CRAN Package Check Results for Package bigKRLS

Last updated on 2019-04-22 07:46:34 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 3.0.5 106.03 79.46 185.49 OK
r-devel-linux-x86_64-debian-gcc 3.0.5 87.28 64.95 152.23 ERROR
r-devel-linux-x86_64-fedora-clang 3.0.5 264.06 OK
r-devel-linux-x86_64-fedora-gcc 3.0.5 244.05 ERROR
r-devel-windows-ix86+x86_64 3.0.5 337.00 158.00 495.00 ERROR
r-patched-linux-x86_64 3.0.5 99.03 76.91 175.94 ERROR
r-patched-solaris-x86 3.0.5 267.40 OK
r-release-linux-x86_64 3.0.5 102.39 75.25 177.64 OK
r-release-windows-ix86+x86_64 3.0.5 260.00 154.00 414.00 WARN
r-release-osx-x86_64 3.0.5 OK
r-oldrel-windows-ix86+x86_64 3.0.5 378.00 202.00 580.00 OK
r-oldrel-osx-x86_64 3.0.5 OK

Additional issues

clang-UBSAN gcc-UBSAN

Check Details

Version: 3.0.5
Check: tests
Result: ERROR
     Running ‘testthat.R’ [17s/23s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(bigKRLS)
     Loading required package: bigmemory
    
    
     bigKRLS is authored by Pete Mohanty (Stanford University) and Robert Shaffer (University of Pennsylvania) under License GPL 3.
    
     To get started, check out vignette("bigKRLS_basics").
    
     If you use this package in your research, kindly cite:
    
    
     Pete Mohanty and Robert Shaffer. 2018. 'Messy Data, Robust Inference? Navigating Obstacles to Inference with bigKRLS.' Political Analysis. Cambridge University Press. DOI=10.1017/pan.2018.33. pages 1-18.
     >
     > # have to disable R_TESTS environment variable or parallel::makePSOCKcluster hangs.
     > Sys.setenv("R_TESTS" = "")
     >
     >
     > test_check("bigKRLS")
     ..........................
     All done. You may wish to use summary() for more detail, predict() for out-of-sample forecasts, or shiny.bigKRLS() to interact with results. For an alternative approach, see help(crossvalidate.bigKRLS). Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     MODEL SUMMARY:
    
     lambda: 0.1306
     N: 32
     N Effective: 15.17858
     R2: 0.9405
     R2AME**: 0.8477
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     cyl -0.0733 0.0765 -0.9581 0.3806
     disp -0.0037 0.0022 -1.6485 0.1582
     hp -0.0145 0.0040 -3.5903 0.0148
     drat 1.2786 0.6303 2.0287 0.0963
     wt -1.4940 0.3115 -4.7968 0.0045
     qsec 0.3678 0.1741 2.1123 0.0864
     vs* 1.2772 0.6234 2.0488 0.0938
     am* 0.9967 0.5954 1.6739 0.1530
     gear 1.0847 0.2867 3.7828 0.0120
     carb -0.5951 0.1518 -3.9200 0.0104
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     cyl -0.3219 -0.1071 -0.0182 0.0065 0.0406
     disp -0.0108 -0.0080 -0.0040 -0.0011 0.0044
     hp -0.0372 -0.0258 -0.0138 -0.0009 0.0038
     drat -0.4892 0.6973 1.1559 2.1076 3.4894
     wt -3.5756 -1.9904 -1.2308 -0.7641 -0.2853
     qsec -0.1250 0.0504 0.2099 0.5900 1.2423
     vs* -0.3757 0.8754 1.2843 1.8167 2.6408
     am* -0.6929 0.3432 0.8767 1.8618 2.7406
     gear -0.4504 -0.1866 0.8420 1.9432 3.4149
     carb -1.1902 -1.0159 -0.5844 -0.2336 0.0107
    
     (*) Reported average and percentiles of dy/dx is for discrete change of the dummy variable from min to max (usually 0 to 1)).
    
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     Saving model estimates to:
    
     bigKRLS_test_results
    
    
     0 matrices saved as big matrices (base R save() may be used safely in this case too).
     Smaller, base R elements of the outputted object saved: bigKRLS_test_results/estimates.RData
    
     Total file size approximately 0 megabytes.
     .
     model estimates will be saved to:
    
     bigKRLS_test_bigmemory_results
    
     ................
     saving vcovmatyhat to /home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/bigKRLS.Rcheck/tests/testthat
     vcovmatyhat successfully saved to disk (and removed from memory for speed).
     ..........
     saving output to bigKRLS_test_bigmemory_results
     writing bigKRLS_test_bigmemory_results/X.txt ...
     writing bigKRLS_test_bigmemory_results/K.txt ...
     writing bigKRLS_test_bigmemory_results/vcov.est.c.txt ...
     writing bigKRLS_test_bigmemory_results/derivatives.txt ...
    
    
     5 matrices saved as big matrices.
    
     to reload, use syntax like:
    
     load.bigKRLS("/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/bigKRLS.Rcheck/tests/testthat/bigKRLS_test_bigmemory_results")
     or
     load.bigKRLS("/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/bigKRLS.Rcheck/tests/testthat/bigKRLS_test_bigmemory_results", newname="my_estimates")
    
     base R elements of the output saved to estimates.RData.
     Total file size approximately 0 megabytes.
    
    
     All done. You may wish to use summary() for more detail, predict() for out-of-sample forecasts, or shiny.bigKRLS() to interact with results. For an alternative approach, see help(crossvalidate.bigKRLS). Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     Reading from K.txt
     Reading from X.txt
     Reading from derivatives.txt
     Reading from vcov.est.c.txt
     NOTE: vcov.est.fitted not found in .RData or in big matrix file, vcov.est.fitted.txt .
    
     ............................
     Overview of Model Performance
    
     N: 32
     Seed: 123
    
     In Sample Out of Sample
     Mean Squared Error (Full Model) 1.125 7.532
     Mean Squared Error (Average Marginal Effects Only) 282.697 302.770
     Pseudo-R^2 (Full Model) 0.971 0.919
     Pseudo-R^2 (Average Marginal Effects Only) 0.860 0.866
    
     N 26.000 6.000
    
    
     Summary of Training Model:
    
    
     MODEL SUMMARY:
    
     lambda: 0.0749
     N: 26
     N Effective: 8.693671
     R2: 0.9713
     R2AME**: 0.8595
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     cyl -0.0308 0.0738 -0.4177 NaN
     disp -0.0013 0.0019 -0.6889 NaN
     hp -0.0214 0.0036 -5.9445 NaN
     drat 0.7105 0.5552 1.2797 NaN
     wt -2.1883 0.2698 -8.1106 NaN
     qsec 0.4290 0.1427 3.0069 NaN
     vs* 1.6259 0.5522 2.9445 NaN
     am* 0.8126 0.5254 1.5466 NaN
     gear 1.2481 0.2464 5.0661 NaN
     carb -0.6583 0.1271 -5.1802 NaN
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     cyl -0.2678 -0.0583 -0.0249 0.0639 0.1224
     disp -0.0157 -0.0097 -0.0034 0.0065 0.0148
     hp -0.0532 -0.0355 -0.0238 -0.0039 0.0019
     drat -0.8121 0.1667 0.6667 1.2357 3.1801
     wt -4.8466 -2.6870 -1.8917 -1.4949 -0.6539
     qsec -0.1957 0.1062 0.3431 0.7820 1.2412
     vs* 0.1074 1.0071 1.6507 2.2429 3.3543
     am* -0.8303 -0.0115 0.7399 1.5441 3.1487
     gear -0.4625 -0.0895 1.0600 2.1423 3.4811
     carb -1.4582 -1.2111 -0.5200 -0.2011 -0.0732
    
     (*) Reported average and percentiles of dy/dx is for discrete change of the dummy variable from min to max (usually 0 to 1)).
    
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     ......................................
     .....................
     ....................
     ....................
     ..................
     ..................
     ..................
     ................
     ....................
     .....................
     ....................
     ....................
     Overview of Model Performance
    
     N: 250
     Kfolds: 4
     Seed: 1234
    
     Fold 1 Fold 2 Fold 3 Fold 4
     MSE (In Sample) 0.734 0.743 0.695 0.686
     MSE (Out of Sample) 1.276 0.873 1.195 1.226
     MSE AME (In Sample) 2.695 1.984 2.540 2.209
     MSE AME (Out of Sample) 3.177 1.677 3.045 1.936
     R2 (In Sample) 0.774 0.791 0.787 0.787
     R2 (Out of Sample) 0.648 0.681 0.667 0.659
     R2 AME (In Sample) 0.742 0.731 0.732 0.750
     R2 AME (Out of Sample) 0.708 0.769 0.734 0.682
    
     MSE denotes Mean Squared Error. AME implies calculations done with Average Marginal Effects only.
    
     Summary of Training Model1:
    
    
     MODEL SUMMARY:
    
     lambda: 0.899
     N: 187
     N Effective: 156.3024
     R2: 0.7736
     R2AME**: 0.7417
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.9589 0.1905 5.0346 0
     x2 1.4519 0.1891 7.6767 0
     x3 2.0993 0.1798 11.6737 0
     x4 2.7440 0.1757 15.6168 0
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.6189 -0.0087 1.0668 1.8372 3.3296
     x2 -1.0785 0.6842 1.7075 2.3498 3.0562
     x3 -0.3417 1.2507 2.1789 3.1527 4.4687
     x4 0.7443 1.9765 2.8437 3.5838 4.4058
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model2:
    
    
     MODEL SUMMARY:
    
     lambda: 0.5778
     N: 188
     N Effective: 151.2168
     R2: 0.7908
     R2AME**: 0.7309
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.8270 0.2062 4.0110 1e-04
     x2 1.5459 0.2060 7.5055 0e+00
     x3 2.4301 0.2042 11.9033 0e+00
     x4 3.1351 0.2036 15.3949 0e+00
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.6407 -0.2399 0.9918 1.9215 3.0752
     x2 -0.4728 0.7705 1.4049 2.2533 3.8459
     x3 0.0212 1.5036 2.6631 3.3750 4.4612
     x4 0.4207 2.1119 3.3870 4.2646 5.2462
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model3:
    
    
     MODEL SUMMARY:
    
     lambda: 0.6276
     N: 188
     N Effective: 152.5316
     R2: 0.787
     R2AME**: 0.7324
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.5377 0.2009 2.6770 0.0083
     x2 1.4111 0.2024 6.9715 0.0000
     x3 2.3743 0.1888 12.5778 0.0000
     x4 2.9933 0.1911 15.6618 0.0000
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.8595 -0.2222 0.6688 1.4931 2.4555
     x2 -0.8720 0.5198 1.2902 2.5101 3.4258
     x3 0.0256 1.2932 2.2869 3.3879 4.8906
     x4 0.0714 2.1910 3.0890 4.0984 5.1052
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model4:
    
    
     MODEL SUMMARY:
    
     lambda: 0.6245
     N: 187
     N Effective: 151.8663
     R2: 0.7868
     R2AME**: 0.7503
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.8540 0.1975 4.3235 0
     x2 1.6994 0.1972 8.6181 0
     x3 2.1450 0.1909 11.2334 0
     x4 3.0553 0.1950 15.6664 0
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.1126 0.2378 0.9325 1.5662 3.0208
     x2 -0.2084 1.0239 1.7979 2.5718 3.3397
     x3 -0.1887 1.3657 2.3700 2.9835 3.8805
     x4 0.6929 2.1339 3.1462 4.0975 5.0257
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     ── 1. Failure: Kfolds crossvalidation works (@test_basic_usage.R#148) ─────────
     kcv$folds not equal to kcvbig$folds.
     2/250 mismatches (average diff: 2)
     [1] 2 - 4 == -2
     [204] 4 - 2 == 2
    
     ── 2. Failure: Kfolds crossvalidation works (@test_basic_usage.R#150) ─────────
     sum(kcv$fold_2$tested$predicted) not equal to sum(kcvbig$fold_2$tested$predicted[]).
     1/1 mismatches
     [1] 296 - 297 == -1.35
    
     ── 3. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#157
     kcv$R2_is not equal to kcvbig$R2_is.
     2/4 mismatches (average diff: 0.00769)
     [2] 0.791 - 0.790 == 0.000627
     [4] 0.787 - 0.772 == 0.014752
    
     ── 4. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#158
     kcv$R2_oos not equal to kcvbig$R2_oos.
     2/4 mismatches (average diff: 0.00807)
     [2] 0.681 - 0.690 == -0.00893
     [4] 0.659 - 0.652 == 0.00720
    
     ── 5. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#159
     kcv$R2AME_is not equal to kcvbig$R2AME_is.
     2/4 mismatches (average diff: 0.000358)
     [2] 0.731 - 0.731 == 6.97e-05
     [4] 0.750 - 0.750 == 6.47e-04
    
     ── 6. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#160
     kcv$MSE_is not equal to kcvbig$MSE_is.
     2/4 mismatches (average diff: 0.0249)
     [2] 0.743 - 0.746 == -0.00287
     [4] 0.686 - 0.733 == -0.04684
    
     ── 7. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#166
     kcv$MSE_oos not equal to kcvbig$MSE_oos.
     2/4 mismatches (average diff: 0.0209)
     [2] 0.873 - 0.849 == 0.0238
     [4] 1.226 - 1.244 == -0.0180
    
     ── 8. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#167
     kcv$MSE_AME_is not equal to kcvbig$MSE_AME_is.
     2/4 mismatches (average diff: 0.297)
     [2] 1.98 - 2.00 == -0.0115
     [4] 2.21 - 2.79 == -0.5824
    
     ── 9. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#168
     kcv$R2AME_oos not equal to kcvbig$R2AME_oos.
     2/4 mismatches (average diff: 0.00162)
     [2] 0.769 - 0.769 == -0.00024
     [4] 0.682 - 0.685 == -0.00300
    
     ── 10. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#16
     kcv$MSE_AME_oos not equal to kcvbig$MSE_AME_oos.
     2/4 mismatches (average diff: 0.304)
     [2] 1.68 - 1.61 == 0.0707
     [4] 1.94 - 2.47 == -0.5368
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 18 SKIPPED: 0 FAILED: 10
     1. Failure: Kfolds crossvalidation works (@test_basic_usage.R#148)
     2. Failure: Kfolds crossvalidation works (@test_basic_usage.R#150)
     3. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#157)
     4. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#158)
     5. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#159)
     6. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#160)
     7. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#166)
     8. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#167)
     9. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#168)
     10. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#169)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 3.0.5
Check: tests
Result: ERROR
     Running ‘testthat.R’ [23s/25s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(bigKRLS)
     Loading required package: bigmemory
    
    
     bigKRLS is authored by Pete Mohanty (Stanford University) and Robert Shaffer (University of Pennsylvania) under License GPL 3.
    
     To get started, check out vignette("bigKRLS_basics").
    
     If you use this package in your research, kindly cite:
    
    
     Pete Mohanty and Robert Shaffer. 2018. 'Messy Data, Robust Inference? Navigating Obstacles to Inference with bigKRLS.' Political Analysis. Cambridge University Press. DOI=10.1017/pan.2018.33. pages 1-18.
     >
     > # have to disable R_TESTS environment variable or parallel::makePSOCKcluster hangs.
     > Sys.setenv("R_TESTS" = "")
     >
     >
     > test_check("bigKRLS")
     ..........................
     All done. You may wish to use summary() for more detail, predict() for out-of-sample forecasts, or shiny.bigKRLS() to interact with results. For an alternative approach, see help(crossvalidate.bigKRLS). Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     MODEL SUMMARY:
    
     lambda: 0.1306
     N: 32
     N Effective: 15.17858
     R2: 0.9405
     R2AME**: 0.8477
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     cyl -0.0733 0.0765 -0.9581 0.3806
     disp -0.0037 0.0022 -1.6485 0.1582
     hp -0.0145 0.0040 -3.5903 0.0148
     drat 1.2786 0.6303 2.0287 0.0963
     wt -1.4940 0.3115 -4.7968 0.0045
     qsec 0.3678 0.1741 2.1123 0.0864
     vs* 1.2772 0.6234 2.0488 0.0938
     am* 0.9967 0.5954 1.6739 0.1530
     gear 1.0847 0.2867 3.7828 0.0120
     carb -0.5951 0.1518 -3.9200 0.0104
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     cyl -0.3219 -0.1071 -0.0182 0.0065 0.0406
     disp -0.0108 -0.0080 -0.0040 -0.0011 0.0044
     hp -0.0372 -0.0258 -0.0138 -0.0009 0.0038
     drat -0.4892 0.6973 1.1559 2.1076 3.4894
     wt -3.5756 -1.9904 -1.2308 -0.7641 -0.2853
     qsec -0.1250 0.0504 0.2099 0.5900 1.2423
     vs* -0.3757 0.8754 1.2843 1.8167 2.6408
     am* -0.6929 0.3432 0.8767 1.8618 2.7406
     gear -0.4504 -0.1866 0.8420 1.9432 3.4149
     carb -1.1902 -1.0159 -0.5844 -0.2336 0.0107
    
     (*) Reported average and percentiles of dy/dx is for discrete change of the dummy variable from min to max (usually 0 to 1)).
    
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     Saving model estimates to:
    
     bigKRLS_test_results
    
    
     0 matrices saved as big matrices (base R save() may be used safely in this case too).
     Smaller, base R elements of the outputted object saved: bigKRLS_test_results/estimates.RData
    
     Total file size approximately 0 megabytes.
     .
     model estimates will be saved to:
    
     bigKRLS_test_bigmemory_results
    
     ................
     saving vcovmatyhat to /data/gannet/ripley/R/packages/tests-devel/bigKRLS.Rcheck/tests/testthat
     vcovmatyhat successfully saved to disk (and removed from memory for speed).
     ..........
     saving output to bigKRLS_test_bigmemory_results
     writing bigKRLS_test_bigmemory_results/X.txt ...
     writing bigKRLS_test_bigmemory_results/K.txt ...
     writing bigKRLS_test_bigmemory_results/vcov.est.c.txt ...
     writing bigKRLS_test_bigmemory_results/derivatives.txt ...
    
    
     5 matrices saved as big matrices.
    
     to reload, use syntax like:
    
     load.bigKRLS("/data/gannet/ripley/R/packages/tests-devel/bigKRLS.Rcheck/tests/testthat/bigKRLS_test_bigmemory_results")
     or
     load.bigKRLS("/data/gannet/ripley/R/packages/tests-devel/bigKRLS.Rcheck/tests/testthat/bigKRLS_test_bigmemory_results", newname="my_estimates")
    
     base R elements of the output saved to estimates.RData.
     Total file size approximately 0 megabytes.
    
    
     All done. You may wish to use summary() for more detail, predict() for out-of-sample forecasts, or shiny.bigKRLS() to interact with results. For an alternative approach, see help(crossvalidate.bigKRLS). Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     Reading from K.txt
     Reading from X.txt
     Reading from derivatives.txt
     Reading from vcov.est.c.txt
     NOTE: vcov.est.fitted not found in .RData or in big matrix file, vcov.est.fitted.txt .
    
     ............................
     Overview of Model Performance
    
     N: 32
     Seed: 123
    
     In Sample Out of Sample
     Mean Squared Error (Full Model) 1.125 7.532
     Mean Squared Error (Average Marginal Effects Only) 282.697 302.770
     Pseudo-R^2 (Full Model) 0.971 0.919
     Pseudo-R^2 (Average Marginal Effects Only) 0.860 0.866
    
     N 26.000 6.000
    
    
     Summary of Training Model:
    
    
     MODEL SUMMARY:
    
     lambda: 0.0749
     N: 26
     N Effective: 8.693671
     R2: 0.9713
     R2AME**: 0.8595
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     cyl -0.0308 0.0738 -0.4177 NaN
     disp -0.0013 0.0019 -0.6889 NaN
     hp -0.0214 0.0036 -5.9445 NaN
     drat 0.7105 0.5552 1.2797 NaN
     wt -2.1883 0.2698 -8.1106 NaN
     qsec 0.4290 0.1427 3.0069 NaN
     vs* 1.6259 0.5522 2.9445 NaN
     am* 0.8126 0.5254 1.5466 NaN
     gear 1.2481 0.2464 5.0661 NaN
     carb -0.6583 0.1271 -5.1802 NaN
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     cyl -0.2678 -0.0583 -0.0249 0.0639 0.1224
     disp -0.0157 -0.0097 -0.0034 0.0065 0.0148
     hp -0.0532 -0.0355 -0.0238 -0.0039 0.0019
     drat -0.8121 0.1667 0.6667 1.2357 3.1801
     wt -4.8466 -2.6870 -1.8917 -1.4949 -0.6539
     qsec -0.1957 0.1062 0.3431 0.7820 1.2412
     vs* 0.1074 1.0071 1.6507 2.2429 3.3543
     am* -0.8303 -0.0115 0.7399 1.5441 3.1487
     gear -0.4625 -0.0895 1.0600 2.1423 3.4811
     carb -1.4582 -1.2111 -0.5200 -0.2011 -0.0732
    
     (*) Reported average and percentiles of dy/dx is for discrete change of the dummy variable from min to max (usually 0 to 1)).
    
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     ......................................
     .....................
     ....................
     ....................
     ................
     .................
     ................
     ................
     ......................
     ......................
     ......................
     ....................
     Overview of Model Performance
    
     N: 250
     Kfolds: 4
     Seed: 1234
    
     Fold 1 Fold 2 Fold 3 Fold 4
     MSE (In Sample) 0.734 0.743 0.695 0.686
     MSE (Out of Sample) 1.276 0.873 1.195 1.226
     MSE AME (In Sample) 2.695 1.984 2.540 2.209
     MSE AME (Out of Sample) 3.177 1.677 3.045 1.936
     R2 (In Sample) 0.774 0.791 0.787 0.787
     R2 (Out of Sample) 0.648 0.681 0.667 0.659
     R2 AME (In Sample) 0.742 0.731 0.732 0.750
     R2 AME (Out of Sample) 0.708 0.769 0.734 0.682
    
     MSE denotes Mean Squared Error. AME implies calculations done with Average Marginal Effects only.
    
     Summary of Training Model1:
    
    
     MODEL SUMMARY:
    
     lambda: 0.899
     N: 187
     N Effective: 156.3024
     R2: 0.7736
     R2AME**: 0.7417
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.9589 0.1905 5.0346 0
     x2 1.4519 0.1891 7.6767 0
     x3 2.0993 0.1798 11.6737 0
     x4 2.7440 0.1757 15.6168 0
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.6189 -0.0087 1.0668 1.8372 3.3296
     x2 -1.0785 0.6842 1.7075 2.3498 3.0562
     x3 -0.3417 1.2507 2.1789 3.1527 4.4687
     x4 0.7443 1.9765 2.8437 3.5838 4.4058
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model2:
    
    
     MODEL SUMMARY:
    
     lambda: 0.5778
     N: 188
     N Effective: 151.2168
     R2: 0.7908
     R2AME**: 0.7309
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.8270 0.2062 4.0110 1e-04
     x2 1.5459 0.2060 7.5055 0e+00
     x3 2.4301 0.2042 11.9033 0e+00
     x4 3.1351 0.2036 15.3949 0e+00
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.6407 -0.2399 0.9918 1.9215 3.0752
     x2 -0.4728 0.7705 1.4049 2.2533 3.8459
     x3 0.0212 1.5036 2.6631 3.3750 4.4612
     x4 0.4207 2.1119 3.3870 4.2646 5.2462
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model3:
    
    
     MODEL SUMMARY:
    
     lambda: 0.6276
     N: 188
     N Effective: 152.5316
     R2: 0.787
     R2AME**: 0.7324
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.5377 0.2009 2.6770 0.0083
     x2 1.4111 0.2024 6.9715 0.0000
     x3 2.3743 0.1888 12.5778 0.0000
     x4 2.9933 0.1911 15.6618 0.0000
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.8595 -0.2222 0.6688 1.4931 2.4555
     x2 -0.8720 0.5198 1.2902 2.5101 3.4258
     x3 0.0256 1.2932 2.2869 3.3879 4.8906
     x4 0.0714 2.1910 3.0890 4.0984 5.1052
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model4:
    
    
     MODEL SUMMARY:
    
     lambda: 0.6245
     N: 187
     N Effective: 151.8663
     R2: 0.7868
     R2AME**: 0.7503
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.8540 0.1975 4.3235 0
     x2 1.6994 0.1972 8.6181 0
     x3 2.1450 0.1909 11.2334 0
     x4 3.0553 0.1950 15.6664 0
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.1126 0.2378 0.9325 1.5662 3.0208
     x2 -0.2084 1.0239 1.7979 2.5718 3.3397
     x3 -0.1887 1.3657 2.3700 2.9835 3.8805
     x4 0.6929 2.1339 3.1462 4.0975 5.0257
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     ── 1. Failure: Kfolds crossvalidation works (@test_basic_usage.R#148) ─────────
     kcv$folds not equal to kcvbig$folds.
     183/250 mismatches (average diff: 1.73)
     [1] 2 - 3 == -1
     [2] 3 - 2 == 1
     [3] 2 - 4 == -2
     [4] 4 - 2 == 2
     [5] 2 - 3 == -1
     [10] 3 - 2 == 1
     [12] 2 - 4 == -2
     [13] 4 - 2 == 2
     [16] 2 - 4 == -2
     ...
    
     ── 2. Failure: Kfolds crossvalidation works (@test_basic_usage.R#149) ─────────
     sum(kcv$fold_1$tested$newdata) not equal to sum(kcvbig$fold_1$tested$newdata[]).
     1/1 mismatches
     [1] 134 - 123 == 11.3
    
     ── 3. Failure: Kfolds crossvalidation works (@test_basic_usage.R#150) ─────────
     sum(kcv$fold_2$tested$predicted) not equal to sum(kcvbig$fold_2$tested$predicted[]).
     1/1 mismatches
     [1] 296 - 312 == -15.9
    
     ── 4. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#157
     kcv$R2_is not equal to kcvbig$R2_is.
     4/4 mismatches (average diff: 0.0139)
     [1] 0.774 - 0.791 == -0.0173
     [2] 0.791 - 0.778 == 0.0125
     [3] 0.787 - 0.799 == -0.0115
     [4] 0.787 - 0.773 == 0.0141
    
     ── 5. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#158
     kcv$R2_oos not equal to kcvbig$R2_oos.
     4/4 mismatches (average diff: 0.042)
     [1] 0.648 - 0.729 == -0.0816
     [2] 0.681 - 0.707 == -0.0257
     [3] 0.667 - 0.626 == 0.0411
     [4] 0.659 - 0.679 == -0.0197
    
     ── 6. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#159
     kcv$R2AME_is not equal to kcvbig$R2AME_is.
     4/4 mismatches (average diff: 0.0128)
     [1] 0.742 - 0.727 == 0.01453
     [2] 0.731 - 0.730 == 0.00107
     [3] 0.732 - 0.756 == -0.02316
     [4] 0.750 - 0.738 == 0.01224
    
     ── 7. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#160
     kcv$MSE_is not equal to kcvbig$MSE_is.
     4/4 mismatches (average diff: 0.0738)
     [1] 0.734 - 0.588 == 0.1464
     [2] 0.743 - 0.759 == -0.0159
     [3] 0.695 - 0.739 == -0.0438
     [4] 0.686 - 0.776 == -0.0893
    
     ── 8. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#166
     kcv$MSE_oos not equal to kcvbig$MSE_oos.
     4/4 mismatches (average diff: 0.176)
     [1] 1.276 - 1.397 == -0.1210
     [2] 0.873 - 0.911 == -0.0378
     [3] 1.195 - 0.873 == 0.3216
     [4] 1.226 - 1.000 == 0.2255
    
     ── 9. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#167
     kcv$MSE_AME_is not equal to kcvbig$MSE_AME_is.
     4/4 mismatches (average diff: 0.514)
     [1] 2.69 - 1.84 == 0.8533
     [2] 1.98 - 1.93 == 0.0559
     [3] 2.54 - 2.20 == 0.3431
     [4] 2.21 - 3.01 == -0.8045
    
     ── 10. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#16
     kcv$R2AME_oos not equal to kcvbig$R2AME_oos.
     4/4 mismatches (average diff: 0.0468)
     [1] 0.708 - 0.759 == -0.0505
     [2] 0.769 - 0.757 == 0.0125
     [3] 0.734 - 0.658 == 0.0768
     [4] 0.682 - 0.729 == -0.0473
    
     ── 11. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#16
     kcv$MSE_AME_oos not equal to kcvbig$MSE_AME_oos.
     4/4 mismatches (average diff: 0.662)
     [1] 3.18 - 2.40 == 0.77362
     [2] 1.68 - 1.67 == 0.00907
     [3] 3.05 - 2.15 == 0.89983
     [4] 1.94 - 2.90 == -0.96532
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 18 SKIPPED: 0 FAILED: 11
     1. Failure: Kfolds crossvalidation works (@test_basic_usage.R#148)
     2. Failure: Kfolds crossvalidation works (@test_basic_usage.R#149)
     3. Failure: Kfolds crossvalidation works (@test_basic_usage.R#150)
     4. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#157)
     5. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#158)
     6. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#159)
     7. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#160)
     8. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#166)
     9. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#167)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 3.0.5
Check: running tests for arch ‘i386’
Result: ERROR
     Running 'testthat.R' [24s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(bigKRLS)
     Loading required package: bigmemory
    
    
     bigKRLS is authored by Pete Mohanty (Stanford University) and Robert Shaffer (University of Pennsylvania) under License GPL 3.
    
     To get started, check out vignette("bigKRLS_basics").
    
     If you use this package in your research, kindly cite:
    
    
     Pete Mohanty and Robert Shaffer. 2018. 'Messy Data, Robust Inference? Navigating Obstacles to Inference with bigKRLS.' Political Analysis. Cambridge University Press. DOI=10.1017/pan.2018.33. pages 1-18.
     >
     > # have to disable R_TESTS environment variable or parallel::makePSOCKcluster hangs.
     > Sys.setenv("R_TESTS" = "")
     >
     >
     > test_check("bigKRLS")
     ..........................
     All done. You may wish to use summary() for more detail, predict() for out-of-sample forecasts, or shiny.bigKRLS() to interact with results. For an alternative approach, see help(crossvalidate.bigKRLS). Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     MODEL SUMMARY:
    
     lambda: 0.1306
     N: 32
     N Effective: 15.17858
     R2: 0.9405
     R2AME**: 0.8477
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     cyl -0.0733 0.0765 -0.9581 0.3806
     disp -0.0037 0.0022 -1.6485 0.1582
     hp -0.0145 0.0040 -3.5903 0.0148
     drat 1.2786 0.6303 2.0287 0.0963
     wt -1.4940 0.3115 -4.7968 0.0045
     qsec 0.3678 0.1741 2.1123 0.0864
     vs* 1.2772 0.6234 2.0488 0.0938
     am* 0.9967 0.5954 1.6739 0.1530
     gear 1.0847 0.2867 3.7828 0.0120
     carb -0.5951 0.1518 -3.9200 0.0104
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     cyl -0.3219 -0.1071 -0.0182 0.0065 0.0406
     disp -0.0108 -0.0080 -0.0040 -0.0011 0.0044
     hp -0.0372 -0.0258 -0.0138 -0.0009 0.0038
     drat -0.4892 0.6973 1.1559 2.1076 3.4894
     wt -3.5756 -1.9904 -1.2308 -0.7641 -0.2853
     qsec -0.1250 0.0504 0.2099 0.5900 1.2423
     vs* -0.3757 0.8754 1.2843 1.8167 2.6408
     am* -0.6929 0.3432 0.8767 1.8618 2.7406
     gear -0.4504 -0.1866 0.8420 1.9432 3.4149
     carb -1.1902 -1.0159 -0.5844 -0.2336 0.0107
    
     (*) Reported average and percentiles of dy/dx is for discrete change of the dummy variable from min to max (usually 0 to 1)).
    
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     Saving model estimates to:
    
     bigKRLS_test_results
    
    
     0 matrices saved as big matrices (base R save() may be used safely in this case too).
     Smaller, base R elements of the outputted object saved: bigKRLS_test_results/estimates.RData
    
     Total file size approximately 0 megabytes.
     .
     model estimates will be saved to:
    
     bigKRLS_test_bigmemory_results
    
     ................
     saving vcovmatyhat to d:/Rcompile/CRANpkg/local/3.6/bigKRLS.Rcheck/tests_i386/testthat
     vcovmatyhat successfully saved to disk (and removed from memory for speed).
     ..........
     saving output to bigKRLS_test_bigmemory_results
     writing bigKRLS_test_bigmemory_results/X.txt ...
     writing bigKRLS_test_bigmemory_results/K.txt ...
     writing bigKRLS_test_bigmemory_results/vcov.est.c.txt ...
     writing bigKRLS_test_bigmemory_results/derivatives.txt ...
    
    
     5 matrices saved as big matrices.
    
     to reload, use syntax like:
    
     load.bigKRLS("d:\RCompile\CRANpkg\local\3.6\bigKRLS.Rcheck\tests_i386\testthat\bigKRLS_test_bigmemory_results")
     or
     load.bigKRLS("d:\RCompile\CRANpkg\local\3.6\bigKRLS.Rcheck\tests_i386\testthat\bigKRLS_test_bigmemory_results", newname="my_estimates")
    
     base R elements of the output saved to estimates.RData.
     Total file size approximately 0 megabytes.
    
    
     All done. You may wish to use summary() for more detail, predict() for out-of-sample forecasts, or shiny.bigKRLS() to interact with results. For an alternative approach, see help(crossvalidate.bigKRLS). Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     Reading from K.txt
     Reading from X.txt
     Reading from derivatives.txt
     Reading from vcov.est.c.txt
     NOTE: vcov.est.fitted not found in .RData or in big matrix file, vcov.est.fitted.txt .
    
     ............................
     Overview of Model Performance
    
     N: 32
     Seed: 123
    
     In Sample Out of Sample
     Mean Squared Error (Full Model) 1.125 7.532
     Mean Squared Error (Average Marginal Effects Only) 282.697 302.770
     Pseudo-R^2 (Full Model) 0.971 0.919
     Pseudo-R^2 (Average Marginal Effects Only) 0.860 0.866
    
     N 26.000 6.000
    
    
     Summary of Training Model:
    
    
     MODEL SUMMARY:
    
     lambda: 0.0749
     N: 26
     N Effective: 8.693671
     R2: 0.9713
     R2AME**: 0.8595
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     cyl -0.0308 0.0738 -0.4177 NaN
     disp -0.0013 0.0019 -0.6889 NaN
     hp -0.0214 0.0036 -5.9445 NaN
     drat 0.7105 0.5552 1.2797 NaN
     wt -2.1883 0.2698 -8.1106 NaN
     qsec 0.4290 0.1427 3.0069 NaN
     vs* 1.6259 0.5522 2.9445 NaN
     am* 0.8126 0.5254 1.5466 NaN
     gear 1.2481 0.2464 5.0661 NaN
     carb -0.6583 0.1271 -5.1802 NaN
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     cyl -0.2678 -0.0583 -0.0249 0.0639 0.1224
     disp -0.0157 -0.0097 -0.0034 0.0065 0.0148
     hp -0.0532 -0.0355 -0.0238 -0.0039 0.0019
     drat -0.8121 0.1667 0.6667 1.2357 3.1801
     wt -4.8466 -2.6870 -1.8917 -1.4949 -0.6539
     qsec -0.1957 0.1062 0.3431 0.7820 1.2412
     vs* 0.1074 1.0071 1.6507 2.2429 3.3543
     am* -0.8303 -0.0115 0.7399 1.5441 3.1487
     gear -0.4625 -0.0895 1.0600 2.1423 3.4811
     carb -1.4582 -1.2111 -0.5200 -0.2011 -0.0732
    
     (*) Reported average and percentiles of dy/dx is for discrete change of the dummy variable from min to max (usually 0 to 1)).
    
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     .......................-- 1. Failure: crossvalidation function works (@test_basic_usage.R#133) -------
     cv$pseudoR2_oos not equal to cv_noderivs$pseudoR2_oos.
     1/1 mismatches
     [1] 0.919 - 0.498 == 0.421
    
     ....................
     .....................
     ....................
     ....................
     ................
     .................
     ................
     ................
     ....................
     .....................
     ....................
     ....................
     Overview of Model Performance
    
     N: 250
     Kfolds: 4
     Seed: 1234
    
     Fold 1 Fold 2 Fold 3 Fold 4
     MSE (In Sample) 0.734 0.746 0.695 0.733
     MSE (Out of Sample) 1.276 0.849 1.195 1.244
     MSE AME (In Sample) 2.695 1.995 2.540 2.791
     MSE AME (Out of Sample) 3.177 1.606 3.045 2.473
     R2 (In Sample) 0.774 0.790 0.787 0.772
     R2 (Out of Sample) 0.648 0.690 0.667 0.652
     R2 AME (In Sample) 0.742 0.731 0.732 0.750
     R2 AME (Out of Sample) 0.708 0.769 0.734 0.685
    
     MSE denotes Mean Squared Error. AME implies calculations done with Average Marginal Effects only.
    
     Summary of Training Model1:
    
    
     MODEL SUMMARY:
    
     lambda: 0.899
     N: 187
     N Effective: 156.3024
     R2: 0.7736
     R2AME**: 0.7417
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.9589 0.1905 5.0346 0
     x2 1.4519 0.1891 7.6767 0
     x3 2.0993 0.1798 11.6737 0
     x4 2.7440 0.1757 15.6168 0
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.6189 -0.0087 1.0668 1.8372 3.3296
     x2 -1.0785 0.6842 1.7075 2.3498 3.0562
     x3 -0.3417 1.2507 2.1789 3.1527 4.4687
     x4 0.7443 1.9765 2.8437 3.5838 4.4058
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model2:
    
    
     MODEL SUMMARY:
    
     lambda: 0.5778
     N: 188
     N Effective: 151.1978
     R2: 0.7902
     R2AME**: 0.7308
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.7756 0.2061 3.7637 2e-04
     x2 1.5853 0.2077 7.6327 0e+00
     x3 2.4326 0.2053 11.8491 0e+00
     x4 3.1498 0.2043 15.4139 0e+00
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.6653 -0.3349 0.9298 1.8751 3.0522
     x2 -0.3976 0.8668 1.3767 2.3247 3.8451
     x3 0.0959 1.5179 2.6396 3.3611 4.4275
     x4 0.3016 2.0586 3.4568 4.3551 5.2430
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model3:
    
    
     MODEL SUMMARY:
    
     lambda: 0.6276
     N: 188
     N Effective: 152.5316
     R2: 0.787
     R2AME**: 0.7324
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.5377 0.2009 2.6770 0.0083
     x2 1.4111 0.2024 6.9715 0.0000
     x3 2.3743 0.1888 12.5778 0.0000
     x4 2.9933 0.1911 15.6618 0.0000
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.8595 -0.2222 0.6688 1.4931 2.4555
     x2 -0.8720 0.5198 1.2902 2.5101 3.4258
     x3 0.0256 1.2932 2.2869 3.3879 4.8906
     x4 0.0714 2.1910 3.0890 4.0984 5.1052
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model4:
    
    
     MODEL SUMMARY:
    
     lambda: 0.9487
     N: 187
     N Effective: 157.1787
     R2: 0.7721
     R2AME**: 0.7497
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.8128 0.1854 4.3831 0
     x2 1.5713 0.1857 8.4612 0
     x3 2.0508 0.1754 11.6948 0
     x4 2.8821 0.1817 15.8639 0
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.2934 0.0750 0.9622 1.5400 2.8176
     x2 -0.5135 0.7996 1.6848 2.4301 3.0530
     x3 -0.3719 1.2459 2.2440 2.9152 3.8446
     x4 0.4845 2.0039 2.9042 3.9110 4.8957
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     -- 2. Failure: Kfolds crossvalidation works (@test_basic_usage.R#148) ---------
     kcv$folds not equal to kcvbig$folds.
     2/250 mismatches (average diff: 2)
     [1] 4 - 2 == 2
     [204] 2 - 4 == -2
    
     -- 3. Failure: Kfolds crossvalidation works (@test_basic_usage.R#150) ---------
     sum(kcv$fold_2$tested$predicted) not equal to sum(kcvbig$fold_2$tested$predicted[]).
     1/1 mismatches
     [1] 297 - 296 == 1.35
    
     -- 4. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#157
     kcv$R2_is not equal to kcvbig$R2_is.
     2/4 mismatches (average diff: 0.00769)
     [2] 0.790 - 0.791 == -0.000627
     [4] 0.772 - 0.787 == -0.014752
    
     -- 5. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#158
     kcv$R2_oos not equal to kcvbig$R2_oos.
     2/4 mismatches (average diff: 0.00807)
     [2] 0.690 - 0.681 == 0.00893
     [4] 0.652 - 0.659 == -0.00720
    
     -- 6. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#159
     kcv$R2AME_is not equal to kcvbig$R2AME_is.
     2/4 mismatches (average diff: 0.000358)
     [2] 0.731 - 0.731 == -6.97e-05
     [4] 0.750 - 0.750 == -6.47e-04
    
     -- 7. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#160
     kcv$MSE_is not equal to kcvbig$MSE_is.
     2/4 mismatches (average diff: 0.0249)
     [2] 0.746 - 0.743 == 0.00287
     [4] 0.733 - 0.686 == 0.04684
    
     -- 8. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#166
     kcv$MSE_oos not equal to kcvbig$MSE_oos.
     2/4 mismatches (average diff: 0.0209)
     [2] 0.849 - 0.873 == -0.0238
     [4] 1.244 - 1.226 == 0.0180
    
     -- 9. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#167
     kcv$MSE_AME_is not equal to kcvbig$MSE_AME_is.
     2/4 mismatches (average diff: 0.297)
     [2] 2.00 - 1.98 == 0.0115
     [4] 2.79 - 2.21 == 0.5824
    
     -- 10. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#16
     kcv$R2AME_oos not equal to kcvbig$R2AME_oos.
     2/4 mismatches (average diff: 0.00162)
     [2] 0.769 - 0.769 == 0.00024
     [4] 0.685 - 0.682 == 0.00300
    
     -- 11. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#16
     kcv$MSE_AME_oos not equal to kcvbig$MSE_AME_oos.
     2/4 mismatches (average diff: 0.304)
     [2] 1.61 - 1.68 == -0.0707
     [4] 2.47 - 1.94 == 0.5368
    
     == testthat results ===========================================================
     OK: 12 SKIPPED: 0 FAILED: 11
     1. Failure: crossvalidation function works (@test_basic_usage.R#133)
     2. Failure: Kfolds crossvalidation works (@test_basic_usage.R#148)
     3. Failure: Kfolds crossvalidation works (@test_basic_usage.R#150)
     4. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#157)
     5. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#158)
     6. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#159)
     7. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#160)
     8. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#166)
     9. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#167)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-windows-ix86+x86_64

Version: 3.0.5
Check: tests
Result: ERROR
     Running ‘testthat.R’ [18s/19s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(bigKRLS)
     Loading required package: bigmemory
    
    
     bigKRLS is authored by Pete Mohanty (Stanford University) and Robert Shaffer (University of Pennsylvania) under License GPL 3.
    
     To get started, check out vignette("bigKRLS_basics").
    
     If you use this package in your research, kindly cite:
    
    
     Pete Mohanty and Robert Shaffer. 2018. 'Messy Data, Robust Inference? Navigating Obstacles to Inference with bigKRLS.' Political Analysis. Cambridge University Press. DOI=10.1017/pan.2018.33. pages 1-18.
     >
     > # have to disable R_TESTS environment variable or parallel::makePSOCKcluster hangs.
     > Sys.setenv("R_TESTS" = "")
     >
     >
     > test_check("bigKRLS")
     ..........................
     All done. You may wish to use summary() for more detail, predict() for out-of-sample forecasts, or shiny.bigKRLS() to interact with results. For an alternative approach, see help(crossvalidate.bigKRLS). Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     MODEL SUMMARY:
    
     lambda: 0.1306
     N: 32
     N Effective: 15.17858
     R2: 0.9405
     R2AME**: 0.8477
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     cyl -0.0733 0.0765 -0.9581 0.3806
     disp -0.0037 0.0022 -1.6485 0.1582
     hp -0.0145 0.0040 -3.5903 0.0148
     drat 1.2786 0.6303 2.0287 0.0963
     wt -1.4940 0.3115 -4.7968 0.0045
     qsec 0.3678 0.1741 2.1123 0.0864
     vs* 1.2772 0.6234 2.0488 0.0938
     am* 0.9967 0.5954 1.6739 0.1530
     gear 1.0847 0.2867 3.7828 0.0120
     carb -0.5951 0.1518 -3.9200 0.0104
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     cyl -0.3219 -0.1071 -0.0182 0.0065 0.0406
     disp -0.0108 -0.0080 -0.0040 -0.0011 0.0044
     hp -0.0372 -0.0258 -0.0138 -0.0009 0.0038
     drat -0.4892 0.6973 1.1559 2.1076 3.4894
     wt -3.5756 -1.9904 -1.2308 -0.7641 -0.2853
     qsec -0.1250 0.0504 0.2099 0.5900 1.2423
     vs* -0.3757 0.8754 1.2843 1.8167 2.6408
     am* -0.6929 0.3432 0.8767 1.8618 2.7406
     gear -0.4504 -0.1866 0.8420 1.9432 3.4149
     carb -1.1902 -1.0159 -0.5844 -0.2336 0.0107
    
     (*) Reported average and percentiles of dy/dx is for discrete change of the dummy variable from min to max (usually 0 to 1)).
    
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     Saving model estimates to:
    
     bigKRLS_test_results
    
    
     0 matrices saved as big matrices (base R save() may be used safely in this case too).
     Smaller, base R elements of the outputted object saved: bigKRLS_test_results/estimates.RData
    
     Total file size approximately 0 megabytes.
     .
     model estimates will be saved to:
    
     bigKRLS_test_bigmemory_results
    
     ................
     saving vcovmatyhat to /home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/bigKRLS.Rcheck/tests/testthat
     vcovmatyhat successfully saved to disk (and removed from memory for speed).
     ..........
     saving output to bigKRLS_test_bigmemory_results
     writing bigKRLS_test_bigmemory_results/X.txt ...
     writing bigKRLS_test_bigmemory_results/K.txt ...
     writing bigKRLS_test_bigmemory_results/vcov.est.c.txt ...
     writing bigKRLS_test_bigmemory_results/derivatives.txt ...
    
    
     5 matrices saved as big matrices.
    
     to reload, use syntax like:
    
     load.bigKRLS("/home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/bigKRLS.Rcheck/tests/testthat/bigKRLS_test_bigmemory_results")
     or
     load.bigKRLS("/home/hornik/tmp/R.check/r-patched-gcc/Work/PKGS/bigKRLS.Rcheck/tests/testthat/bigKRLS_test_bigmemory_results", newname="my_estimates")
    
     base R elements of the output saved to estimates.RData.
     Total file size approximately 0 megabytes.
    
    
     All done. You may wish to use summary() for more detail, predict() for out-of-sample forecasts, or shiny.bigKRLS() to interact with results. For an alternative approach, see help(crossvalidate.bigKRLS). Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     Reading from K.txt
     Reading from X.txt
     Reading from derivatives.txt
     Reading from vcov.est.c.txt
     NOTE: vcov.est.fitted not found in .RData or in big matrix file, vcov.est.fitted.txt .
    
     ..........................
     Overview of Model Performance
    
     N: 32
     Seed: 123
    
     In Sample Out of Sample
     Mean Squared Error (Full Model) 3.067 4.512
     Mean Squared Error (Average Marginal Effects Only) 241.673 204.649
     Pseudo-R^2 (Full Model) 0.917 0.850
     Pseudo-R^2 (Average Marginal Effects Only) 0.842 0.888
    
     N 26.000 6.000
    
    
     Summary of Training Model:
    
    
     MODEL SUMMARY:
    
     lambda: 0.2203
     N: 26
     N Effective: 13.31767
     R2: 0.917
     R2AME**: 0.8424
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     cyl -0.0707 0.0728 -0.9707 0.3970
     disp -0.0023 0.0022 -1.0483 0.3648
     hp -0.0150 0.0038 -3.9729 0.0236
     drat 0.8467 0.6580 1.2868 0.2806
     wt -1.3875 0.2990 -4.6396 0.0151
     qsec 0.3421 0.1685 2.0300 0.1265
     vs* 1.4893 0.6266 2.3770 0.0896
     am* 1.2569 0.6508 1.9311 0.1401
     gear 1.0324 0.3206 3.2204 0.0421
     carb -0.5432 0.1383 -3.9272 0.0244
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     cyl -0.3023 -0.0914 -0.0251 -0.0045 0.0251
     disp -0.0101 -0.0065 -0.0036 0.0028 0.0062
     hp -0.0310 -0.0223 -0.0151 -0.0081 0.0005
     drat -0.5234 0.0534 0.5663 1.3290 3.2306
     wt -3.1320 -1.8435 -1.2400 -0.7973 -0.1654
     qsec -0.2059 0.1193 0.3080 0.4881 0.9398
     vs* 0.2663 0.9610 1.4825 2.0927 2.6374
     am* -0.5025 0.7251 1.1666 1.8925 3.0413
     gear -0.4105 -0.1462 1.0044 1.9403 2.9580
     carb -1.1097 -0.6895 -0.5497 -0.3194 -0.0618
    
     (*) Reported average and percentiles of dy/dx is for discrete change of the dummy variable from min to max (usually 0 to 1)).
    
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     ....................................
     .....................
     ....................
     ....................
     ................
     .................
     ................
     ................
     ....................
     .....................
     ....................
     ....................
     Overview of Model Performance
    
     N: 250
     Kfolds: 4
     Seed: 1234
    
     Fold 1 Fold 2 Fold 3 Fold 4
     MSE (In Sample) 0.734 0.743 0.695 0.686
     MSE (Out of Sample) 1.276 0.873 1.195 1.226
     MSE AME (In Sample) 2.695 1.984 2.540 2.209
     MSE AME (Out of Sample) 3.177 1.677 3.045 1.936
     R2 (In Sample) 0.774 0.791 0.787 0.787
     R2 (Out of Sample) 0.648 0.681 0.667 0.659
     R2 AME (In Sample) 0.742 0.731 0.732 0.750
     R2 AME (Out of Sample) 0.708 0.769 0.734 0.682
    
     MSE denotes Mean Squared Error. AME implies calculations done with Average Marginal Effects only.
    
     Summary of Training Model1:
    
    
     MODEL SUMMARY:
    
     lambda: 0.899
     N: 187
     N Effective: 156.3024
     R2: 0.7736
     R2AME**: 0.7417
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.9589 0.1905 5.0346 0
     x2 1.4519 0.1891 7.6767 0
     x3 2.0993 0.1798 11.6737 0
     x4 2.7440 0.1757 15.6168 0
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.6189 -0.0087 1.0668 1.8372 3.3296
     x2 -1.0785 0.6842 1.7075 2.3498 3.0562
     x3 -0.3417 1.2507 2.1789 3.1527 4.4687
     x4 0.7443 1.9765 2.8437 3.5838 4.4058
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model2:
    
    
     MODEL SUMMARY:
    
     lambda: 0.5778
     N: 188
     N Effective: 151.2168
     R2: 0.7908
     R2AME**: 0.7309
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.8270 0.2062 4.0110 1e-04
     x2 1.5459 0.2060 7.5055 0e+00
     x3 2.4301 0.2042 11.9033 0e+00
     x4 3.1351 0.2036 15.3949 0e+00
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.6407 -0.2399 0.9918 1.9215 3.0752
     x2 -0.4728 0.7705 1.4049 2.2533 3.8459
     x3 0.0212 1.5036 2.6631 3.3750 4.4612
     x4 0.4207 2.1119 3.3870 4.2646 5.2462
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model3:
    
    
     MODEL SUMMARY:
    
     lambda: 0.6276
     N: 188
     N Effective: 152.5316
     R2: 0.787
     R2AME**: 0.7324
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.5377 0.2009 2.6770 0.0083
     x2 1.4111 0.2024 6.9715 0.0000
     x3 2.3743 0.1888 12.5778 0.0000
     x4 2.9933 0.1911 15.6618 0.0000
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.8595 -0.2222 0.6688 1.4931 2.4555
     x2 -0.8720 0.5198 1.2902 2.5101 3.4258
     x3 0.0256 1.2932 2.2869 3.3879 4.8906
     x4 0.0714 2.1910 3.0890 4.0984 5.1052
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
    
    
     Summary of Training Model4:
    
    
     MODEL SUMMARY:
    
     lambda: 0.6245
     N: 187
     N Effective: 151.8663
     R2: 0.7868
     R2AME**: 0.7503
    
     Average Marginal Effects:
    
     Estimate Std. Error t value Pr(>|t|)
     x1 0.8540 0.1975 4.3235 0
     x2 1.6994 0.1972 8.6181 0
     x3 2.1450 0.1909 11.2334 0
     x4 3.0553 0.1950 15.6664 0
    
    
     Percentiles of Marginal Effects:
    
     5% 25% 50% 75% 95%
     x1 -1.1126 0.2378 0.9325 1.5662 3.0208
     x2 -0.2084 1.0239 1.7979 2.5718 3.3397
     x3 -0.1887 1.3657 2.3700 2.9835 3.8805
     x4 0.6929 2.1339 3.1462 4.0975 5.0257
    
     (**) Pseudo-R^2 computed using only the Average Marginal Effects.
    
    
     You may also wish to use predict() for out-of-sample forecasts or shiny.bigKRLS() to interact with results. Type vignette("bigKRLS_basics") for sample syntax. Use save.bigKRLS() to store results and load.bigKRLS() to re-open them.
    
     ── 1. Failure: Kfolds crossvalidation works (@test_basic_usage.R#148) ─────────
     kcv$folds not equal to kcvbig$folds.
     2/250 mismatches (average diff: 2)
     [1] 2 - 4 == -2
     [204] 4 - 2 == 2
    
     ── 2. Failure: Kfolds crossvalidation works (@test_basic_usage.R#150) ─────────
     sum(kcv$fold_2$tested$predicted) not equal to sum(kcvbig$fold_2$tested$predicted[]).
     1/1 mismatches
     [1] 296 - 297 == -1.35
    
     ── 3. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#157
     kcv$R2_is not equal to kcvbig$R2_is.
     2/4 mismatches (average diff: 0.00769)
     [2] 0.791 - 0.790 == 0.000627
     [4] 0.787 - 0.772 == 0.014752
    
     ── 4. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#158
     kcv$R2_oos not equal to kcvbig$R2_oos.
     2/4 mismatches (average diff: 0.00807)
     [2] 0.681 - 0.690 == -0.00893
     [4] 0.659 - 0.652 == 0.00720
    
     ── 5. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#159
     kcv$R2AME_is not equal to kcvbig$R2AME_is.
     2/4 mismatches (average diff: 0.000358)
     [2] 0.731 - 0.731 == 6.97e-05
     [4] 0.750 - 0.750 == 6.47e-04
    
     ── 6. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#160
     kcv$MSE_is not equal to kcvbig$MSE_is.
     2/4 mismatches (average diff: 0.0249)
     [2] 0.743 - 0.746 == -0.00287
     [4] 0.686 - 0.733 == -0.04684
    
     ── 7. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#166
     kcv$MSE_oos not equal to kcvbig$MSE_oos.
     2/4 mismatches (average diff: 0.0209)
     [2] 0.873 - 0.849 == 0.0238
     [4] 1.226 - 1.244 == -0.0180
    
     ── 8. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#167
     kcv$MSE_AME_is not equal to kcvbig$MSE_AME_is.
     2/4 mismatches (average diff: 0.297)
     [2] 1.98 - 2.00 == -0.0115
     [4] 2.21 - 2.79 == -0.5824
    
     ── 9. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#168
     kcv$R2AME_oos not equal to kcvbig$R2AME_oos.
     2/4 mismatches (average diff: 0.00162)
     [2] 0.769 - 0.769 == -0.00024
     [4] 0.682 - 0.685 == -0.00300
    
     ── 10. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#16
     kcv$MSE_AME_oos not equal to kcvbig$MSE_AME_oos.
     2/4 mismatches (average diff: 0.304)
     [2] 1.68 - 1.61 == 0.0707
     [4] 1.94 - 2.47 == -0.5368
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 18 SKIPPED: 0 FAILED: 10
     1. Failure: Kfolds crossvalidation works (@test_basic_usage.R#148)
     2. Failure: Kfolds crossvalidation works (@test_basic_usage.R#150)
     3. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#157)
     4. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#158)
     5. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#159)
     6. Failure: Kfolds test stats, big vs base (batch 1) (@test_basic_usage.R#160)
     7. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#166)
     8. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#167)
     9. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#168)
     10. Failure: Kfolds test stats, big vs base (batch 2) (@test_basic_usage.R#169)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-linux-x86_64

Version: 3.0.5
Check: whether package can be installed
Result: WARN
    Found the following significant warnings:
     Warning: S3 methods '[.fun_list', '[.grouped_df', 'all.equal.tbl_df', 'anti_join.data.frame', 'anti_join.tbl_df', 'arrange.data.frame', 'arrange.default', 'arrange.grouped_df', 'arrange.tbl_df', 'arrange_.data.frame', 'arrange_.tbl_df', 'as.data.frame.grouped_df', 'as.data.frame.rowwise_df', 'as.data.frame.tbl_cube', 'as.table.tbl_cube', 'as.tbl.data.frame', 'as.tbl.tbl', 'as.tbl_cube.array', 'as.tbl_cube.data.frame', 'as.tbl_cube.matrix', 'as.tbl_cube.table', 'as_tibble.grouped_df', 'as_tibble.tbl_cube', 'auto_copy.tbl_cube', 'auto_copy.tbl_df', 'cbind.grouped_df', 'collapse.data.frame', 'collect.data.frame', 'common_by.NULL', 'common_by.character', 'common_by.default', 'common_by.list', 'compute.data.frame', 'copy_to.DBIConnection', 'copy_to.src_local', 'default_missing.data.frame', 'default_missing.default', 'dim.tbl_cube', 'distinct.data.frame', 'distinct.default', 'distinct.grouped_df', 'distinct.tbl_df', 'distinct_.data.frame', 'distinct_.grouped_df', 'distinct_.tbl_df', 'do.NULL', 'do.da [... truncated]
Flavor: r-release-windows-ix86+x86_64