Update R doc by roxygen2. (#5201)

R-package/DESCRIPTION

@@ -63,5 +63,5 @@ Imports:
     data.table (>= 1.9.6),
     magrittr (>= 1.5),
     stringi (>= 0.5.2)
-RoxygenNote: 6.1.0
+RoxygenNote: 7.0.2
 SystemRequirements: GNU make, C++11

R-package/man/cb.early.stop.Rd

@@ -4,8 +4,12 @@
 \alias{cb.early.stop}
 \title{Callback closure to activate the early stopping.}
 \usage{
-cb.early.stop(stopping_rounds, maximize = FALSE, metric_name = NULL,
+cb.early.stop(
-  verbose = TRUE)
+  stopping_rounds,
+  maximize = FALSE,
+  metric_name = NULL,
+  verbose = TRUE
+)
 }
 \arguments{
 \item{stopping_rounds}{The number of rounds with no improvement in

R-package/man/predict.xgb.Booster.Rd

@@ -5,10 +5,20 @@
 \alias{predict.xgb.Booster.handle}
 \title{Predict method for eXtreme Gradient Boosting model}
 \usage{
-\method{predict}{xgb.Booster}(object, newdata, missing = NA,
+\method{predict}{xgb.Booster}(
-  outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE,
+  object,
-  predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
+  newdata,
-  reshape = FALSE, ...)
+  missing = NA,
+  outputmargin = FALSE,
+  ntreelimit = NULL,
+  predleaf = FALSE,
+  predcontrib = FALSE,
+  approxcontrib = FALSE,
+  predinteraction = FALSE,
+  reshape = FALSE,
+  training = FALSE,
+  ...
+)
 
 \method{predict}{xgb.Booster.handle}(object, ...)
 }

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

@@ -87,6 +87,6 @@ accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /
 
 # Here the accuracy was already good and is now perfect.
 cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
-          accuracy.after, "!\\n"))
+          accuracy.after, "!\n"))
 
 }

R-package/man/xgb.cv.Rd

@@ -4,11 +4,28 @@
 \alias{xgb.cv}
 \title{Cross Validation}
 \usage{
-xgb.cv(params = list(), data, nrounds, nfold, label = NULL, missing = NA,
+xgb.cv(
-  prediction = FALSE, showsd = TRUE, metrics = list(), obj = NULL,
+  params = list(),
-  feval = NULL, stratified = TRUE, folds = NULL, verbose = TRUE,
+  data,
-  print_every_n = 1L, early_stopping_rounds = NULL, maximize = NULL,
+  nrounds,
-  callbacks = list(), ...)
+  nfold,
+  label = NULL,
+  missing = NA,
+  prediction = FALSE,
+  showsd = TRUE,
+  metrics = list(),
+  obj = NULL,
+  feval = NULL,
+  stratified = TRUE,
+  folds = NULL,
+  train_folds = NULL,
+  verbose = TRUE,
+  print_every_n = 1L,
+  early_stopping_rounds = NULL,
+  maximize = NULL,
+  callbacks = list(),
+  ...
+)
 }
 \arguments{
 \item{params}{the list of parameters. Commonly used ones are:
@@ -69,6 +86,9 @@ by the values of outcome labels.}
 (each element must be a vector of test fold's indices). When folds are supplied,
 the \code{nfold} and \code{stratified} parameters are ignored.}
 
+\item{train_folds}{\code{list} list specifying which indicies to use for training. If \code{NULL}
+(the default) all indices not specified in \code{folds} will be used for training.}
+
 \item{verbose}{\code{boolean}, print the statistics during the process}
 
 \item{print_every_n}{Print each n-th iteration evaluation messages when \code{verbose>0}.

R-package/man/xgb.dump.Rd

@@ -4,8 +4,14 @@
 \alias{xgb.dump}
 \title{Dump an xgboost model in text format.}
 \usage{
-xgb.dump(model, fname = NULL, fmap = "", with_stats = FALSE,
+xgb.dump(
-  dump_format = c("text", "json"), ...)
+  model,
+  fname = NULL,
+  fmap = "",
+  with_stats = FALSE,
+  dump_format = c("text", "json"),
+  ...
+)
 }
 \arguments{
 \item{model}{the model object.}

R-package/man/xgb.importance.Rd

@@ -4,8 +4,14 @@
 \alias{xgb.importance}
 \title{Importance of features in a model.}
 \usage{
-xgb.importance(feature_names = NULL, model = NULL, trees = NULL,
+xgb.importance(
-  data = NULL, label = NULL, target = NULL)
+  feature_names = NULL,
+  model = NULL,
+  trees = NULL,
+  data = NULL,
+  label = NULL,
+  target = NULL
+)
 }
 \arguments{
 \item{feature_names}{character vector of feature names. If the model already

R-package/man/xgb.model.dt.tree.Rd

@@ -4,8 +4,14 @@
 \alias{xgb.model.dt.tree}
 \title{Parse a boosted tree model text dump}
 \usage{
-xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL,
+xgb.model.dt.tree(
-  trees = NULL, use_int_id = FALSE, ...)
+  feature_names = NULL,
+  model = NULL,
+  text = NULL,
+  trees = NULL,
+  use_int_id = FALSE,
+  ...
+)
 }
 \arguments{
 \item{feature_names}{character vector of feature names. If the model already

R-package/man/xgb.plot.deepness.Rd

@@ -5,11 +5,17 @@
 \alias{xgb.plot.deepness}
 \title{Plot model trees deepness}
 \usage{
-xgb.ggplot.deepness(model = NULL, which = c("2x1", "max.depth", "med.depth",
+xgb.ggplot.deepness(
-  "med.weight"))
+  model = NULL,
+  which = c("2x1", "max.depth", "med.depth", "med.weight")
+)
 
-xgb.plot.deepness(model = NULL, which = c("2x1", "max.depth", "med.depth",
+xgb.plot.deepness(
-  "med.weight"), plot = TRUE, ...)
+  model = NULL,
+  which = c("2x1", "max.depth", "med.depth", "med.weight"),
+  plot = TRUE,
+  ...
+)
 }
 \arguments{
 \item{model}{either an \code{xgb.Booster} model generated by the \code{xgb.train} function

R-package/man/xgb.plot.importance.Rd

@@ -5,12 +5,25 @@
 \alias{xgb.plot.importance}
 \title{Plot feature importance as a bar graph}
 \usage{
-xgb.ggplot.importance(importance_matrix = NULL, top_n = NULL,
+xgb.ggplot.importance(
-  measure = NULL, rel_to_first = FALSE, n_clusters = c(1:10), ...)
+  importance_matrix = NULL,
+  top_n = NULL,
+  measure = NULL,
+  rel_to_first = FALSE,
+  n_clusters = c(1:10),
+  ...
+)
 
-xgb.plot.importance(importance_matrix = NULL, top_n = NULL,
+xgb.plot.importance(
-  measure = NULL, rel_to_first = FALSE, left_margin = 10, cex = NULL,
+  importance_matrix = NULL,
-  plot = TRUE, ...)
+  top_n = NULL,
+  measure = NULL,
+  rel_to_first = FALSE,
+  left_margin = 10,
+  cex = NULL,
+  plot = TRUE,
+  ...
+)
 }
 \arguments{
 \item{importance_matrix}{a \code{data.table} returned by \code{\link{xgb.importance}}.}

R-package/man/xgb.plot.multi.trees.Rd

@@ -4,8 +4,15 @@
 \alias{xgb.plot.multi.trees}
 \title{Project all trees on one tree and plot it}
 \usage{
-xgb.plot.multi.trees(model, feature_names = NULL, features_keep = 5,
+xgb.plot.multi.trees(
-  plot_width = NULL, plot_height = NULL, render = TRUE, ...)
+  model,
+  feature_names = NULL,
+  features_keep = 5,
+  plot_width = NULL,
+  plot_height = NULL,
+  render = TRUE,
+  ...
+)
 }
 \arguments{
 \item{model}{produced by the \code{xgb.train} function.}

R-package/man/xgb.plot.shap.Rd

@@ -4,13 +4,33 @@
 \alias{xgb.plot.shap}
 \title{SHAP contribution dependency plots}
 \usage{
-xgb.plot.shap(data, shap_contrib = NULL, features = NULL, top_n = 1,
+xgb.plot.shap(
-  model = NULL, trees = NULL, target_class = NULL,
+  data,
-  approxcontrib = FALSE, subsample = NULL, n_col = 1, col = rgb(0, 0, 1,
+  shap_contrib = NULL,
-  0.2), pch = ".", discrete_n_uniq = 5, discrete_jitter = 0.01,
+  features = NULL,
-  ylab = "SHAP", plot_NA = TRUE, col_NA = rgb(0.7, 0, 1, 0.6),
+  top_n = 1,
-  pch_NA = ".", pos_NA = 1.07, plot_loess = TRUE, col_loess = 2,
+  model = NULL,
-  span_loess = 0.5, which = c("1d", "2d"), plot = TRUE, ...)
+  trees = NULL,
+  target_class = NULL,
+  approxcontrib = FALSE,
+  subsample = NULL,
+  n_col = 1,
+  col = rgb(0, 0, 1, 0.2),
+  pch = ".",
+  discrete_n_uniq = 5,
+  discrete_jitter = 0.01,
+  ylab = "SHAP",
+  plot_NA = TRUE,
+  col_NA = rgb(0.7, 0, 1, 0.6),
+  pch_NA = ".",
+  pos_NA = 1.07,
+  plot_loess = TRUE,
+  col_loess = 2,
+  span_loess = 0.5,
+  which = c("1d", "2d"),
+  plot = TRUE,
+  ...
+)
 }
 \arguments{
 \item{data}{data as a \code{matrix} or \code{dgCMatrix}.}

R-package/man/xgb.plot.tree.Rd

@@ -4,9 +4,16 @@
 \alias{xgb.plot.tree}
 \title{Plot a boosted tree model}
 \usage{
-xgb.plot.tree(feature_names = NULL, model = NULL, trees = NULL,
+xgb.plot.tree(
-  plot_width = NULL, plot_height = NULL, render = TRUE,
+  feature_names = NULL,
-  show_node_id = FALSE, ...)
+  model = NULL,
+  trees = NULL,
+  plot_width = NULL,
+  plot_height = NULL,
+  render = TRUE,
+  show_node_id = FALSE,
+  ...
+)
 }
 \arguments{
 \item{feature_names}{names of each feature as a \code{character} vector.}

R-package/man/xgb.train.Rd

@@ -5,15 +5,41 @@
 \alias{xgboost}
 \title{eXtreme Gradient Boosting Training}
 \usage{
-xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
+xgb.train(
-  feval = NULL, verbose = 1, print_every_n = 1L,
+  params = list(),
-  early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
+  data,
-  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), ...)
+  nrounds,
+  watchlist = list(),
+  obj = NULL,
+  feval = NULL,
+  verbose = 1,
+  print_every_n = 1L,
+  early_stopping_rounds = NULL,
+  maximize = NULL,
+  save_period = NULL,
+  save_name = "xgboost.model",
+  xgb_model = NULL,
+  callbacks = list(),
+  ...
+)
 
-xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
+xgboost(
-  params = list(), nrounds, verbose = 1, print_every_n = 1L,
+  data = NULL,
-  early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
+  label = NULL,
-  save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), ...)
+  missing = NA,
+  weight = NULL,
+  params = list(),
+  nrounds,
+  verbose = 1,
+  print_every_n = 1L,
+  early_stopping_rounds = NULL,
+  maximize = NULL,
+  save_period = NULL,
+  save_name = "xgboost.model",
+  xgb_model = NULL,
+  callbacks = list(),
+  ...
+)
 }
 \arguments{
 \item{params}{the list of parameters.