Merge branch 'remove_ellipsis' into clear_test_warnings

R-package/R/xgb.create.features.R

@@ -52,10 +52,10 @@
 #' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
 #' dtest <- with(agaricus.test, xgb.DMatrix(data, label = label, nthread = 2))
 #'
-#' param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic')
+#' param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic', nthread = 1)
 #' nrounds = 4
 #'
-#' bst <- xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
+#' bst <- xgb.train(params = param, data = dtrain, nrounds = nrounds)
 #'
 #' # Model accuracy without new features
 #' accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) /
@@ -67,12 +67,12 @@
 #'
 #' # learning with new features
 #' new.dtrain <- xgb.DMatrix(
-#'   data = new.features.train, label = agaricus.train$label, nthread = 2
+#'   data = new.features.train, label = agaricus.train$label
 #' )
 #' new.dtest <- xgb.DMatrix(
-#'   data = new.features.test, label = agaricus.test$label, nthread = 2
+#'   data = new.features.test, label = agaricus.test$label
 #' )
-#' bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
+#' bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds)
 #'
 #' # Model accuracy with new features
 #' accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /

R-package/R/xgb.train.R

@@ -176,7 +176,7 @@
 #'
 #' # ... or as dedicated 'objective' and 'custom_metric' parameters of xgb.train:
 #' bst <- xgb.train(
-#'   within(param, rm(c("objective", "eval_metric"))),
+#'   within(param, rm("objective", "eval_metric")),
 #'   dtrain, nrounds = 2, evals = evals,
 #'   objective = logregobj, custom_metric = evalerror
 #' )