CRAN Package Check Results for Package caretEnsemble

Last updated on 2019-12-15 05:47:34 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 2.0.0 14.48 337.15 351.63 ERROR
r-devel-linux-x86_64-debian-gcc 2.0.1 12.09 308.43 320.52 OK
r-devel-linux-x86_64-fedora-clang 2.0.1 513.89 OK
r-devel-linux-x86_64-fedora-gcc 2.0.1 430.26 OK
r-devel-windows-ix86+x86_64 2.0.1 41.00 448.00 489.00 OK
r-devel-windows-ix86+x86_64-gcc8 2.0.0 32.00 454.00 486.00 OK
r-patched-linux-x86_64 2.0.1 13.35 344.39 357.74 OK
r-patched-solaris-x86 2.0.1 560.10 OK
r-release-linux-x86_64 2.0.1 12.46 342.46 354.92 OK
r-release-windows-ix86+x86_64 2.0.0 24.00 336.00 360.00 OK
r-release-osx-x86_64 2.0.1 OK
r-oldrel-windows-ix86+x86_64 2.0.1 15.00 311.00 326.00 OK
r-oldrel-osx-x86_64 2.0.1 OK

Check Details

Version: 2.0.0
Check: tests
Result: ERROR
     Running 'testthat.R' [190s/180s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > library(testthat)
     > library(caretEnsemble)
     >
     > test_check("caretEnsemble")
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(models)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(models)
     where 2: predict(models)
     where 3: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 4: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 5: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 6: quasi_capture(enquo(object), label, capture_warnings)
     where 7 at testthat/test-caretList.R#109: expect_warning(p1 <- predict(models))
     where 8: eval(code, test_env)
     where 9: eval(code, test_env)
     where 10: 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)
     where 11: doTryCatch(return(expr), name, parentenv, handler)
     where 12: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 13: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 14: doTryCatch(return(expr), name, parentenv, handler)
     where 15: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 16: tryCatchList(expr, classes, parentenv, handlers)
     where 17: 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) {
     })
     where 18: test_code(desc, code, env = parent.frame())
     where 19 at testthat/test-caretList.R#94: test_that("caretList predictions", {
     skip_if_not_installed("randomForest")
     skip_if_not_installed("nnet")
     skip_if_not_installed("plyr")
     expect_warning({
     models <- caretList(iris[, 1:2], iris[, 5], tuneLength = 1,
     verbose = FALSE, methodList = "rf", tuneList = list(nnet = caretModelSpec(method = "nnet",
     trace = FALSE)), trControl = trainControl(method = "cv",
     number = 2, savePredictions = "final", classProbs = FALSE))
     })
     expect_warning(p1 <- predict(models))
     p2 <- predict(models, newdata = iris[100, c(1:2)])
     p3 <- predict(models, newdata = iris[110, c(1:2)])
     expect_is(p1, "matrix")
     expect_is(p1[, 1], "character")
     expect_is(p1[, 2], "character")
     expect_equal(names(models), colnames(p1))
     expect_is(p2, "matrix")
     expect_is(p2[, 1], "character")
     expect_is(p2[, 2], "character")
     expect_equal(names(models), colnames(p2))
     expect_is(p3, "matrix")
     expect_is(p3[, 1], "character")
     expect_is(p3[, 2], "character")
     expect_equal(names(models), colnames(p3))
     expect_warning({
     models <- caretList(iris[, 1:2], iris[, 5], tuneLength = 1,
     verbose = FALSE, methodList = "rf", tuneList = list(nnet = caretModelSpec(method = "nnet",
     trace = FALSE)), trControl = trainControl(method = "cv",
     number = 2, savePredictions = "final", classProbs = TRUE))
     })
     expect_warning(p2 <- predict(models))
     p3 <- predict(models, newdata = iris[100, c(1:2)])
     expect_is(p2, "matrix")
     expect_is(p2[, 1], "numeric")
     expect_is(p2[, 2], "numeric")
     expect_is(p3, "matrix")
     expect_is(p3[, 1], "numeric")
     expect_is(p3[, 2], "numeric")
     expect_equal(names(models), colnames(p3))
     models[[1]]$modelType <- "Bogus"
     expect_error(expect_warning(predict(models)))
     })
     where 20: eval(code, test_env)
     where 21: eval(code, test_env)
     where 22: withCallingHandlers({
     eval(code, test_env)
     if (!handled && !is.null(test)) {
     skip_empty()
     }
     }, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
     message = handle_message, error = handle_error)
     where 23: doTryCatch(return(expr), name, parentenv, handler)
     where 24: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 25: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 26: doTryCatch(return(expr), name, parentenv, handler)
     where 27: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 28: tryCatchList(expr, classes, parentenv, handlers)
     where 29: tryCatch(withCallingHandlers({
     eval(code, test_env)
     if (!handled && !is.null(test)) {
     skip_empty()
     }
     }, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
     message = handle_message, error = handle_error), error = handle_fatal,
     skip = function(e) {
     })
     where 30: test_code(NULL, exprs, env)
     where 31: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 32: force(code)
     where 33: doWithOneRestart(return(expr), restart)
     where 34: withOneRestart(expr, restarts[[1L]])
     where 35: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 36: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 37: FUN(X[[i]], ...)
     where 38: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 39: force(code)
     where 40: doWithOneRestart(return(expr), restart)
     where 41: withOneRestart(expr, restarts[[1L]])
     where 42: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 43: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 44: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 45: 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)
     where 46: 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)
     where 47: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 1. Error: caretList predictions (@test-caretList.R#109) --------------------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(p1 <- predict(models))
     7. caretEnsemble:::predict.caretList(models)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(object$models, newdata = newdata)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(object$models, newdata = newdata)
     where 2: predict(object$models, newdata = newdata)
     where 3: predict.caretStack(ens.reg, newdata = X.reg)
     where 4: predict(ens.reg, newdata = X.reg)
     where 5: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 6: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 7: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 8: quasi_capture(enquo(object), label, capture_warnings)
     where 9 at testthat/test-caretStack.R#23: expect_warning(pred.reg <- predict(ens.reg, newdata = X.reg))
     where 10: eval(code, test_env)
     where 11: eval(code, test_env)
     where 12: 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)
     where 13: doTryCatch(return(expr), name, parentenv, handler)
     where 14: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 15: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 16: doTryCatch(return(expr), name, parentenv, handler)
     where 17: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 18: tryCatchList(expr, classes, parentenv, handlers)
     where 19: 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) {
     })
     where 20: test_code(desc, code, env = parent.frame())
     where 21 at testthat/test-caretStack.R#14: test_that("We can stack regression models", {
     set.seed(96367)
     ens.reg <- caretStack(models.reg, method = "lm", preProcess = "pca",
     trControl = trainControl(number = 2, allowParallel = FALSE))
     expect_that(ens.reg, is_a("caretStack"))
     expect_is(summary(ens.reg), "summary.lm")
     sink <- capture.output(print(ens.reg))
     expect_warning(pred.reg <- predict(ens.reg, newdata = X.reg))
     expect_true(is.numeric(pred.reg))
     expect_true(length(pred.reg) == 150)
     })
     where 22: eval(code, test_env)
     where 23: eval(code, test_env)
     where 24: 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)
     where 25: doTryCatch(return(expr), name, parentenv, handler)
     where 26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 27: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 28: doTryCatch(return(expr), name, parentenv, handler)
     where 29: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 30: tryCatchList(expr, classes, parentenv, handlers)
     where 31: 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) {
     })
     where 32: test_code(NULL, exprs, env)
     where 33: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 34: force(code)
     where 35: doWithOneRestart(return(expr), restart)
     where 36: withOneRestart(expr, restarts[[1L]])
     where 37: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 38: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 39: FUN(X[[i]], ...)
     where 40: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 41: force(code)
     where 42: doWithOneRestart(return(expr), restart)
     where 43: withOneRestart(expr, restarts[[1L]])
     where 44: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 45: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 46: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 47: 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)
     where 48: 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)
     where 49: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 2. Error: We can stack regression models (@test-caretStack.R#23) -----------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(pred.reg <- predict(ens.reg, newdata = X.reg))
     7. caretEnsemble:::predict.caretStack(ens.reg, newdata = X.reg)
     9. caretEnsemble:::predict.caretList(object$models, newdata = newdata)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(object$models, newdata = newdata)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(object$models, newdata = newdata)
     where 2: predict(object$models, newdata = newdata)
     where 3: predict.caretStack(ens.class, X.class, type = "prob")
     where 4: predict(ens.class, X.class, type = "prob")
     where 5: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 6: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 7: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 8: quasi_capture(enquo(object), label, capture_warnings)
     where 9 at testthat/test-caretStack.R#36: expect_warning(pred.class <- predict(ens.class, X.class, type = "prob"))
     where 10: eval(code, test_env)
     where 11: eval(code, test_env)
     where 12: 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)
     where 13: doTryCatch(return(expr), name, parentenv, handler)
     where 14: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 15: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 16: doTryCatch(return(expr), name, parentenv, handler)
     where 17: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 18: tryCatchList(expr, classes, parentenv, handlers)
     where 19: 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) {
     })
     where 20: test_code(desc, code, env = parent.frame())
     where 21 at testthat/test-caretStack.R#28: test_that("We can stack classification models", {
     set.seed(42)
     ens.class <- caretStack(models.class, method = "glm", trControl = trainControl(number = 2,
     allowParallel = FALSE))
     expect_that(ens.class, is_a("caretStack"))
     expect_is(summary(ens.class), "summary.glm")
     sink <- capture.output(print(ens.class))
     expect_warning(pred.class <- predict(ens.class, X.class,
     type = "prob"))
     expect_true(is.numeric(pred.class))
     expect_true(length(pred.class) == 150)
     expect_warning(raw.class <- predict(ens.class, X.class, type = "raw"))
     expect_true(is.factor(raw.class))
     expect_true(length(raw.class) == 150)
     })
     where 22: eval(code, test_env)
     where 23: eval(code, test_env)
     where 24: 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)
     where 25: doTryCatch(return(expr), name, parentenv, handler)
     where 26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 27: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 28: doTryCatch(return(expr), name, parentenv, handler)
     where 29: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 30: tryCatchList(expr, classes, parentenv, handlers)
     where 31: 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) {
     })
     where 32: test_code(NULL, exprs, env)
     where 33: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 34: force(code)
     where 35: doWithOneRestart(return(expr), restart)
     where 36: withOneRestart(expr, restarts[[1L]])
     where 37: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 38: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 39: FUN(X[[i]], ...)
     where 40: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 41: force(code)
     where 42: doWithOneRestart(return(expr), restart)
     where 43: withOneRestart(expr, restarts[[1L]])
     where 44: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 45: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 46: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 47: 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)
     where 48: 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)
     where 49: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 3. Error: We can stack classification models (@test-caretStack.R#36) -------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(...)
     7. caretEnsemble:::predict.caretStack(ens.class, X.class, type = "prob")
     9. caretEnsemble:::predict.caretList(object$models, newdata = newdata)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(object$models, newdata = newdata)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(object$models, newdata = newdata)
     where 2: predict(object$models, newdata = newdata)
     where 3: predict.caretStack(ens.class, X.class, type = "raw", se = TRUE)
     where 4: predict(ens.class, X.class, type = "raw", se = TRUE)
     where 5: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 6: withCallingHandlers(code, message = function(condition) {
     out$push(condition)
     maybe_restart("muffleMessage")
     })
     where 7: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 8: quasi_capture(enquo(object), label, capture_messages)
     where 9 at testthat/test-caretStack.R#64: expect_message(predict(ens.class, X.class, type = "raw", se = TRUE))
     where 10: eval(code, test_env)
     where 11: eval(code, test_env)
     where 12: 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)
     where 13: doTryCatch(return(expr), name, parentenv, handler)
     where 14: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 15: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 16: doTryCatch(return(expr), name, parentenv, handler)
     where 17: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 18: tryCatchList(expr, classes, parentenv, handlers)
     where 19: 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) {
     })
     where 20: test_code(desc, code, env = parent.frame())
     where 21 at testthat/test-caretStack.R#59: test_that("Failure to calculate se occurs gracefully", {
     ens.class <- caretStack(models.class, method = "glm", trControl = trainControl(number = 2,
     allowParallel = FALSE))
     expect_message(predict(ens.class, X.class, type = "raw",
     se = TRUE))
     expect_warning(expect_is(predict(ens.class, X.class, type = "raw"),
     "factor"))
     expect_warning(expect_is(predict(ens.class, X.class, type = "raw",
     se = TRUE), "factor"))
     expect_warning({
     expect_identical(predict(ens.class, X.class, type = "raw",
     se = TRUE), predict(ens.class, X.class, type = "raw"))
     })
     ens.reg <- caretStack(models.reg, method = "lm", preProcess = "pca",
     trControl = trainControl(number = 2, allowParallel = FALSE))
     expect_warning(pred <- predict(ens.reg, X.reg, se = TRUE))
     expect_warning(expect_is(predict(ens.reg, X.reg, se = TRUE),
     "data.frame"))
     expect_warning(expect_is(predict(ens.class, X.class, type = "prob",
     se = TRUE), "data.frame"))
     expect_warning(expect_is(predict(ens.class, X.class, type = "prob",
     se = TRUE, return_weights = TRUE), "data.frame"))
     expect_warning(expect_identical(colnames(predict(ens.class,
     X.class, type = "prob", se = TRUE)), c("fit", "lwr",
     "upr")))
     expect_warning(expect_false(identical(predict(ens.class,
     X.class, type = "raw", return_weights = TRUE), predict(ens.class,
     X.class, type = "raw", return_weights = FALSE))))
     expect_warning(expect_false(identical(predict(ens.class,
     X.class, type = "prob", se = TRUE, level = 0.8), predict(ens.class,
     X.class, type = "prob", se = TRUE, return_weights = FALSE))))
     expect_warning(expect_true(identical(predict(ens.class, X.class,
     type = "prob", level = 0.8), predict(ens.class, X.class,
     type = "prob", return_weights = FALSE))))
     })
     where 22: eval(code, test_env)
     where 23: eval(code, test_env)
     where 24: 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)
     where 25: doTryCatch(return(expr), name, parentenv, handler)
     where 26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 27: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 28: doTryCatch(return(expr), name, parentenv, handler)
     where 29: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 30: tryCatchList(expr, classes, parentenv, handlers)
     where 31: 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) {
     })
     where 32: test_code(NULL, exprs, env)
     where 33: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 34: force(code)
     where 35: doWithOneRestart(return(expr), restart)
     where 36: withOneRestart(expr, restarts[[1L]])
     where 37: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 38: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 39: FUN(X[[i]], ...)
     where 40: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 41: force(code)
     where 42: doWithOneRestart(return(expr), restart)
     where 43: withOneRestart(expr, restarts[[1L]])
     where 44: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 45: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 46: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 47: 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)
     where 48: 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)
     where 49: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 4. Error: Failure to calculate se occurs gracefully (@test-caretStack.R#64)
     the condition has length > 1
     Backtrace:
     1. testthat::expect_message(...)
     7. caretEnsemble:::predict.caretStack(...)
     9. caretEnsemble:::predict.caretList(object$models, newdata = newdata)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(object$models, newdata = newdata)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(object$models, newdata = newdata)
     where 2: predict(object$models, newdata = newdata)
     where 3: predict.caretStack(object, type = "prob")
     where 4: predict(object, type = "prob")
     where 5: residuals.caretEnsemble(ens)
     where 6: residuals(ens)
     where 7: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 8: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 9: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 10: quasi_capture(enquo(object), label, capture_warnings)
     where 11 at testthat/test-ensemble.R#43: expect_warning(r <- residuals(ens))
     where 12: eval(code, test_env)
     where 13: eval(code, test_env)
     where 14: 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)
     where 15: doTryCatch(return(expr), name, parentenv, handler)
     where 16: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 17: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 18: doTryCatch(return(expr), name, parentenv, handler)
     where 19: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 20: tryCatchList(expr, classes, parentenv, handlers)
     where 21: 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) {
     })
     where 22: test_code(desc, code, env = parent.frame())
     where 23 at testthat/test-ensemble.R#41: test_that("We can extract resdiuals from caretEnsemble objects",
     {
     ens <- caretEnsemble(models.class)
     expect_warning(r <- residuals(ens))
     expect_is(r, "numeric")
     expect_equal(length(r), 150)
     ens <- caretEnsemble(models.reg)
     expect_warning(r <- residuals(ens))
     expect_is(r, "numeric")
     expect_equal(length(r), 150)
     })
     where 24: eval(code, test_env)
     where 25: eval(code, test_env)
     where 26: 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)
     where 27: doTryCatch(return(expr), name, parentenv, handler)
     where 28: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 29: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 30: doTryCatch(return(expr), name, parentenv, handler)
     where 31: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 32: tryCatchList(expr, classes, parentenv, handlers)
     where 33: 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) {
     })
     where 34: test_code(NULL, exprs, env)
     where 35: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 36: force(code)
     where 37: doWithOneRestart(return(expr), restart)
     where 38: withOneRestart(expr, restarts[[1L]])
     where 39: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 40: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 41: FUN(X[[i]], ...)
     where 42: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 43: force(code)
     where 44: doWithOneRestart(return(expr), restart)
     where 45: withOneRestart(expr, restarts[[1L]])
     where 46: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 47: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 48: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 49: 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)
     where 50: 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)
     where 51: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 5. Error: We can extract resdiuals from caretEnsemble objects (@test-ensemble
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(r <- residuals(ens))
     7. caretEnsemble:::residuals.caretEnsemble(ens)
     9. caretEnsemble:::predict.caretStack(object, type = "prob")
     11. caretEnsemble:::predict.caretList(object$models, newdata = newdata)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(object$models, newdata = newdata)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(object$models, newdata = newdata)
     where 2: predict(object$models, newdata = newdata)
     where 3: predict.caretStack(ens.reg)
     where 4: predict(ens.reg)
     where 5: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 6: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 7: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 8: quasi_capture(enquo(object), label, capture_warnings)
     where 9 at testthat/test-ensemble.R#79: expect_warning(pred.reg <- predict(ens.reg))
     where 10: eval(code, test_env)
     where 11: eval(code, test_env)
     where 12: withCallingHandlers({
     eval(code, test_env)
     if (!handled && !is.null(test)) {
     skip_empty()
     }
     }, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
     message = handle_message, error = handle_error)
     where 13: doTryCatch(return(expr), name, parentenv, handler)
     where 14: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 15: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 16: doTryCatch(return(expr), name, parentenv, handler)
     where 17: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 18: tryCatchList(expr, classes, parentenv, handlers)
     where 19: tryCatch(withCallingHandlers({
     eval(code, test_env)
     if (!handled && !is.null(test)) {
     skip_empty()
     }
     }, expectation = handle_expectation, skip = handle_skip, warning = handle_warning,
     message = handle_message, error = handle_error), error = handle_fatal,
     skip = function(e) {
     })
     where 20: test_code(desc, code, env = parent.frame())
     where 21 at testthat/test-ensemble.R#76: test_that("We can ensemble regression models", {
     ens.reg <- caretEnsemble(models.reg, trControl = trainControl(number = 2))
     expect_that(ens.reg, is_a("caretEnsemble"))
     expect_warning(pred.reg <- predict(ens.reg))
     expect_warning(pred.reg2 <- predict(ens.reg, se = TRUE))
     expect_true(all(pred.reg == pred.reg2$fit))
     expect_warning(expect_error(predict(ens.reg, return_weights = "BOGUS")))
     expect_true(is.numeric(pred.reg))
     expect_true(length(pred.reg) == 150)
     ens.class <- caretEnsemble(models.class, trControl = trainControl(number = 2))
     expect_that(ens.class, is_a("caretEnsemble"))
     expect_warning(pred.class <- predict(ens.class, type = "prob"))
     expect_true(is.numeric(pred.class))
     expect_true(length(pred.class) == 150)
     expect_warning(p1 <- predict(ens.reg, return_weights = TRUE,
     se = FALSE))
     expect_is(attr(p1, which = "weights"), "numeric")
     expect_is(p1, "numeric")
     expect_warning(p2 <- predict(ens.reg, return_weights = TRUE,
     se = TRUE))
     expect_is(attr(p2, which = "weights"), "numeric")
     expect_is(p2, "data.frame")
     expect_equal(ncol(p2), 3)
     expect_identical(names(p2), c("fit", "lwr", "upr"))
     expect_warning(p3 <- predict(ens.reg, return_weights = FALSE,
     se = FALSE))
     expect_is(p3, "numeric")
     expect_true(all(p1 == p3))
     expect_false(identical(p1, p3))
     expect_true(all(p2$fit == p1))
     expect_true(all(p2$fit == p3))
     expect_null(attr(p3, which = "weights"))
     })
     where 22: eval(code, test_env)
     where 23: eval(code, test_env)
     where 24: 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)
     where 25: doTryCatch(return(expr), name, parentenv, handler)
     where 26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 27: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 28: doTryCatch(return(expr), name, parentenv, handler)
     where 29: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 30: tryCatchList(expr, classes, parentenv, handlers)
     where 31: 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) {
     })
     where 32: test_code(NULL, exprs, env)
     where 33: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 34: force(code)
     where 35: doWithOneRestart(return(expr), restart)
     where 36: withOneRestart(expr, restarts[[1L]])
     where 37: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 38: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 39: FUN(X[[i]], ...)
     where 40: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 41: force(code)
     where 42: doWithOneRestart(return(expr), restart)
     where 43: withOneRestart(expr, restarts[[1L]])
     where 44: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 45: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 46: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 47: 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)
     where 48: 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)
     where 49: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 6. Error: We can ensemble regression models (@test-ensemble.R#79) ----------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(pred.reg <- predict(ens.reg))
     7. caretEnsemble:::predict.caretStack(ens.reg)
     9. caretEnsemble:::predict.caretList(object$models, newdata = newdata)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(object$models, newdata = newdata)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(object$models, newdata = newdata)
     where 2: predict(object$models, newdata = newdata)
     where 3: predict.caretStack(ens.reg)
     where 4: predict(ens.reg)
     where 5: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 6: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 7: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 8: quasi_capture(enquo(object), label, capture_warnings)
     where 9 at testthat/test-ensemble.R#160: expect_warning(pred.reg <- predict(ens.reg))
     where 10: eval(code, test_env)
     where 11: eval(code, test_env)
     where 12: 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)
     where 13: doTryCatch(return(expr), name, parentenv, handler)
     where 14: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 15: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 16: doTryCatch(return(expr), name, parentenv, handler)
     where 17: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 18: tryCatchList(expr, classes, parentenv, handlers)
     where 19: 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) {
     })
     where 20: test_code(desc, code, env = parent.frame())
     where 21 at testthat/test-ensemble.R#156: test_that("It works for regression models", {
     set.seed(1234)
     ens.reg <- caretEnsemble(models.reg, trControl = trainControl(number = 2))
     expect_is(ens.reg, "caretEnsemble")
     expect_warning(pred.reg <- predict(ens.reg))
     newPreds1 <- as.data.frame(X.reg)
     expect_warning(pred.regb <- predict(ens.reg, newdata = newPreds1))
     expect_warning(pred.regc <- predict(ens.reg, newdata = newPreds1[2,
     ]))
     expect_identical(pred.reg, pred.regb)
     expect_less_than(abs(4.740135 - pred.regc), 0.01)
     expect_is(pred.reg, "numeric")
     expect_is(pred.regb, "numeric")
     expect_is(pred.regc, "numeric")
     expect_equal(length(pred.regc), 1)
     })
     where 22: eval(code, test_env)
     where 23: eval(code, test_env)
     where 24: 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)
     where 25: doTryCatch(return(expr), name, parentenv, handler)
     where 26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 27: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 28: doTryCatch(return(expr), name, parentenv, handler)
     where 29: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 30: tryCatchList(expr, classes, parentenv, handlers)
     where 31: 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) {
     })
     where 32: test_code(NULL, exprs, env)
     where 33: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 34: force(code)
     where 35: doWithOneRestart(return(expr), restart)
     where 36: withOneRestart(expr, restarts[[1L]])
     where 37: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 38: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 39: FUN(X[[i]], ...)
     where 40: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 41: force(code)
     where 42: doWithOneRestart(return(expr), restart)
     where 43: withOneRestart(expr, restarts[[1L]])
     where 44: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 45: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 46: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 47: 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)
     where 48: 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)
     where 49: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 7. Error: It works for regression models (@test-ensemble.R#160) ------------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(pred.reg <- predict(ens.reg))
     7. caretEnsemble:::predict.caretStack(ens.reg)
     9. caretEnsemble:::predict.caretList(object$models, newdata = newdata)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(object$models, newdata = newdata)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(object$models, newdata = newdata)
     where 2: predict(object$models, newdata = newdata)
     where 3: predict.caretStack(ens.class, type = "prob")
     where 4: predict(ens.class, type = "prob")
     where 5: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 6: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 7: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 8: quasi_capture(enquo(object), label, capture_warnings)
     where 9 at testthat/test-ensemble.R#176: expect_warning(pred.class <- predict(ens.class, type = "prob"))
     where 10: eval(code, test_env)
     where 11: eval(code, test_env)
     where 12: 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)
     where 13: doTryCatch(return(expr), name, parentenv, handler)
     where 14: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 15: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 16: doTryCatch(return(expr), name, parentenv, handler)
     where 17: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 18: tryCatchList(expr, classes, parentenv, handlers)
     where 19: 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) {
     })
     where 20: test_code(desc, code, env = parent.frame())
     where 21 at testthat/test-ensemble.R#172: test_that("It works for classification models", {
     set.seed(1234)
     ens.class <- caretEnsemble(models.class, trControl = trainControl(number = 2))
     expect_that(ens.class, is_a("caretEnsemble"))
     expect_warning(pred.class <- predict(ens.class, type = "prob"))
     newPreds1 <- as.data.frame(X.class)
     expect_warning(pred.classb <- predict(ens.class, newdata = newPreds1,
     type = "prob"))
     expect_warning(pred.classc <- predict(ens.class, newdata = newPreds1[2,
     ], type = "prob"))
     expect_true(is.numeric(pred.class))
     expect_true(length(pred.class) == 150)
     expect_identical(pred.class, pred.classb)
     expect_less_than(abs(0.03727609 - pred.classc), 0.01)
     expect_is(pred.class, "numeric")
     expect_is(pred.classb, "numeric")
     expect_is(pred.classc, "numeric")
     expect_equal(length(pred.classc), 1)
     })
     where 22: eval(code, test_env)
     where 23: eval(code, test_env)
     where 24: 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)
     where 25: doTryCatch(return(expr), name, parentenv, handler)
     where 26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 27: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 28: doTryCatch(return(expr), name, parentenv, handler)
     where 29: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 30: tryCatchList(expr, classes, parentenv, handlers)
     where 31: 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) {
     })
     where 32: test_code(NULL, exprs, env)
     where 33: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 34: force(code)
     where 35: doWithOneRestart(return(expr), restart)
     where 36: withOneRestart(expr, restarts[[1L]])
     where 37: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 38: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 39: FUN(X[[i]], ...)
     where 40: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 41: force(code)
     where 42: doWithOneRestart(return(expr), restart)
     where 43: withOneRestart(expr, restarts[[1L]])
     where 44: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 45: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 46: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 47: 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)
     where 48: 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)
     where 49: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 8. Error: It works for classification models (@test-ensemble.R#176) --------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(pred.class <- predict(ens.class, type = "prob"))
     7. caretEnsemble:::predict.caretStack(ens.class, type = "prob")
     9. caretEnsemble:::predict.caretList(object$models, newdata = newdata)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(models.reg, "reg", newdata = X.reg)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(models.reg, "reg", newdata = X.reg)
     where 2: predict(models.reg, "reg", newdata = X.reg)
     where 3: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 4: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 5: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 6: quasi_capture(enquo(object), label, capture_warnings)
     where 7 at testthat/test-helper_functions.R#63: expect_warning(out <- predict(models.reg, "reg", newdata = X.reg))
     where 8: eval(code, test_env)
     where 9: eval(code, test_env)
     where 10: 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)
     where 11: doTryCatch(return(expr), name, parentenv, handler)
     where 12: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 13: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 14: doTryCatch(return(expr), name, parentenv, handler)
     where 15: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 16: tryCatchList(expr, classes, parentenv, handlers)
     where 17: 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) {
     })
     where 18: test_code(desc, code, env = parent.frame())
     where 19 at testthat/test-helper_functions.R#62: test_that("We can predict", {
     expect_warning(out <- predict(models.reg, "reg", newdata = X.reg))
     expect_is(out, "matrix")
     expect_true(all(dim(out) == c(150, 4)))
     expect_true(all(colnames(out) == c("rf", "glm", "rpart",
     "treebag")))
     })
     where 20: eval(code, test_env)
     where 21: eval(code, test_env)
     where 22: 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)
     where 23: doTryCatch(return(expr), name, parentenv, handler)
     where 24: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 25: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 26: doTryCatch(return(expr), name, parentenv, handler)
     where 27: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 28: tryCatchList(expr, classes, parentenv, handlers)
     where 29: 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) {
     })
     where 30: test_code(NULL, exprs, env)
     where 31: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 32: force(code)
     where 33: doWithOneRestart(return(expr), restart)
     where 34: withOneRestart(expr, restarts[[1L]])
     where 35: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 36: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 37: FUN(X[[i]], ...)
     where 38: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 39: force(code)
     where 40: doWithOneRestart(return(expr), restart)
     where 41: withOneRestart(expr, restarts[[1L]])
     where 42: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 43: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 44: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 45: 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)
     where 46: 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)
     where 47: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 9. Error: We can predict (@test-helper_functions.R#63) ---------------------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(out <- predict(models.reg, "reg", newdata = X.reg))
     7. caretEnsemble:::predict.caretList(models.reg, "reg", newdata = X.reg)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(models.class, "Classification", newdata = X.class)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(models.class, "Classification", newdata = X.class)
     where 2: predict(models.class, "Classification", newdata = X.class)
     where 3: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 4: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 5: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 6: quasi_capture(enquo(object), label, capture_warnings)
     where 7 at testthat/test-helper_functions.R#81: expect_warning(out <- predict(models.class, "Classification",
     newdata = X.class))
     where 8: eval(code, test_env)
     where 9: eval(code, test_env)
     where 10: 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)
     where 11: doTryCatch(return(expr), name, parentenv, handler)
     where 12: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 13: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 14: doTryCatch(return(expr), name, parentenv, handler)
     where 15: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 16: tryCatchList(expr, classes, parentenv, handlers)
     where 17: 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) {
     })
     where 18: test_code(desc, code, env = parent.frame())
     where 19 at testthat/test-helper_functions.R#80: test_that("We can predict", {
     expect_warning(out <- predict(models.class, "Classification",
     newdata = X.class))
     expect_is(out, "matrix")
     expect_true(all(dim(out) == c(150, 4)))
     expect_true(all(colnames(out) == c("rf", "glm", "rpart",
     "treebag")))
     expect_warning(out2 <- predict(models.reg, "Regression",
     newdata = X.reg))
     expect_true(all(dim(out2) == c(150, 4)))
     expect_true(all(colnames(out2) == c("rf", "glm", "rpart",
     "treebag")))
     })
     where 20: eval(code, test_env)
     where 21: eval(code, test_env)
     where 22: 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)
     where 23: doTryCatch(return(expr), name, parentenv, handler)
     where 24: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 25: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 26: doTryCatch(return(expr), name, parentenv, handler)
     where 27: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 28: tryCatchList(expr, classes, parentenv, handlers)
     where 29: 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) {
     })
     where 30: test_code(NULL, exprs, env)
     where 31: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 32: force(code)
     where 33: doWithOneRestart(return(expr), restart)
     where 34: withOneRestart(expr, restarts[[1L]])
     where 35: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 36: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 37: FUN(X[[i]], ...)
     where 38: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 39: force(code)
     where 40: doWithOneRestart(return(expr), restart)
     where 41: withOneRestart(expr, restarts[[1L]])
     where 42: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 43: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 44: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 45: 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)
     where 46: 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)
     where 47: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 10. Error: We can predict (@test-helper_functions.R#81) --------------------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(...)
     7. caretEnsemble:::predict.caretList(...)
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
     :
     --- package (from environment) ---
     caretEnsemble
     --- call from context ---
     predict.caretList(models.class, "Classification", newdata = X.class)
     --- call from argument ---
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     --- R stacktrace ---
     where 1: predict.caretList(models.class, "Classification", newdata = X.class)
     where 2: predict(models.class, "Classification", newdata = X.class)
     where 3: eval_bare(expr, quo_get_env(quo))
     where 4: quasi_label(enquo(object), label, arg = "object")
     where 5: expect_is(predict(models.class, "Classification", newdata = X.class),
     "matrix")
     where 6: eval_bare(quo_get_expr(.quo), quo_get_env(.quo))
     where 7: withCallingHandlers(code, warning = function(condition) {
     out$push(condition)
     maybe_restart("muffleWarning")
     })
     where 8: .capture(act$val <- eval_bare(quo_get_expr(.quo), quo_get_env(.quo)),
     ...)
     where 9: quasi_capture(enquo(object), label, capture_warnings)
     where 10: expect_warning({
     expect_is(predict(models.class, "Classification", newdata = X.class),
     "matrix")
     out1 <- predict(models.class, "Classification", newdata = X.class)
     out2 <- predict(models.class, "Classification", verbose = TRUE,
     newdata = X.class)
     expect_identical(out1, out2)
     })
     where 11: eval(expr, pf)
     where 12: eval(expr, pf)
     where 13: withVisible(eval(expr, pf))
     where 14: evalVis(expr)
     where 15 at testthat/test-helper_functions.R#91: capture.output({
     expect_warning({
     expect_is(predict(models.class, "Classification", newdata = X.class),
     "matrix")
     out1 <- predict(models.class, "Classification", newdata = X.class)
     out2 <- predict(models.class, "Classification", verbose = TRUE,
     newdata = X.class)
     expect_identical(out1, out2)
     })
     expect_warning({
     expect_is(predict(models.reg, "Regression", newdata = X.reg),
     "matrix")
     out1 <- predict(models.reg, "Regression", newdata = X.reg)
     out2 <- predict(models.reg, "Regression", verbose = TRUE,
     newdata = X.reg)
     expect_identical(out1, out2)
     })
     })
     where 16: eval(code, test_env)
     where 17: eval(code, test_env)
     where 18: 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)
     where 19: doTryCatch(return(expr), name, parentenv, handler)
     where 20: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 21: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 22: doTryCatch(return(expr), name, parentenv, handler)
     where 23: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 24: tryCatchList(expr, classes, parentenv, handlers)
     where 25: 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) {
     })
     where 26: test_code(desc, code, env = parent.frame())
     where 27 at testthat/test-helper_functions.R#90: test_that("predict results same regardless of verbose option",
     {
     sink <- capture.output({
     expect_warning({
     expect_is(predict(models.class, "Classification",
     newdata = X.class), "matrix")
     out1 <- predict(models.class, "Classification",
     newdata = X.class)
     out2 <- predict(models.class, "Classification",
     verbose = TRUE, newdata = X.class)
     expect_identical(out1, out2)
     })
     expect_warning({
     expect_is(predict(models.reg, "Regression", newdata = X.reg),
     "matrix")
     out1 <- predict(models.reg, "Regression", newdata = X.reg)
     out2 <- predict(models.reg, "Regression", verbose = TRUE,
     newdata = X.reg)
     expect_identical(out1, out2)
     })
     })
     })
     where 28: eval(code, test_env)
     where 29: eval(code, test_env)
     where 30: 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)
     where 31: doTryCatch(return(expr), name, parentenv, handler)
     where 32: tryCatchOne(expr, names, parentenv, handlers[[1L]])
     where 33: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])
     where 34: doTryCatch(return(expr), name, parentenv, handler)
     where 35: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]),
     names[nh], parentenv, handlers[[nh]])
     where 36: tryCatchList(expr, classes, parentenv, handlers)
     where 37: 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) {
     })
     where 38: test_code(NULL, exprs, env)
     where 39: source_file(path, new.env(parent = env), chdir = TRUE, wrap = wrap)
     where 40: force(code)
     where 41: doWithOneRestart(return(expr), restart)
     where 42: withOneRestart(expr, restarts[[1L]])
     where 43: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 44: with_reporter(reporter = reporter, start_end_reporter = start_end_reporter,
     {
     reporter$start_file(basename(path))
     lister$start_file(basename(path))
     source_file(path, new.env(parent = env), chdir = TRUE,
     wrap = wrap)
     reporter$.end_context()
     reporter$end_file()
     })
     where 45: FUN(X[[i]], ...)
     where 46: lapply(paths, test_file, env = env, reporter = current_reporter,
     start_end_reporter = FALSE, load_helpers = FALSE, wrap = wrap)
     where 47: force(code)
     where 48: doWithOneRestart(return(expr), restart)
     where 49: withOneRestart(expr, restarts[[1L]])
     where 50: withRestarts(testthat_abort_reporter = function() NULL, force(code))
     where 51: with_reporter(reporter = current_reporter, results <- lapply(paths,
     test_file, env = env, reporter = current_reporter, start_end_reporter = FALSE,
     load_helpers = FALSE, wrap = wrap))
     where 52: test_files(paths, reporter = reporter, env = env, stop_on_failure = stop_on_failure,
     stop_on_warning = stop_on_warning, wrap = wrap)
     where 53: 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)
     where 54: 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)
     where 55: test_check("caretEnsemble")
    
     --- value of length: 2 type: logical ---
     [1] FALSE TRUE
     --- function from context ---
     function (object, newdata = NULL, ..., verbose = FALSE)
     {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
     }
     <bytecode: 0xcda46f8>
     <environment: namespace:caretEnsemble>
     --- function search by body ---
     Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
     -- 11. Error: predict results same regardless of verbose option ---------------
     the condition has length > 1
     Backtrace:
     1. testthat::expect_warning(...)
     10. caretEnsemble:::predict.caretList(...)
    
     == testthat results ===========================================================
     [ OK: 317 | SKIPPED: 29 | WARNINGS: 3 | FAILED: 11 ]
     1. Error: caretList predictions (@test-caretList.R#109)
     2. Error: We can stack regression models (@test-caretStack.R#23)
     3. Error: We can stack classification models (@test-caretStack.R#36)
     4. Error: Failure to calculate se occurs gracefully (@test-caretStack.R#64)
     5. Error: We can extract resdiuals from caretEnsemble objects (@test-ensemble.R#43)
     6. Error: We can ensemble regression models (@test-ensemble.R#79)
     7. Error: It works for regression models (@test-ensemble.R#160)
     8. Error: It works for classification models (@test-ensemble.R#176)
     9. Error: We can predict (@test-helper_functions.R#63)
     1. ...
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 2.0.0
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building 'caretEnsemble-intro.Rmd' using rmarkdown
    Warning in train.default(x, y, weights = w, ...) :
     The metric "Accuracy" was not in the result set. ROC will be used instead.
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning: glm.fit: algorithm did not converge
    Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
    Warning in train.default(x, y, weights = w, ...) :
     The metric "Accuracy" was not in the result set. ROC will be used instead.
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    caretEnsemble
     --- call from context ---
    predict.caretList(model_list, newdata = head(testing))
     --- call from argument ---
    if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
    }
     --- R stacktrace ---
    where 1: predict.caretList(model_list, newdata = head(testing))
    where 2: predict(model_list, newdata = head(testing))
    where 3: as.data.frame(predict(model_list, newdata = head(testing)))
    where 4: eval(expr, envir, enclos)
    where 5: eval(expr, envir, enclos)
    where 6: withVisible(eval(expr, envir, enclos))
    where 7: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
     error = eHandler, message = mHandler)
    where 8: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
     enclos)), warning = wHandler, error = eHandler, message = mHandler))
    where 9: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
     envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
    where 10: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
     debug = debug, last = i == length(out), use_try = stop_on_error !=
     2L, keep_warning = keep_warning, keep_message = keep_message,
     output_handler = output_handler, include_timing = include_timing)
    where 11: evaluate::evaluate(...)
    where 12: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
     keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
     options$include) 0L else 2L, output_handler = knit_handlers(options$render,
     options))
    where 13: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
     keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
     stop_on_error = if (options$error && options$include) 0L else 2L,
     output_handler = knit_handlers(options$render, options)))
    where 14: block_exec(params)
    where 15: call_block(x)
    where 16: process_group.block(group)
    where 17: process_group(group)
    where 18: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
     error = function(e) {
     setwd(wd)
     cat(res, sep = "\n", file = output %n% "")
     message("Quitting from lines ", paste(current_lines(i),
     collapse = "-"), " (", knit_concord$get("infile"),
     ") ")
     })
    where 19: process_file(text, output)
    where 20: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet,
     encoding = encoding)
    where 21: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
     ...)
    where 22: vweave_rmarkdown(...)
    where 23: engine$weave(file, quiet = quiet, encoding = enc)
    where 24: doTryCatch(return(expr), name, parentenv, handler)
    where 25: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    where 26: tryCatchList(expr, classes, parentenv, handlers)
    where 27: tryCatch({
     engine$weave(file, quiet = quiet, encoding = enc)
     setwd(startdir)
     output <- find_vignette_product(name, by = "weave", engine = engine)
     if (!have.makefile && vignette_is_tex(output)) {
     texi2pdf(file = output, clean = FALSE, quiet = quiet)
     output <- find_vignette_product(name, by = "texi2pdf",
     engine = engine)
     }
     outputs <- c(outputs, output)
    }, error = function(e) {
     thisOK <<- FALSE
     fails <<- c(fails, file)
     message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
     file, conditionMessage(e)))
    })
    where 28: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/caretEnsemble.Rcheck/vign_test/caretEnsemble",
     ser_elibs = "/tmp/RtmpUwDkvO/file3fa23470276a.rds")
    
     --- value of length: 2 type: logical ---
    [1] FALSE TRUE
     --- function from context ---
    function (object, newdata = NULL, ..., verbose = FALSE)
    {
     if (is.null(newdata)) {
     warning("Predicting without new data is not well supported. Attempting to predict on the training data.")
     newdata <- object[[1]]$trainingData
     if (is.null(newdata)) {
     stop("Could not find training data in the first model in the ensemble.")
     }
     }
     if (verbose == TRUE) {
     pboptions(type = "txt", char = "*")
     }
     else if (verbose == FALSE) {
     pboptions(type = "none")
     }
     preds <- pbsapply(object, function(x) {
     type <- x$modelType
     if (type == "Classification") {
     if (x$control$classProbs) {
     caret::predict.train(x, type = "prob", newdata = newdata,
     ...)[, 2]
     }
     else {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     }
     else if (type == "Regression") {
     caret::predict.train(x, type = "raw", newdata = newdata,
     ...)
     }
     else {
     stop(paste("Unknown model type:", type))
     }
     })
     if (class(preds) != "matrix" & class(preds) != "data.frame") {
     if (class(preds) == "character" | class(preds) == "factor") {
     preds <- as.character(preds)
     }
     preds <- as.matrix(t(preds))
     }
     colnames(preds) <- make.names(sapply(object, function(x) x$method),
     unique = TRUE)
     return(preds)
    }
    <bytecode: 0x15381df8>
    <environment: namespace:caretEnsemble>
     --- function search by body ---
    Function predict.caretList in namespace caretEnsemble has this body.
     ----------- END OF FAILURE REPORT --------------
    Quitting from lines 60-62 (caretEnsemble-intro.Rmd)
    Error: processing vignette 'caretEnsemble-intro.Rmd' failed with diagnostics:
    the condition has length > 1
    --- failed re-building 'caretEnsemble-intro.Rmd'
    
    SUMMARY: processing the following file failed:
     'caretEnsemble-intro.Rmd'
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang