From 8a8544c88ca08edabe608994b7772aec9df8c715 Mon Sep 17 00:00:00 2001
From: fis <jm.yuan@outlook.com>
Date: Tue, 14 Jan 2020 13:53:24 +0800
Subject: [PATCH] Update R doc by roxygen2.

---
 R-package/DESCRIPTION                 |  2 +-
 R-package/man/cb.early.stop.Rd        |  8 +++--
 R-package/man/predict.xgb.Booster.Rd  | 18 ++++++++---
 R-package/man/xgb.create.features.Rd  |  2 +-
 R-package/man/xgb.cv.Rd               | 30 +++++++++++++++---
 R-package/man/xgb.dump.Rd             | 10 ++++--
 R-package/man/xgb.importance.Rd       | 10 ++++--
 R-package/man/xgb.model.dt.tree.Rd    | 10 ++++--
 R-package/man/xgb.plot.deepness.Rd    | 14 ++++++---
 R-package/man/xgb.plot.importance.Rd  | 25 +++++++++++----
 R-package/man/xgb.plot.multi.trees.Rd | 11 +++++--
 R-package/man/xgb.plot.shap.Rd        | 34 ++++++++++++++++-----
 R-package/man/xgb.plot.tree.Rd        | 13 ++++++--
 R-package/man/xgb.train.Rd            | 44 +++++++++++++++++++++------
 14 files changed, 181 insertions(+), 50 deletions(-)

diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION
index f43ba573e93b..b99b130e11b0 100644
--- a/R-package/DESCRIPTION
+++ b/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
diff --git a/R-package/man/cb.early.stop.Rd b/R-package/man/cb.early.stop.Rd
index 6fd041b6863d..1a099d7d31fb 100644
--- a/R-package/man/cb.early.stop.Rd
+++ b/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,
-  verbose = TRUE)
+cb.early.stop(
+  stopping_rounds,
+  maximize = FALSE,
+  metric_name = NULL,
+  verbose = TRUE
+)
 }
 \arguments{
 \item{stopping_rounds}{The number of rounds with no improvement in
diff --git a/R-package/man/predict.xgb.Booster.Rd b/R-package/man/predict.xgb.Booster.Rd
index 0a9bb4616a91..69b48cd15bba 100644
--- a/R-package/man/predict.xgb.Booster.Rd
+++ b/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,
-  outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE,
-  predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
-  reshape = FALSE, ...)
+\method{predict}{xgb.Booster}(
+  object,
+  newdata,
+  missing = NA,
+  outputmargin = FALSE,
+  ntreelimit = NULL,
+  predleaf = FALSE,
+  predcontrib = FALSE,
+  approxcontrib = FALSE,
+  predinteraction = FALSE,
+  reshape = FALSE,
+  training = FALSE,
+  ...
+)
 
 \method{predict}{xgb.Booster.handle}(object, ...)
 }
diff --git a/R-package/man/xgb.create.features.Rd b/R-package/man/xgb.create.features.Rd
index 1c39c088445c..9c59d90b1f58 100644
--- a/R-package/man/xgb.create.features.Rd
+++ b/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"))
 
 }
diff --git a/R-package/man/xgb.cv.Rd b/R-package/man/xgb.cv.Rd
index 180c09379d57..cbe9966fd003 100644
--- a/R-package/man/xgb.cv.Rd
+++ b/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,
-  prediction = FALSE, showsd = TRUE, metrics = list(), obj = NULL,
-  feval = NULL, stratified = TRUE, folds = NULL, verbose = TRUE,
-  print_every_n = 1L, early_stopping_rounds = NULL, maximize = NULL,
-  callbacks = list(), ...)
+xgb.cv(
+  params = list(),
+  data,
+  nrounds,
+  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}.
diff --git a/R-package/man/xgb.dump.Rd b/R-package/man/xgb.dump.Rd
index 79332f5226ae..210c6e2a967b 100644
--- a/R-package/man/xgb.dump.Rd
+++ b/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,
-  dump_format = c("text", "json"), ...)
+xgb.dump(
+  model,
+  fname = NULL,
+  fmap = "",
+  with_stats = FALSE,
+  dump_format = c("text", "json"),
+  ...
+)
 }
 \arguments{
 \item{model}{the model object.}
diff --git a/R-package/man/xgb.importance.Rd b/R-package/man/xgb.importance.Rd
index 5c968d207c24..84a18e1f2a2e 100644
--- a/R-package/man/xgb.importance.Rd
+++ b/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,
-  data = NULL, label = NULL, target = NULL)
+xgb.importance(
+  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
diff --git a/R-package/man/xgb.model.dt.tree.Rd b/R-package/man/xgb.model.dt.tree.Rd
index a5acc42268e0..cf1750117968 100644
--- a/R-package/man/xgb.model.dt.tree.Rd
+++ b/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,
-  trees = NULL, use_int_id = FALSE, ...)
+xgb.model.dt.tree(
+  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
diff --git a/R-package/man/xgb.plot.deepness.Rd b/R-package/man/xgb.plot.deepness.Rd
index fc9351c55495..b642398701e2 100644
--- a/R-package/man/xgb.plot.deepness.Rd
+++ b/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",
-  "med.weight"))
+xgb.ggplot.deepness(
+  model = NULL,
+  which = c("2x1", "max.depth", "med.depth", "med.weight")
+)
 
-xgb.plot.deepness(model = NULL, which = c("2x1", "max.depth", "med.depth",
-  "med.weight"), plot = TRUE, ...)
+xgb.plot.deepness(
+  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
diff --git a/R-package/man/xgb.plot.importance.Rd b/R-package/man/xgb.plot.importance.Rd
index fce29f052e30..691a8fdfca66 100644
--- a/R-package/man/xgb.plot.importance.Rd
+++ b/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,
-  measure = NULL, rel_to_first = FALSE, n_clusters = c(1:10), ...)
-
-xgb.plot.importance(importance_matrix = NULL, top_n = NULL,
-  measure = NULL, rel_to_first = FALSE, left_margin = 10, cex = NULL,
-  plot = TRUE, ...)
+xgb.ggplot.importance(
+  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,
+  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}}.}
diff --git a/R-package/man/xgb.plot.multi.trees.Rd b/R-package/man/xgb.plot.multi.trees.Rd
index 84055dd22089..74c4a06040f7 100644
--- a/R-package/man/xgb.plot.multi.trees.Rd
+++ b/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,
-  plot_width = NULL, plot_height = NULL, render = TRUE, ...)
+xgb.plot.multi.trees(
+  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.}
diff --git a/R-package/man/xgb.plot.shap.Rd b/R-package/man/xgb.plot.shap.Rd
index 79643ecb4721..3cd3a8953dc8 100644
--- a/R-package/man/xgb.plot.shap.Rd
+++ b/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,
-  model = NULL, 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, ...)
+xgb.plot.shap(
+  data,
+  shap_contrib = NULL,
+  features = NULL,
+  top_n = 1,
+  model = NULL,
+  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}.}
diff --git a/R-package/man/xgb.plot.tree.Rd b/R-package/man/xgb.plot.tree.Rd
index 15685a157dbc..3f9f99a18baf 100644
--- a/R-package/man/xgb.plot.tree.Rd
+++ b/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,
-  plot_width = NULL, plot_height = NULL, render = TRUE,
-  show_node_id = FALSE, ...)
+xgb.plot.tree(
+  feature_names = NULL,
+  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.}
diff --git a/R-package/man/xgb.train.Rd b/R-package/man/xgb.train.Rd
index 8b2cdc556bf1..a6c91ccf47de 100644
--- a/R-package/man/xgb.train.Rd
+++ b/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,
-  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,
-  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(), ...)
+xgb.train(
+  params = list(),
+  data,
+  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,
+  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.