─dplyr:::arrange_rows(.data, dots)
     7. │ ├─base::withCallingHandlers(...)
     8. │ ├─dplyr::transmute(new_data_frame(.data), !!!quosures)
     9. │ └─dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
     10. │ ├─dplyr::mutate(.data, ..., .keep = "none")
     11. │ └─dplyr:::mutate.data.frame(.data, ..., .keep = "none")
     12. │ └─dplyr:::mutate_cols(.data, ...)
     13. │ ├─base::withCallingHandlers(...)
     14. │ └─mask$eval_all_mutate(dots[[i]])
     15. ├─base::get(column)
     16. ├─base::.handleSimpleError(...)
     17. │ └─dplyr:::h(simpleError(msg, call))
     18. │ └─rlang::abort(...)
     19. │ └─rlang:::signal_abort(cnd)
     20. │ └─base::signalCondition(cnd)
     21. └─(function (cnd) ...
    
     ── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     ── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     7 successful models have been tested and 21 thresholds evaluated
    
     Model rmse Threshold success_rate ti_error tii_error
     1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
     2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
     3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
     4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
     5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
     6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
     7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     [1] "\n"── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
    
     # weights: 37
     initial value 314.113022
     iter 10 value 305.860086
     iter 20 value 305.236595
     iter 30 value 305.199531
     final value 305.199440
     converged
     # weights: 73
     initial value 331.204920
     iter 10 value 277.703814
     iter 20 value 264.464583
     iter 30 value 235.346147
     iter 40 value 213.675019
     iter 50 value 204.513413
     iter 60 value 188.579524
     iter 70 value 151.606445
     iter 80 value 135.045373
     iter 90 value 127.862716
     iter 100 value 127.540380
     final value 127.539685
     converged
     # weights: 109
     initial value 313.022965
     iter 10 value 293.213546
     iter 20 value 276.666037
     iter 30 value 272.763270
     iter 40 value 271.860014
     iter 50 value 271.852731
     final value 271.852717
     converged
     # weights: 145
     initial value 342.699156
     iter 10 value 266.493159
     iter 20 value 248.027580
     iter 30 value 202.137254
     iter 40 value 182.841633
     iter 50 value 163.406077
     iter 60 value 153.712411
     iter 70 value 143.761097
     iter 80 value 136.791530
     iter 90 value 131.883986
     iter 100 value 128.256325
     iter 110 value 123.054770
     iter 120 value 119.373904
     iter 130 value 117.935121
     iter 140 value 115.543916
     iter 150 value 113.870705
     iter 160 value 112.397158
     iter 170 value 110.659059
     iter 180 value 110.265167
     iter 190 value 109.914964
     iter 200 value 109.465033
     iter 210 value 109.365831
     iter 220 value 109.309421
     iter 230 value 109.221952
     iter 240 value 109.183033
     iter 250 value 109.077007
     iter 260 value 109.024442
     iter 270 value 108.979083
     iter 280 value 108.966573
     iter 290 value 108.946679
     iter 300 value 108.928539
     iter 310 value 108.906541
     iter 320 value 108.903732
     iter 330 value 108.881879
     iter 340 value 108.860613
     iter 350 value 108.852071
     iter 360 value 108.843541
     iter 370 value 108.833519
     iter 380 value 108.826552
     iter 390 value 108.704253
     iter 400 value 108.679474
     iter 410 value 108.667142
     iter 420 value 108.661706
     iter 430 value 108.659019
     iter 440 value 108.653729
     iter 450 value 108.633311
     iter 460 value 108.613541
     iter 470 value 108.584588
     iter 480 value 108.566776
     iter 490 value 108.527453
     iter 500 value 108.484387
     final value 108.484387
     stopped after 500 iterations
     # weights: 181
     initial value 431.964891
     iter 10 value 268.804053
     iter 20 value 257.332978
     iter 30 value 249.853001
     iter 40 value 185.335385
     iter 50 value 153.782864
     iter 60 value 139.784881
     iter 70 value 126.922476
     iter 80 value 123.252215
     iter 90 value 120.355265
     iter 100 value 116.841582
     iter 110 value 112.118113
     iter 120 value 105.917923
     iter 130 value 105.028943
     iter 140 value 105.024535
     final value 105.024530
     converged
     # weights: 217
     initial value 364.112551
     iter 10 value 274.040840
     iter 20 value 260.171164
     iter 30 value 252.654051
     iter 40 value 245.185007
     iter 50 value 239.105792
     iter 60 value 228.177441
     iter 70 value 209.068307
     iter 80 value 189.216192
     iter 90 value 179.285289
     iter 100 value 177.101089
     iter 110 value 177.096487
     final value 177.096448
     converged
     # weights: 253
     initial value 325.866420
     iter 10 value 269.878024
     iter 20 value 260.328850
     iter 30 value 255.407336
     iter 40 value 248.367432
     iter 50 value 201.019882
     iter 60 value 154.795327
     iter 70 value 142.061451
     iter 80 value 129.177931
     iter 90 value 113.635713
     iter 100 value 109.226495
     iter 110 value 102.067007
     iter 120 value 100.120314
     iter 130 value 99.785251
     iter 140 value 99.503240
     iter 150 value 99.238235
     iter 160 value 99.194327
     iter 170 value 99.004752
     iter 180 value 98.488715
     iter 190 value 97.866264
     iter 200 value 97.553015
     iter 210 value 97.406696
     iter 220 value 97.262658
     iter 230 value 97.069177
     iter 240 value 96.926948
     iter 250 value 96.846568
     iter 260 value 96.575699
     iter 270 value 96.307219
     iter 280 value 96.246913
     iter 290 value 96.193544
     iter 300 value 96.112864
     iter 310 value 96.061954
     iter 320 value 95.737456
     iter 330 value 95.016696
     iter 340 value 94.608414
     iter 350 value 94.331090
     iter 360 value 94.229239
     iter 370 value 94.123306
     iter 380 value 94.070268
     iter 390 value 93.966009
     iter 400 value 93.756340
     iter 410 value 93.566368
     iter 420 value 93.510538
     iter 430 value 93.426889
     iter 440 value 93.342668
     iter 450 value 93.293375
     iter 460 value 93.232193
     iter 470 value 93.158243
     iter 480 value 93.026257
     iter 490 value 92.808916
     iter 500 value 92.697079
     final value 92.697079
     stopped after 500 iterations
     # weights: 289
     initial value 308.589551
     iter 10 value 276.114138
     iter 20 value 271.523670
     iter 30 value 263.359821
     iter 40 value 252.965028
     iter 50 value 215.194350
     iter 60 value 197.931441
     iter 70 value 189.556282
     iter 80 value 162.533108
     iter 90 value 148.780324
     iter 100 value 127.237270
     iter 110 value 113.271915
     iter 120 value 103.681517
     iter 130 value 100.506900
     iter 140 value 99.473416
     iter 150 value 96.419897
     iter 160 value 95.875123
     iter 170 value 95.582754
     iter 180 value 94.802815
     iter 190 value 93.800813
     iter 200 value 93.415047
     iter 210 value 93.113811
     iter 220 value 92.848904
     iter 230 value 92.216786
     iter 240 value 91.199472
     iter 250 value 91.064264
     iter 260 value 90.940703
     iter 270 value 90.613176
     iter 280 value 90.580067
     iter 290 value 90.552349
     iter 300 value 90.469581
     iter 310 value 90.125040
     iter 320 value 89.834299
     iter 330 value 89.634271
     iter 340 value 89.477507
     iter 350 value 89.278901
     iter 360 value 89.110192
     iter 370 value 89.034938
     iter 380 value 88.514948
     iter 390 value 88.368879
     iter 400 value 88.232517
     iter 410 value 88.189377
     iter 420 value 88.178188
     iter 430 value 88.169094
     iter 440 value 88.156563
     iter 450 value 88.136171
     iter 460 value 88.106613
     iter 470 value 87.790970
     iter 480 value 87.676820
     iter 490 value 87.599647
     iter 500 value 87.568524
     final value 87.568524
     stopped after 500 iterations
     # weights: 325
     initial value 387.634927
     iter 10 value 268.437459
     iter 20 value 261.669527
     iter 30 value 238.226127
     iter 40 value 218.985628
     iter 50 value 203.340002
     iter 60 value 197.855735
     iter 70 value 192.165234
     iter 80 value 191.161086
     iter 90 value 191.147630
     iter 100 value 191.146459
     iter 110 value 191.146013
     final value 191.145998
     converged
     9 models have been tested with differents levels of hidden layers
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 5 0.3276663 1 0.5950413 0.4049587 0.00000000
     2 7 0.3331272 1 0.5950413 0.4049587 0.00000000
     3 4 0.3552631 1 0.5950413 0.4049587 0.00000000
     4 8 0.3645871 1 0.7851240 0.1983471 0.01652893
     5 2 0.3706125 1 0.5950413 0.4049587 0.00000000
     6 9 0.4330809 1 0.5950413 0.4049587 0.00000000
     7 6 0.4491394 1 0.5950413 0.4049587 0.00000000
     8 3 0.4793713 1 0.5950413 0.4049587 0.00000000
     9 1 0.5048849 1 0.5950413 0.4049587 0.000000009 successful models have been tested
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 5 0.3276663 1 0.5950413 0.4049587 0.00000000
     2 7 0.3331272 1 0.5950413 0.4049587 0.00000000
     3 4 0.3552631 1 0.5950413 0.4049587 0.00000000
     4 8 0.3645871 1 0.7851240 0.1983471 0.01652893
     5 2 0.3706125 1 0.5950413 0.4049587 0.00000000
     6 9 0.4330809 1 0.5950413 0.4049587 0.00000000
     7 6 0.4491394 1 0.5950413 0.4049587 0.00000000
     8 3 0.4793713 1 0.5950413 0.4049587 0.00000000
     9 1 0.5048849 1 0.5950413 0.4049587 0.00000000
     4 successful kernels have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
     ══ testthat results ═══════════════════════════════════════════════════════════
     ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
    
     [ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
     Error: Test failures
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.1.5
Check: tests
Result: ERROR
     Running ‘testthat.R’ [11s/19s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(OptimClassifier)
     >
     > test_check("OptimClassifier")
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
     rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
     model = FALSE, x = FALSE, y = FALSE, cp = 0)
     n= 482
    
     CP nsplit rel error xerror xstd
     1 0.66515837 0 1.0000000 1.0000000 0.04949948
     2 0.02262443 1 0.3348416 0.3348416 0.03581213
     3 0.01357466 4 0.2669683 0.3438914 0.03620379
     4 0.01131222 5 0.2533937 0.3438914 0.03620379
    
     Variable importance
     X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
     36 16 16 13 10 6 2 1 1 1
    
     Node number 1: 482 observations, complexity param=0.6651584
     predicted class=0 expected loss=0.4585062 P(node) =1
     class counts: 261 221
     probabilities: 0.541 0.459
     left son=2 (219 obs) right son=3 (263 obs)
     Primary splits:
     X8 splits as LR, improve=119.25990, (0 missing)
     X10 < 2.5 to the left, improve= 52.61262, (0 missing)
     X9 splits as LR, improve= 47.76803, (0 missing)
     X14 < 396 to the left, improve= 32.46584, (0 missing)
     X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
     Surrogate splits:
     X9 splits as LR, agree=0.701, adj=0.342, (0 split)
     X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
     X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
     X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
     X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
    
     Node number 2: 219 observations
     predicted class=0 expected loss=0.07305936 P(node) =0.4543568
     class counts: 203 16
     probabilities: 0.927 0.073
    
     Node number 3: 263 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.2205323 P(node) =0.5456432
     class counts: 58 205
     probabilities: 0.221 0.779
     left son=6 (99 obs) right son=7 (164 obs)
     Primary splits:
     X9 splits as LR, improve=11.902240, (0 missing)
     X10 < 0.5 to the left, improve=11.902240, (0 missing)
     X14 < 216.5 to the left, improve=10.195680, (0 missing)
     X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
     X13 < 72.5 to the right, improve= 6.568284, (0 missing)
     Surrogate splits:
     X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
     X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
     X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
     X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
     X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
    
     Node number 6: 99 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.4141414 P(node) =0.2053942
     class counts: 41 58
     probabilities: 0.414 0.586
     left son=12 (61 obs) right son=13 (38 obs)
     Primary splits:
     X13 < 111 to the right, improve=6.520991, (0 missing)
     X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
     X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
     X14 < 388.5 to the left, improve=3.403553, (0 missing)
     X3 < 2.52 to the left, improve=2.599301, (0 missing)
     Surrogate splits:
     X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
     X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
     X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
     X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
     X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
    
     Node number 7: 164 observations
     predicted class=1 expected loss=0.1036585 P(node) =0.340249
     class counts: 17 147
     probabilities: 0.104 0.896
    
     Node number 12: 61 observations, complexity param=0.02262443
     predicted class=0 expected loss=0.442623 P(node) =0.126556
     class counts: 34 27
     probabilities: 0.557 0.443
     left son=24 (49 obs) right son=25 (12 obs)
     Primary splits:
     X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
     X14 < 126 to the left, improve=3.7856330, (0 missing)
     X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
     X3 < 9.625 to the right, improve=1.0257110, (0 missing)
     X2 < 24.5 to the right, improve=0.9861812, (0 missing)
     Surrogate splits:
     X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
     X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
    
     Node number 13: 38 observations
     predicted class=1 expected loss=0.1842105 P(node) =0.07883817
     class counts: 7 31
     probabilities: 0.184 0.816
    
     Node number 24: 49 observations, complexity param=0.01357466
     predicted class=0 expected loss=0.3469388 P(node) =0.1016598
     class counts: 32 17
     probabilities: 0.653 0.347
     left son=48 (34 obs) right son=49 (15 obs)
     Primary splits:
     X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
     X13 < 150 to the left, improve=2.3016430, (0 missing)
     X14 < 126 to the left, improve=2.2040820, (0 missing)
     X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
     X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
     Surrogate splits:
     X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
     X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
     X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
    
     Node number 25: 12 observations
     predicted class=1 expected loss=0.1666667 P(node) =0.02489627
     class counts: 2 10
     probabilities: 0.167 0.833
    
     Node number 48: 34 observations
     predicted class=0 expected loss=0.2352941 P(node) =0.07053942
     class counts: 26 8
     probabilities: 0.765 0.235
    
     Node number 49: 15 observations
     predicted class=1 expected loss=0.4 P(node) =0.03112033
     class counts: 6 9
     probabilities: 0.400 0.600
    
     ── ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ──────────
     Error: arrange() failed at implicit mutate() step.
     ✖ Could not create a temporary column for `..1`.
     ℹ `..1` is `get(column)`.
     Backtrace:
     █
     1. ├─OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
     2. │ └─OptimClassifier:::OrderModels(summary_models, criteria)
     3. │ ├─base::ifelse(...)
     4. │ ├─dplyr::arrange(summary_table, get(column))
     5. │ └─dplyr:::arrange.data.frame(summary_table, get(column))
     6. │ └─dplyr:::arrange_rows(.data, dots)
     7. │ ├─base::withCallingHandlers(...)
     8. │ ├─dplyr::transmute(new_data_frame(.data), !!!quosures)
     9. │ └─dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
     10. │ ├─dplyr::mutate(.data, ..., .keep = "none")
     11. │ └─dplyr:::mutate.data.frame(.data, ..., .keep = "none")
     12. │ └─dplyr:::mutate_cols(.data, ...)
     13. │ ├─base::withCallingHandlers(...)
     14. │ └─mask$eval_all_mutate(dots[[i]])
     15. ├─base::get(column)
     16. ├─base::.handleSimpleError(...)
     17. │ └─dplyr:::h(simpleError(msg, call))
     18. │ └─rlang::abort(...)
     19. │ └─rlang:::signal_abort(cnd)
     20. │ └─base::signalCondition(cnd)
     21. └─(function (cnd) ...
    
     ── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     ── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     7 successful models have been tested and 21 thresholds evaluated
    
     Model rmse Threshold success_rate ti_error tii_error
     1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
     2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
     3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
     4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
     5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
     6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
     7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     [1] "\n"── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
    
     # weights: 37
     initial value 314.113022
     iter 10 value 305.860086
     iter 20 value 305.236595
     iter 30 value 305.199531
     final value 305.199440
     converged
     # weights: 73
     initial value 331.204920
     iter 10 value 277.703814
     iter 20 value 264.464583
     iter 30 value 235.346147
     iter 40 value 213.675019
     iter 50 value 204.512635
     iter 60 value 187.308074
     iter 70 value 147.436155
     iter 80 value 134.805399
     iter 90 value 128.131335
     iter 100 value 127.540544
     final value 127.539685
     converged
     # weights: 109
     initial value 313.022965
     iter 10 value 293.213546
     iter 20 value 276.666037
     iter 30 value 272.763270
     iter 40 value 271.860014
     iter 50 value 271.852731
     final value 271.852717
     converged
     # weights: 145
     initial value 342.699156
     iter 10 value 266.493159
     iter 20 value 248.027579
     iter 30 value 202.156187
     iter 40 value 181.633139
     iter 50 value 165.106980
     iter 60 value 146.805052
     iter 70 value 131.481835
     iter 80 value 118.560985
     iter 90 value 113.589950
     iter 100 value 111.521717
     iter 110 value 109.315739
     iter 120 value 108.445079
     iter 130 value 107.625920
     iter 140 value 106.688656
     iter 150 value 105.653618
     iter 160 value 105.376674
     iter 170 value 105.364509
     final value 105.364086
     converged
     # weights: 181
     initial value 431.964891
     iter 10 value 268.804053
     iter 20 value 257.332978
     iter 30 value 249.853001
     iter 40 value 185.335385
     iter 50 value 153.782864
     iter 60 value 139.784881
     iter 70 value 126.922475
     iter 80 value 123.252145
     iter 90 value 120.348628
     iter 100 value 116.840543
     iter 110 value 112.093661
     iter 120 value 106.440800
     iter 130 value 105.233650
     iter 140 value 105.222728
     final value 105.198137
     converged
     # weights: 217
     initial value 364.112551
     iter 10 value 274.040840
     iter 20 value 260.171164
     iter 30 value 252.654051
     iter 40 value 245.185007
     iter 50 value 239.105471
     iter 60 value 225.547657
     iter 70 value 208.619569
     iter 80 value 196.251825
     iter 90 value 188.803731
     iter 100 value 187.520201
     iter 110 value 180.509639
     iter 120 value 173.309374
     iter 130 value 162.696568
     iter 140 value 158.906941
     iter 150 value 157.467273
     iter 160 value 157.011874
     iter 170 value 156.566607
     iter 180 value 154.830641
     iter 190 value 154.706559
     iter 200 value 154.475990
     iter 210 value 153.619213
     iter 220 value 149.786054
     iter 230 value 147.795144
     iter 240 value 147.083513
     iter 250 value 145.964459
     iter 260 value 145.865523
     iter 270 value 145.820803
     iter 280 value 145.792567
     iter 290 value 145.721832
     iter 300 value 145.610319
     iter 310 value 145.570264
     iter 320 value 145.522248
     iter 330 value 145.455157
     iter 340 value 145.350623
     iter 350 value 145.244467
     iter 360 value 145.203358
     iter 370 value 145.184359
     iter 380 value 145.176312
     iter 390 value 145.169123
     iter 400 value 145.154012
     iter 410 value 145.147479
     iter 420 value 145.127146
     iter 430 value 145.091879
     iter 440 value 145.074548
     iter 450 value 145.026713
     iter 460 value 145.016682
     iter 470 value 145.010994
     iter 480 value 144.949362
     iter 490 value 144.786727
     iter 500 value 144.760616
     final value 144.760616
     stopped after 500 iterations
     6 models have been tested with differents levels of hidden layers
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 4 0.3236595 1 0.5950413 0.4049587 0
     2 5 0.3279188 1 0.5950413 0.4049587 0
     3 2 0.3613537 1 0.5950413 0.4049587 0
     4 6 0.4056531 1 0.5950413 0.4049587 0
     5 3 0.4793713 1 0.5950413 0.4049587 0
     6 1 0.5048849 1 0.5950413 0.4049587 06 successful models have been tested
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 4 0.3236595 1 0.5950413 0.4049587 0
     2 5 0.3279188 1 0.5950413 0.4049587 0
     3 2 0.3613537 1 0.5950413 0.4049587 0
     4 6 0.4056531 1 0.5950413 0.4049587 0
     5 3 0.4793713 1 0.5950413 0.4049587 0
     6 1 0.5048849 1 0.5950413 0.4049587 0
     4 successful kernels have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
     ══ testthat results ═══════════════════════════════════════════════════════════
     ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
    
     [ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
     Error: Test failures
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1.5
Check: tests
Result: ERROR
     Running ‘testthat.R’ [12s/14s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(OptimClassifier)
     >
     > test_check("OptimClassifier")
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
     rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
     model = FALSE, x = FALSE, y = FALSE, cp = 0)
     n= 482
    
     CP nsplit rel error xerror xstd
     1 0.66515837 0 1.0000000 1.0000000 0.04949948
     2 0.02262443 1 0.3348416 0.3348416 0.03581213
     3 0.01357466 4 0.2669683 0.3438914 0.03620379
     4 0.01131222 5 0.2533937 0.3438914 0.03620379
    
     Variable importance
     X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
     36 16 16 13 10 6 2 1 1 1
    
     Node number 1: 482 observations, complexity param=0.6651584
     predicted class=0 expected loss=0.4585062 P(node) =1
     class counts: 261 221
     probabilities: 0.541 0.459
     left son=2 (219 obs) right son=3 (263 obs)
     Primary splits:
     X8 splits as LR, improve=119.25990, (0 missing)
     X10 < 2.5 to the left, improve= 52.61262, (0 missing)
     X9 splits as LR, improve= 47.76803, (0 missing)
     X14 < 396 to the left, improve= 32.46584, (0 missing)
     X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
     Surrogate splits:
     X9 splits as LR, agree=0.701, adj=0.342, (0 split)
     X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
     X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
     X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
     X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
    
     Node number 2: 219 observations
     predicted class=0 expected loss=0.07305936 P(node) =0.4543568
     class counts: 203 16
     probabilities: 0.927 0.073
    
     Node number 3: 263 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.2205323 P(node) =0.5456432
     class counts: 58 205
     probabilities: 0.221 0.779
     left son=6 (99 obs) right son=7 (164 obs)
     Primary splits:
     X9 splits as LR, improve=11.902240, (0 missing)
     X10 < 0.5 to the left, improve=11.902240, (0 missing)
     X14 < 216.5 to the left, improve=10.195680, (0 missing)
     X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
     X13 < 72.5 to the right, improve= 6.568284, (0 missing)
     Surrogate splits:
     X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
     X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
     X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
     X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
     X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
    
     Node number 6: 99 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.4141414 P(node) =0.2053942
     class counts: 41 58
     probabilities: 0.414 0.586
     left son=12 (61 obs) right son=13 (38 obs)
     Primary splits:
     X13 < 111 to the right, improve=6.520991, (0 missing)
     X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
     X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
     X14 < 388.5 to the left, improve=3.403553, (0 missing)
     X3 < 2.52 to the left, improve=2.599301, (0 missing)
     Surrogate splits:
     X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
     X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
     X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
     X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
     X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
    
     Node number 7: 164 observations
     predicted class=1 expected loss=0.1036585 P(node) =0.340249
     class counts: 17 147
     probabilities: 0.104 0.896
    
     Node number 12: 61 observations, complexity param=0.02262443
     predicted class=0 expected loss=0.442623 P(node) =0.126556
     class counts: 34 27
     probabilities: 0.557 0.443
     left son=24 (49 obs) right son=25 (12 obs)
     Primary splits:
     X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
     X14 < 126 to the left, improve=3.7856330, (0 missing)
     X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
     X3 < 9.625 to the right, improve=1.0257110, (0 missing)
     X2 < 24.5 to the right, improve=0.9861812, (0 missing)
     Surrogate splits:
     X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
     X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
    
     Node number 13: 38 observations
     predicted class=1 expected loss=0.1842105 P(node) =0.07883817
     class counts: 7 31
     probabilities: 0.184 0.816
    
     Node number 24: 49 observations, complexity param=0.01357466
     predicted class=0 expected loss=0.3469388 P(node) =0.1016598
     class counts: 32 17
     probabilities: 0.653 0.347
     left son=48 (34 obs) right son=49 (15 obs)
     Primary splits:
     X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
     X13 < 150 to the left, improve=2.3016430, (0 missing)
     X14 < 126 to the left, improve=2.2040820, (0 missing)
     X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
     X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
     Surrogate splits:
     X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
     X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
     X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
    
     Node number 25: 12 observations
     predicted class=1 expected loss=0.1666667 P(node) =0.02489627
     class counts: 2 10
     probabilities: 0.167 0.833
    
     Node number 48: 34 observations
     predicted class=0 expected loss=0.2352941 P(node) =0.07053942
     class counts: 26 8
     probabilities: 0.765 0.235
    
     Node number 49: 15 observations
     predicted class=1 expected loss=0.4 P(node) =0.03112033
     class counts: 6 9
     probabilities: 0.400 0.600
    
     ── ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ──────────
     Error: arrange() failed at implicit mutate() step.
     ✖ Could not create a temporary column for `..1`.
     ℹ `..1` is `get(column)`.
     Backtrace:
     █
     1. ├─OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
     2. │ └─OptimClassifier:::OrderModels(summary_models, criteria)
     3. │ ├─base::ifelse(...)
     4. │ ├─dplyr::arrange(summary_table, get(column))
     5. │ └─dplyr:::arrange.data.frame(summary_table, get(column))
     6. │ └─dplyr:::arrange_rows(.data, dots)
     7. │ ├─base::withCallingHandlers(...)
     8. │ ├─dplyr::transmute(new_data_frame(.data), !!!quosures)
     9. │ └─dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
     10. │ ├─dplyr::mutate(.data, ..., .keep = "none")
     11. │ └─dplyr:::mutate.data.frame(.data, ..., .keep = "none")
     12. │ └─dplyr:::mutate_cols(.data, ...)
     13. │ ├─base::withCallingHandlers(...)
     14. │ └─mask$eval_all_mutate(dots[[i]])
     15. ├─base::get(column)
     16. ├─base::.handleSimpleError(...)
     17. │ └─dplyr:::h(simpleError(msg, call))
     18. │ └─rlang::abort(...)
     19. │ └─rlang:::signal_abort(cnd)
     20. │ └─base::signalCondition(cnd)
     21. └─(function (cnd) ...
    
     ── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     ── Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit ──────────────
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     7 successful models have been tested and 21 thresholds evaluated
    
     Model rmse Threshold success_rate ti_error tii_error
     1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
     2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
     3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
     4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
     5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
     6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
     7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     [1] "\n"── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     ── Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit ──────────────
     Some predictor variables are on very different scales: consider rescaling
    
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
    
     # weights: 37
     initial value 314.113022
     iter 10 value 305.860086
     iter 20 value 305.236595
     iter 30 value 305.199531
     final value 305.199440
     converged
     # weights: 73
     initial value 331.204920
     iter 10 value 277.703814
     iter 20 value 264.464583
     iter 30 value 235.346147
     iter 40 value 213.675019
     iter 50 value 204.512635
     iter 60 value 187.308074
     iter 70 value 147.436155
     iter 80 value 134.805399
     iter 90 value 128.131335
     iter 100 value 127.540544
     final value 127.539685
     converged
     # weights: 109
     initial value 313.022965
     iter 10 value 293.213546
     iter 20 value 276.666037
     iter 30 value 272.763270
     iter 40 value 271.860014
     iter 50 value 271.852731
     final value 271.852717
     converged
     # weights: 145
     initial value 342.699156
     iter 10 value 266.493159
     iter 20 value 248.027579
     iter 30 value 202.156187
     iter 40 value 181.633139
     iter 50 value 165.106980
     iter 60 value 146.805052
     iter 70 value 131.481835
     iter 80 value 118.560985
     iter 90 value 113.589950
     iter 100 value 111.521717
     iter 110 value 109.315739
     iter 120 value 108.445079
     iter 130 value 107.625920
     iter 140 value 106.688656
     iter 150 value 105.653618
     iter 160 value 105.376674
     iter 170 value 105.364509
     final value 105.364086
     converged
     # weights: 181
     initial value 431.964891
     iter 10 value 268.804053
     iter 20 value 257.332978
     iter 30 value 249.853001
     iter 40 value 185.335385
     iter 50 value 153.782864
     iter 60 value 139.784881
     iter 70 value 126.922475
     iter 80 value 123.252145
     iter 90 value 120.348628
     iter 100 value 116.840543
     iter 110 value 112.093661
     iter 120 value 106.440800
     iter 130 value 105.233650
     iter 140 value 105.222728
     final value 105.198137
     converged
     # weights: 217
     initial value 364.112551
     iter 10 value 274.040840
     iter 20 value 260.171164
     iter 30 value 252.654051
     iter 40 value 245.185007
     iter 50 value 239.105471
     iter 60 value 225.547657
     iter 70 value 208.619569
     iter 80 value 196.251825
     iter 90 value 188.803731
     iter 100 value 187.520201
     iter 110 value 180.509639
     iter 120 value 173.309374
     iter 130 value 162.696568
     iter 140 value 158.906941
     iter 150 value 157.467273
     iter 160 value 157.011874
     iter 170 value 156.566607
     iter 180 value 154.830641
     iter 190 value 154.706559
     iter 200 value 154.475990
     iter 210 value 153.619213
     iter 220 value 149.786054
     iter 230 value 147.795144
     iter 240 value 147.083513
     iter 250 value 145.964459
     iter 260 value 145.865523
     iter 270 value 145.820803
     iter 280 value 145.792567
     iter 290 value 145.721832
     iter 300 value 145.610319
     iter 310 value 145.570264
     iter 320 value 145.522248
     iter 330 value 145.455157
     iter 340 value 145.350623
     iter 350 value 145.244467
     iter 360 value 145.203358
     iter 370 value 145.184359
     iter 380 value 145.176312
     iter 390 value 145.169123
     iter 400 value 145.154012
     iter 410 value 145.147479
     iter 420 value 145.127146
     iter 430 value 145.091879
     iter 440 value 145.074548
     iter 450 value 145.026713
     iter 460 value 145.016682
     iter 470 value 145.010994
     iter 480 value 144.949362
     iter 490 value 144.786727
     iter 500 value 144.760616
     final value 144.760616
     stopped after 500 iterations
     6 models have been tested with differents levels of hidden layers
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 4 0.3236595 1 0.5950413 0.4049587 0
     2 5 0.3279188 1 0.5950413 0.4049587 0
     3 2 0.3613537 1 0.5950413 0.4049587 0
     4 6 0.4056531 1 0.5950413 0.4049587 0
     5 3 0.4793713 1 0.5950413 0.4049587 0
     6 1 0.5048849 1 0.5950413 0.4049587 06 successful models have been tested
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 4 0.3236595 1 0.5950413 0.4049587 0
     2 5 0.3279188 1 0.5950413 0.4049587 0
     3 2 0.3613537 1 0.5950413 0.4049587 0
     4 6 0.4056531 1 0.5950413 0.4049587 0
     5 3 0.4793713 1 0.5950413 0.4049587 0
     6 1 0.5048849 1 0.5950413 0.4049587 0
     4 successful kernels have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
     ══ testthat results ═══════════════════════════════════════════════════════════
     ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
    
     [ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
     Error: Test failures
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.1.5
Check: tests
Result: ERROR
     Running 'testthat.R' [14s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(OptimClassifier)
     >
     > test_check("OptimClassifier")
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1
     6 successful models have been tested
    
     CP rmse success_rate ti_error tii_error Nnodes
     1 0.002262443 0.3602883 0.8701923 0.04807692 0.08173077 17
     2 0.009049774 0.3466876 0.8798077 0.03846154 0.08173077 15
     3 0.011312217 0.3325311 0.8894231 0.04807692 0.06250000 11
     4 0.013574661 0.3325311 0.8894231 0.05769231 0.05288462 9
     5 0.022624434 0.3535534 0.8750000 0.03365385 0.09134615 3
     6 0.665158371 0.6430097 0.5865385 0.41346154 0.00000000 1Call:
     rpart::rpart(formula = formula, data = training, na.action = rpart::na.rpart,
     model = FALSE, x = FALSE, y = FALSE, cp = 0)
     n= 482
    
     CP nsplit rel error xerror xstd
     1 0.66515837 0 1.0000000 1.0000000 0.04949948
     2 0.02262443 1 0.3348416 0.3348416 0.03581213
     3 0.01357466 4 0.2669683 0.3438914 0.03620379
     4 0.01131222 5 0.2533937 0.3438914 0.03620379
    
     Variable importance
     X8 X10 X9 X7 X5 X14 X13 X6 X12 X3
     36 16 16 13 10 6 2 1 1 1
    
     Node number 1: 482 observations, complexity param=0.6651584
     predicted class=0 expected loss=0.4585062 P(node) =1
     class counts: 261 221
     probabilities: 0.541 0.459
     left son=2 (219 obs) right son=3 (263 obs)
     Primary splits:
     X8 splits as LR, improve=119.25990, (0 missing)
     X10 < 2.5 to the left, improve= 52.61262, (0 missing)
     X9 splits as LR, improve= 47.76803, (0 missing)
     X14 < 396 to the left, improve= 32.46584, (0 missing)
     X7 < 1.0425 to the left, improve= 31.53528, (0 missing)
     Surrogate splits:
     X9 splits as LR, agree=0.701, adj=0.342, (0 split)
     X10 < 0.5 to the left, agree=0.701, adj=0.342, (0 split)
     X7 < 0.435 to the left, agree=0.699, adj=0.338, (0 split)
     X5 splits as LLLLRRRRRRRRRR, agree=0.641, adj=0.210, (0 split)
     X14 < 127 to the left, agree=0.606, adj=0.132, (0 split)
    
     Node number 2: 219 observations
     predicted class=0 expected loss=0.07305936 P(node) =0.4543568
     class counts: 203 16
     probabilities: 0.927 0.073
    
     Node number 3: 263 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.2205323 P(node) =0.5456432
     class counts: 58 205
     probabilities: 0.221 0.779
     left son=6 (99 obs) right son=7 (164 obs)
     Primary splits:
     X9 splits as LR, improve=11.902240, (0 missing)
     X10 < 0.5 to the left, improve=11.902240, (0 missing)
     X14 < 216.5 to the left, improve=10.195680, (0 missing)
     X5 splits as LLLLLLRRRRRRRR, improve= 7.627675, (0 missing)
     X13 < 72.5 to the right, improve= 6.568284, (0 missing)
     Surrogate splits:
     X10 < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
     X14 < 3 to the left, agree=0.722, adj=0.263, (0 split)
     X12 splits as LR-, agree=0.688, adj=0.172, (0 split)
     X5 splits as RLRLRLRRRRRRRR, agree=0.665, adj=0.111, (0 split)
     X7 < 0.27 to the left, agree=0.665, adj=0.111, (0 split)
    
     Node number 6: 99 observations, complexity param=0.02262443
     predicted class=1 expected loss=0.4141414 P(node) =0.2053942
     class counts: 41 58
     probabilities: 0.414 0.586
     left son=12 (61 obs) right son=13 (38 obs)
     Primary splits:
     X13 < 111 to the right, improve=6.520991, (0 missing)
     X5 splits as LLLLLLLLRRRRRR, improve=5.770954, (0 missing)
     X6 splits as LRLRL-RR, improve=4.176207, (0 missing)
     X14 < 388.5 to the left, improve=3.403553, (0 missing)
     X3 < 2.52 to the left, improve=2.599301, (0 missing)
     Surrogate splits:
     X3 < 4.5625 to the left, agree=0.697, adj=0.211, (0 split)
     X2 < 22.835 to the right, agree=0.677, adj=0.158, (0 split)
     X5 splits as RRLLRLLLLRRLLL, agree=0.667, adj=0.132, (0 split)
     X7 < 0.02 to the right, agree=0.667, adj=0.132, (0 split)
     X6 splits as RRRLL-LR, agree=0.657, adj=0.105, (0 split)
    
     Node number 7: 164 observations
     predicted class=1 expected loss=0.1036585 P(node) =0.340249
     class counts: 17 147
     probabilities: 0.104 0.896
    
     Node number 12: 61 observations, complexity param=0.02262443
     predicted class=0 expected loss=0.442623 P(node) =0.126556
     class counts: 34 27
     probabilities: 0.557 0.443
     left son=24 (49 obs) right son=25 (12 obs)
     Primary splits:
     X5 splits as LLLL-LLLRLRRLL, improve=4.5609460, (0 missing)
     X14 < 126 to the left, improve=3.7856330, (0 missing)
     X6 splits as L--LL-R-, improve=3.2211680, (0 missing)
     X3 < 9.625 to the right, improve=1.0257110, (0 missing)
     X2 < 24.5 to the right, improve=0.9861812, (0 missing)
     Surrogate splits:
     X14 < 2202.5 to the left, agree=0.836, adj=0.167, (0 split)
     X3 < 11.3125 to the left, agree=0.820, adj=0.083, (0 split)
    
     Node number 13: 38 observations
     predicted class=1 expected loss=0.1842105 P(node) =0.07883817
     class counts: 7 31
     probabilities: 0.184 0.816
    
     Node number 24: 49 observations, complexity param=0.01357466
     predicted class=0 expected loss=0.3469388 P(node) =0.1016598
     class counts: 32 17
     probabilities: 0.653 0.347
     left son=48 (34 obs) right son=49 (15 obs)
     Primary splits:
     X6 splits as L--LL-R-, improve=2.7687880, (0 missing)
     X13 < 150 to the left, improve=2.3016430, (0 missing)
     X14 < 126 to the left, improve=2.2040820, (0 missing)
     X3 < 4.4575 to the right, improve=1.5322870, (0 missing)
     X5 splits as LLRL-LRR-R--RR, improve=0.8850852, (0 missing)
     Surrogate splits:
     X2 < 50.415 to the left, agree=0.735, adj=0.133, (0 split)
     X5 splits as LLLL-LLL-L--LR, agree=0.735, adj=0.133, (0 split)
     X7 < 2.625 to the left, agree=0.735, adj=0.133, (0 split)
    
     Node number 25: 12 observations
     predicted class=1 expected loss=0.1666667 P(node) =0.02489627
     class counts: 2 10
     probabilities: 0.167 0.833
    
     Node number 48: 34 observations
     predicted class=0 expected loss=0.2352941 P(node) =0.07053942
     class counts: 26 8
     probabilities: 0.765 0.235
    
     Node number 49: 15 observations
     predicted class=1 expected loss=0.4 P(node) =0.03112033
     class counts: 6 9
     probabilities: 0.400 0.600
    
     -- ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit ----------
     Error: arrange() failed at implicit mutate() step.
     x Could not create a temporary column for `..1`.
     i `..1` is `get(column)`.
     Backtrace:
     x
     1. +-OptimClassifier::Optim.DA(Y ~ ., AustralianCredit, p = 0.8, seed = 2018) test-OptimDA.R:4:2
     2. | \-OptimClassifier:::OrderModels(summary_models, criteria)
     3. | +-base::ifelse(...)
     4. | +-dplyr::arrange(summary_table, get(column))
     5. | \-dplyr:::arrange.data.frame(summary_table, get(column))
     6. | \-dplyr:::arrange_rows(.data, dots)
     7. | +-base::withCallingHandlers(...)
     8. | +-dplyr::transmute(new_data_frame(.data), !!!quosures)
     9. | \-dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
     10. | +-dplyr::mutate(.data, ..., .keep = "none")
     11. | \-dplyr:::mutate.data.frame(.data, ..., .keep = "none")
     12. | \-dplyr:::mutate_cols(.data, ...)
     13. | +-base::withCallingHandlers(...)
     14. | \-mask$eval_all_mutate(dots[[i]])
     15. +-base::get(column)
     16. +-base::.handleSimpleError(...)
     17. | \-dplyr:::h(simpleError(msg, call))
     18. | \-rlang::abort(...)
     19. | \-rlang:::signal_abort(cnd)
     20. | \-base::signalCondition(cnd)
     21. \-(function (cnd) ...
    
     -- Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit --------------
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     -- Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit --------------
     glm.fit: fitted probabilities numerically 0 or 1 occurred
    
     7 successful models have been tested and 21 thresholds evaluated
    
     Model rmse Threshold success_rate ti_error tii_error
     1 binomial(logit) 0.3011696 1.00 0.5865385 0.4134615 0
     2 binomial(probit) 0.3016317 1.00 0.5865385 0.4134615 0
     3 binomial(cloglog) 0.3020186 1.00 0.5865385 0.4134615 0
     4 poisson(log) 0.3032150 0.95 0.6634615 0.3365385 0
     5 poisson(sqrt) 0.3063370 0.95 0.6490385 0.3509615 0
     6 gaussian 0.3109044 0.95 0.6442308 0.3557692 0
     7 poisson 0.3111360 1.00 0.6153846 0.3846154 0
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     3 successful models have been tested
    
     Model rmse threshold success_rate ti_error tii_error
     1 LM 0.3109044 1 0.5625000 0.009615385 0.4278846
     2 SQRT.LM 0.4516999 1 0.5625000 0.009615385 0.4278846
     3 LOG.LM 1.1762341 1 0.5865385 0.413461538 0.0000000
     [1] "\n"-- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     -- Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit --------------
     Some predictor variables are on very different scales: consider rescaling
    
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308
     8 random variables have been tested
    
     Random_Variable aic bic rmse threshold success_rate ti_error
     1 X5 495.8364 600.7961 1.023786 1.70 0.8942308 0.08653846
     2 X1 497.6737 628.8733 1.035826 1.60 0.8942308 0.04807692
     3 X6 514.7091 645.9087 1.019398 1.50 0.8653846 0.04807692
     4 X11 524.0760 677.1422 1.016578 1.55 0.8750000 0.04807692
     5 X4 531.7380 684.8042 1.017809 1.30 0.8653846 0.03846154
     6 X9 534.2266 691.6661 1.016536 1.55 0.8750000 0.04807692
     7 X12 536.3424 689.4086 1.016180 1.55 0.8750000 0.04807692
     8 X8 537.4437 694.8833 1.016513 1.55 0.8750000 0.04807692
     tii_error
     1 0.01923077
     2 0.05769231
     3 0.08653846
     4 0.07692308
     5 0.09615385
     6 0.07692308
     7 0.07692308
     8 0.07692308Warning: Thresholds' criteria not selected. The success rate is defined as the default.
    
     # weights: 37
     initial value 314.113022
     iter 10 value 305.860086
     iter 20 value 305.236595
     iter 30 value 305.199531
     final value 305.199440
     converged
     # weights: 73
     initial value 331.204920
     iter 10 value 277.703814
     iter 20 value 264.464583
     iter 30 value 235.346119
     iter 40 value 213.143728
     iter 50 value 198.653605
     iter 60 value 189.722819
     iter 70 value 147.320177
     iter 80 value 136.263418
     iter 90 value 128.270425
     iter 100 value 127.663106
     iter 110 value 127.661701
     iter 110 value 127.661700
     iter 110 value 127.661700
     final value 127.661700
     converged
     # weights: 109
     initial value 313.022965
     iter 10 value 293.213546
     iter 20 value 276.666037
     iter 30 value 272.763270
     iter 40 value 271.860014
     iter 50 value 271.852731
     final value 271.852717
     converged
     # weights: 145
     initial value 342.699156
     iter 10 value 266.493159
     iter 20 value 248.027580
     iter 30 value 202.137254
     iter 40 value 182.841633
     iter 50 value 163.406077
     iter 60 value 153.712611
     iter 70 value 143.751965
     iter 80 value 136.924097
     iter 90 value 132.451409
     iter 100 value 128.008563
     iter 110 value 121.143215
     iter 120 value 118.825805
     iter 130 value 118.697294
     iter 140 value 118.365630
     iter 150 value 118.026769
     iter 160 value 117.290617
     iter 170 value 116.989570
     iter 180 value 115.674799
     iter 190 value 114.569251
     iter 200 value 114.484329
     iter 210 value 114.166832
     iter 220 value 113.601200
     iter 230 value 112.748507
     iter 240 value 111.069187
     iter 250 value 109.623750
     iter 260 value 109.507997
     iter 270 value 109.319786
     iter 280 value 109.173316
     iter 290 value 109.098379
     iter 300 value 109.080459
     iter 310 value 109.071379
     iter 320 value 109.054026
     iter 330 value 109.038041
     iter 340 value 109.023128
     iter 350 value 109.009024
     iter 360 value 109.002012
     iter 370 value 108.997458
     iter 380 value 108.985170
     iter 390 value 108.968280
     iter 400 value 108.947004
     iter 410 value 108.927032
     iter 420 value 108.841519
     final value 108.836900
     converged
     # weights: 181
     initial value 431.964891
     iter 10 value 268.804053
     iter 20 value 257.332978
     iter 30 value 249.853001
     iter 40 value 185.335385
     iter 50 value 153.782804
     iter 60 value 139.768644
     iter 70 value 128.585804
     iter 80 value 123.378576
     iter 90 value 119.778357
     iter 100 value 117.186852
     iter 110 value 116.089822
     iter 120 value 107.815280
     iter 130 value 102.184831
     iter 140 value 99.993525
     iter 150 value 99.336193
     iter 160 value 99.255967
     iter 170 value 99.255697
     iter 180 value 99.255239
     iter 180 value 99.255239
     iter 180 value 99.255239
     final value 99.255239
     converged
     # weights: 217
     initial value 364.112551
     iter 10 value 274.040840
     iter 20 value 260.171164
     iter 30 value 252.654051
     iter 40 value 245.185007
     iter 50 value 239.105792
     iter 60 value 228.177441
     iter 70 value 209.068307
     iter 80 value 189.216192
     iter 90 value 179.285289
     iter 100 value 177.101089
     iter 110 value 177.096487
     final value 177.096448
     converged
     # weights: 253
     initial value 325.866420
     iter 10 value 269.878024
     iter 20 value 260.328850
     iter 30 value 255.407336
     iter 40 value 246.177487
     iter 50 value 189.449891
     iter 60 value 148.214287
     iter 70 value 138.638245
     iter 80 value 126.342161
     iter 90 value 115.768794
     iter 100 value 106.821478
     iter 110 value 96.604319
     iter 120 value 95.772744
     iter 130 value 94.530406
     iter 140 value 93.117235
     iter 150 value 92.779526
     iter 160 value 92.426473
     iter 170 value 91.639027
     iter 180 value 89.777304
     iter 190 value 89.146231
     iter 200 value 89.048580
     iter 210 value 89.043519
     iter 220 value 88.987536
     iter 230 value 88.922038
     iter 240 value 88.919203
     iter 250 value 88.868516
     iter 260 value 88.866360
     iter 270 value 88.792197
     iter 280 value 88.761927
     iter 290 value 88.759643
     iter 300 value 88.756572
     iter 310 value 88.754814
     iter 320 value 88.754232
     iter 330 value 88.753935
     iter 340 value 88.744635
     iter 350 value 88.610024
     final value 88.606398
     converged
     # weights: 289
     initial value 308.589551
     iter 10 value 276.114138
     iter 20 value 271.523670
     iter 30 value 263.359821
     iter 40 value 253.586724
     iter 50 value 213.816844
     iter 60 value 191.744528
     iter 70 value 186.738090
     iter 80 value 172.224440
     iter 90 value 150.368428
     iter 100 value 113.936207
     iter 110 value 104.523212
     iter 120 value 102.944488
     iter 130 value 101.630580
     iter 140 value 97.269033
     iter 150 value 92.837710
     iter 160 value 89.914339
     iter 170 value 86.641147
     iter 180 value 81.772484
     iter 190 value 81.083576
     iter 200 value 80.411390
     iter 210 value 80.328208
     iter 220 value 80.291606
     iter 230 value 80.277154
     iter 240 value 80.274125
     iter 250 value 80.272141
     iter 260 value 80.266943
     iter 270 value 80.264131
     iter 280 value 80.263133
     iter 290 value 80.262658
     iter 300 value 80.262196
     iter 310 value 80.260835
     iter 320 value 80.256191
     iter 330 value 80.247971
     iter 340 value 80.239631
     final value 80.238950
     converged
     # weights: 325
     initial value 387.634927
     iter 10 value 268.437459
     iter 20 value 261.669527
     iter 30 value 238.226415
     iter 40 value 218.917605
     iter 50 value 207.230019
     iter 60 value 205.169683
     iter 70 value 198.999367
     iter 80 value 191.407467
     iter 90 value 176.982664
     iter 100 value 172.309578
     iter 110 value 169.081819
     iter 120 value 168.137239
     iter 130 value 167.927451
     iter 140 value 167.907049
     iter 150 value 167.864890
     iter 160 value 167.791594
     iter 170 value 167.772690
     iter 180 value 167.757983
     iter 190 value 167.750873
     iter 200 value 167.747380
     iter 210 value 167.743021
     iter 220 value 167.739851
     iter 230 value 167.737052
     iter 240 value 167.736204
     iter 250 value 167.735055
     iter 260 value 167.734493
     iter 270 value 167.733498
     iter 280 value 167.732784
     iter 290 value 167.732492
     iter 300 value 167.732100
     iter 310 value 167.731155
     iter 320 value 167.730510
     iter 330 value 167.728127
     iter 340 value 167.726124
     iter 350 value 167.724631
     iter 360 value 167.724583
     iter 370 value 167.669584
     iter 380 value 167.622086
     iter 390 value 167.612038
     iter 400 value 167.606902
     iter 410 value 167.442066
     iter 420 value 167.394360
     iter 430 value 167.386859
     iter 440 value 167.293435
     iter 450 value 167.228548
     iter 460 value 167.168939
     iter 470 value 167.077454
     iter 480 value 166.296543
     iter 490 value 165.650435
     iter 500 value 164.097554
     final value 164.097554
     stopped after 500 iterations
     9 models have been tested with differents levels of hidden layers
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 2 0.3515271 1 0.5950413 0.4049587 0.000000000
     2 5 0.3543909 1 0.5950413 0.4049587 0.000000000
     3 4 0.3565713 1 0.5950413 0.4049587 0.000000000
     4 7 0.3622068 1 0.6033058 0.3925620 0.004132231
     5 8 0.3796342 1 0.5950413 0.4049587 0.000000000
     6 9 0.4423661 1 0.5950413 0.4049587 0.000000000
     7 6 0.4491394 1 0.5950413 0.4049587 0.000000000
     8 3 0.4793713 1 0.5950413 0.4049587 0.000000000
     9 1 0.5048849 1 0.5950413 0.4049587 0.0000000009 successful models have been tested
    
     hiddenlayers rmse threshold success_rate ti_error tii_error
     1 2 0.3515271 1 0.5950413 0.4049587 0.000000000
     2 5 0.3543909 1 0.5950413 0.4049587 0.000000000
     3 4 0.3565713 1 0.5950413 0.4049587 0.000000000
     4 7 0.3622068 1 0.6033058 0.3925620 0.004132231
     5 8 0.3796342 1 0.5950413 0.4049587 0.000000000
     6 9 0.4423661 1 0.5950413 0.4049587 0.000000000
     7 6 0.4491394 1 0.5950413 0.4049587 0.000000000
     8 3 0.4793713 1 0.5950413 0.4049587 0.000000000
     9 1 0.5048849 1 0.5950413 0.4049587 0.000000000
     4 successful kernels have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.054662384 successful models have been tested
    
     Kernels rmse threshold success_rate ti_error tii_error
     1 radial 0.3351234 1.75 0.8745981 0.05787781 0.06752412
     2 linear 0.3517729 1.20 0.8617363 0.03536977 0.10289389
     3 sigmoid 0.4044390 1.30 0.8617363 0.04180064 0.09646302
     4 polynomial 0.5285653 1.15 0.8360129 0.10932476 0.05466238
     == testthat results ===========================================================
     ERROR (test-OptimDA.R:4:3): Test DA methods with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimGLM.R:5:3): Test GLM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
     Warning (test-OptimLMM.R:5:3): Test LMM with Australian Credit
    
     [ FAIL 1 | WARN 9 | SKIP 0 | PASS 12 ]
     Error: Test failures
     Execution halted
Flavor: r-devel-windows-ix86+x86_64