Running ‘testthat.R’ [139s/227s]
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")
  ── 1. Error: (unknown) (@test-XGBoost.R#2)  ────────────────────────────────────
  there is no package called 'xgboost'
  1: library(xgboost) at testthat/test-XGBoost.R:2
  
  lasso-penalized linear regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0073):
  -------------------------------------------------
    Nonzero coefficients: 12
    Cross-validation error (deviance): 24.60
    R-squared: 0.71
    Signal-to-noise ratio: 2.43
    Scale estimate (sigma): 4.960
  lasso-penalized logistic regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0017):
  -------------------------------------------------
    Nonzero coefficients: 13
    Cross-validation error (deviance): 0.64
    R-squared: 0.50
    Signal-to-noise ratio: 0.98
    Prediction error: 0.125
  lasso-penalized linear regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0068):
  -------------------------------------------------
    Nonzero coefficients: 12
    Cross-validation error (deviance): 23.94
    R-squared: 0.72
    Signal-to-noise ratio: 2.53
    Scale estimate (sigma): 4.893
  lasso-penalized logistic regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0018):
  -------------------------------------------------
    Nonzero coefficients: 13
    Cross-validation error (deviance): 0.64
    R-squared: 0.50
    Signal-to-noise ratio: 0.99
    Prediction error: 0.123
  
  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     84.42119 0.04698365
  SL.biglasso_All 25.47217 0.95301635
  
  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     0.23918511    0
  SL.biglasso_All 0.09457758    1
  Y
   0  1 
  53 47 
  $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 0.048174    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 0.06237030 0.1395108
  SL.randomForest_2_All 0.05143236 0.8604892
  SL.randomForest_3_All 0.05668573 0.0000000
  $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    0.05304882 0.7805482
  SL.glmnet_0.25_All 0.05230718 0.0000000
  SL.glmnet_0.5_All  0.05365255 0.0000000
  SL.glmnet_0.75_All 0.05875278 0.2194518
  SL.glmnet_1_All    0.06431806 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           0.26154929 0.0000000
  SL.svm_polynomial_All 0.09844471 0.3172948
  SL.svm_radial_All     0.08934360 0.0000000
  SL.svm_sigmoid_All    0.08320888 0.6827052
  
   *** caught segfault ***
  address bffdefa2, 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 ...