Running ‘testthat.R’ [134s/220s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(SuperLearner)
  Loading required package: nnls
  Super Learner
  Version: 2.0-24
  Package created on 2018-08-10
  
  > 
  > test_check("SuperLearner")
  
  Call:  
  SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,  
      xgb_grid$names), cvControl = list(V = 2)) 
  
  
                     Risk      Coef
  SL.mean_All    1.114574 0.6304922
  SL.xgboost_All 1.326036 0.1298382
  SL.xgb.1_All   1.240151 0.0000000
  SL.xgb.2_All   1.140147 0.0000000
  SL.xgb.3_All   1.224808 0.0000000
  SL.xgb.4_All   1.135035 0.2396697
  SL.xgb.5_All   1.458607 0.0000000
  SL.xgb.6_All   1.424464 0.0000000
  SL.xgb.7_All   1.455419 0.0000000
  SL.xgb.8_All   1.420126 0.0000000
  SL.xgb.9_All   1.492676 0.0000000
  SL.xgb.10_All  1.488743 0.0000000
  SL.xgb.11_All  1.492265 0.0000000
  SL.xgb.12_All  1.487902 0.0000000
  lasso-penalized linear regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0068):
  -------------------------------------------------
    Nonzero coefficients: 12
    Cross-validation error (deviance): 23.55
    R-squared: 0.72
    Signal-to-noise ratio: 2.58
    Scale estimate (sigma): 4.853
  lasso-penalized logistic regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0024):
  -------------------------------------------------
    Nonzero coefficients: 12
    Cross-validation error (deviance): 0.65
    R-squared: 0.49
    Signal-to-noise ratio: 0.95
    Prediction error: 0.128
  lasso-penalized linear regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0238):
  -------------------------------------------------
    Nonzero coefficients: 11
    Cross-validation error (deviance): 24.01
    R-squared: 0.72
    Signal-to-noise ratio: 2.52
    Scale estimate (sigma): 4.900
  lasso-penalized logistic regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0026):
  -------------------------------------------------
    Nonzero coefficients: 12
    Cross-validation error (deviance): 0.67
    R-squared: 0.48
    Signal-to-noise ratio: 0.93
    Prediction error: 0.128
  
  Call:  
  SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",  
      "SL.biglasso"), cvControl = list(V = 2)) 
  
  
                      Risk       Coef
  SL.mean_All     84.77675 0.00326959
  SL.biglasso_All 23.67154 0.99673041
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
      "SL.biglasso"), cvControl = list(V = 2)) 
  
  
                       Risk       Coef
  SL.mean_All     0.2382946 0.02063139
  SL.biglasso_All 0.1024175 0.97936861
  Y
   0  1 
  66 34 
  $grid
  NULL
  
  $names
  [1] "SL.randomForest_1"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  $params$ntree
  [1] 100
  
  
  [1] "SL.randomForest_1" "X"                 "Y"                
  [4] "create_rf"         "data"             
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2)) 
  
  
                            Risk Coef
  SL.randomForest_1_All 0.050813    1
  $grid
    mtry
  1    1
  2    4
  3   20
  
  $names
  [1] "SL.randomForest_1" "SL.randomForest_2" "SL.randomForest_3"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  list()
  
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2)) 
  
  
                              Risk      Coef
  SL.randomForest_1_All 0.05805154 0.2008891
  SL.randomForest_2_All 0.04498379 0.6278342
  SL.randomForest_3_All 0.04960304 0.1712766
  $grid
    alpha
  1  0.00
  2  0.25
  3  0.50
  4  0.75
  5  1.00
  
  $names
  [1] "SL.glmnet_0"    "SL.glmnet_0.25" "SL.glmnet_0.5"  "SL.glmnet_0.75"
  [5] "SL.glmnet_1"   
  
  $base_learner
  [1] "SL.glmnet"
  
  $params
  list()
  
  [1] "SL.glmnet_0"    "SL.glmnet_0.25" "SL.glmnet_0.5"  "SL.glmnet_0.75"
  [5] "SL.glmnet_1"   
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = ls(learners),  
      cvControl = list(V = 2), env = learners) 
  
  
                           Risk      Coef
  SL.glmnet_0_All    0.04764882 0.5990378
  SL.glmnet_0.25_All 0.05046096 0.0000000
  SL.glmnet_0.5_All  0.04818493 0.4009622
  SL.glmnet_0.75_All 0.05707002 0.0000000
  SL.glmnet_1_All    0.06232678 0.0000000
  
  Call:  
  SuperLearner(Y = Y, X = X_clean, family = binomial(), SL.library = c("SL.mean",  
      svm$names), cvControl = list(V = 3)) 
  
  
                             Risk      Coef
  SL.mean_All           0.2355795 0.1272078
  SL.svm_polynomial_All 0.1746948 0.0000000
  SL.svm_radial_All     0.1662340 0.1502054
  SL.svm_sigmoid_All    0.1587918 0.7225868
  
   *** caught segfault ***
  address 3fe2c50c, cause 'memory not mapped'
  
  Traceback:
   1: .Call(C_dbarts_run, ptr, as.integer(numBurnIn), as.integer(numSamples))
   2: sampler$run(0L, control@n.burn, FALSE)
   3: dbarts::bart(x.train = X, y.train = Y, x.test = newX, sigest = sigest,     sigdf = sigdf, sigquant = sigquant, k = k, power = power,     base = base, binaryOffset = binaryOffset, weights = obsWeights,     ntree = ntree, ndpost = ndpost, nskip = nskip, printevery = printevery,     keepevery = keepevery, keeptrainfits = keeptrainfits, usequants = usequants,     numcut = numcut, printcutoffs = printcutoffs, nthread = nthread,     keepcall = keepcall, verbose = verbose)
   4: SuperLearner::SL.dbarts(Y_gaus, X, X, family = gaussian(), obsWeights = rep(1,     nrow(X)))
   5: eval(code, test_env)
   6: eval(code, test_env)
   7: withCallingHandlers({    eval(code, test_env)    if (!handled && !is.null(test)) {        skip_empty()    }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,     message = handle_message, error = handle_error)
   8: doTryCatch(return(expr), name, parentenv, handler)
   9: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  10: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
  11: doTryCatch(return(expr), name, parentenv, handler)
  12: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),     names[nh], parentenv, handlers[[nh]])
  13: tryCatchList(expr, classes, parentenv, handlers)
  14: tryCatch(withCallingHandlers({    eval(code, test_env)    if (!handled && !is.null(test)) {        skip_empty()    }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,     message = handle_message, error = handle_error), error = handle_fatal,     skip = function(e) {    })
  15: test_code(NULL, exprs, env)
  16: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
  17: force(code)
  18: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,     {        lister$start_file(basename(path))        source_file(path, new.env(parent = env), chdir = TRUE,             wrap = wrap)        end_context()    })
  19: FUN(X[[i]], ...)
  20: lapply(paths, test_file, env = env, reporter = current_reporter,     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
  21: force(code)
  22: with_reporter(reporter = current_reporter, results <- lapply(paths,     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,     load_helpers = FALSE, wrap = wrap))
  23: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,     stop_on_warning = stop_on_warning, wrap = wrap)
  24: test_dir(path = test_path, reporter = reporter, env = env, filter = filter,     ..., stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning,     wrap = wrap)
  25: test_package_dir(package = package, test_path = test_path, filter = filter,     reporter = reporter, ..., stop_on_failure = stop_on_failure,     stop_on_warning = stop_on_warning, wrap = wrap)
  26: test_check("SuperLearner")
  An irrecoverable exception occurred. R is aborting now ...