Running ‘testthat.R’ [17s/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 Texas at Austin) under License GPL 3.
  
  Check out vignette("bigKRLS_basics") for a brief explanation of the statistics, syntax, and references.
  
  For the newest version, check out github.com/rdrr1990/bigKRLS/
  > 
  > # 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**: 0.0441 
  
  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)                        2.792        19.073
  Mean Squared Error (Average Marginal Effects Only)   499.730       429.962
  Pseudo-R^2 (Full Model)                                0.918         0.745
  Pseudo-R^2 (Average Marginal Effects Only)             0.239         0.870
                                                                            
  N                                                     26.000         6.000
  
  
  Summary of Training Model:
  
  
  MODEL SUMMARY:
  
  lambda: 0.3907 
  N: 26 
  N Effective: 15.27154 
  R2: 0.9176 
  R2AME**: 0.2386 
  
  Average Marginal Effects:
  
       Estimate Std. Error t value Pr(>|t|)
  cyl   -0.2678     0.0508 -5.2732   0.0028
  disp  -0.0026     0.0016 -1.5777   0.1725
  hp    -0.0046     0.0021 -2.2151   0.0748
  drat   0.4127     0.4058  1.0171   0.3535
  wt    -0.8819     0.1836 -4.8036   0.0042
  qsec   0.1016     0.1014  1.0015   0.3603
  vs*    1.4757     0.5046  2.9242   0.0308
  am*    1.8006     0.4292  4.1954   0.0076
  gear   0.2228     0.1636  1.3618   0.2286
  carb  -0.5348     0.1027 -5.2052   0.0030
  
  
  Percentiles of Marginal Effects:
  
            5%     25%     50%     75%    95%
  cyl  -0.8088 -0.4459 -0.1913 -0.0386 0.0185
  disp -0.0077 -0.0049 -0.0022 -0.0001 0.0021
  hp   -0.0129 -0.0074 -0.0044 -0.0017 0.0036
  drat -0.6224 -0.1601  0.3839  0.7648 1.7098
  wt   -1.5324 -1.1679 -1.0411 -0.6096 0.1083
  qsec -0.1978 -0.0276  0.0583  0.2199 0.5650
  vs*   0.6450  0.9360  1.1939  1.7593 3.2486
  am*   0.0906  1.0729  1.6257  2.5612 3.7224
  gear -0.6637 -0.2914  0.3449  0.6459 1.1174
  carb -0.9377 -0.7794 -0.5222 -0.4086 0.0212
  
  (*) 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.
  
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  ....................── 1. Error: (unknown) (@test_basic_usage.R#143)  ──────────────────────────────
  Problem creating filebacked matrix.
  1: crossvalidate.bigKRLS(y, as.big.matrix(X), Kfolds = 4, seed = 1234, Neig = 0.75 * 
         nrow(X), Ncores = 1) at testthat/test_basic_usage.R:143
  2: to.big.matrix(matrix(trained$avgderivatives), p = 1, path = big.meta)
  3: as.big.matrix(matrix(as.numeric(object), ncol = p), backingfile = name, backingpath = path, 
         descriptorfile = paste0(name, ".desc"))
  4: as.big.matrix(matrix(as.numeric(object), ncol = p), backingfile = name, backingpath = path, 
         descriptorfile = paste0(name, ".desc"))
  5: big.matrix(nrow = nrow(x), ncol = ncol(x), type = type, init = NULL, dimnames = dimnames(x), 
         separated = separated, backingfile = backingfile, backingpath = backingpath, 
         descriptorfile = descriptorfile, binarydescriptor = binarydescriptor, shared = shared)
  6: filebacked.big.matrix(nrow = nrow, ncol = ncol, type = type, init = init, dimnames = dimnames, 
         separated = separated, backingfile = backingfile, backingpath = backingpath, 
         descriptorfile = descriptorfile, binarydescriptor = binarydescriptor)
  7: CreateFileBackedBigMatrix(as.character(backingfile), as.character(backingpath), as.double(nrow), 
         as.double(ncol), as.character(colnames), as.character(rownames), as.integer(typeVal), 
         as.double(init), as.logical(separated))
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 20 SKIPPED: 0 FAILED: 1
  1. Error: (unknown) (@test_basic_usage.R#143) 
  
  Error: testthat unit tests failed
  Execution halted