[SPARK-16142][R] group naiveBayes method docs in a single Rd

## What changes were proposed in this pull request?

This PR groups `spark.naiveBayes`, `summary(NB)`, `predict(NB)`, and `write.ml(NB)` into a single Rd.

## How was this patch tested?

Manually checked generated HTML doc. See attached screenshots.

![screen shot 2016-06-23 at 2 11 00 pm](https://cloud.githubusercontent.com/assets/829644/16320452/a5885e92-394c-11e6-994f-2ab5cddad86f.png)

![screen shot 2016-06-23 at 2 11 15 pm](https://cloud.githubusercontent.com/assets/829644/16320455/aad1f6d8-394c-11e6-8ef4-13bee989f52f.png)

Author: Xiangrui Meng <meng@databricks.com>

Closes #13877 from mengxr/SPARK-16142.

1 files changed, 42 insertions, 48 deletions

diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index dbff1b900d..853cfce74a 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -218,9 +218,10 @@ print.summary.GeneralizedLinearRegressionModel <- function(x, ...) {
 
 #  Makes predictions from a generalized linear model produced by glm() or spark.glm(),
 #  similarly to R's predict().
-#'
+
 #' @param newData SparkDataFrame for testing
-#' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named "prediction"
+#' @return \code{predict} returns a SparkDataFrame containing predicted labels in a column named
+#'         "prediction"
 #' @rdname spark.glm
 #' @export
 #' @note predict(GeneralizedLinearRegressionModel) since 1.5.0
@@ -229,41 +230,26 @@ setMethod("predict", signature(object = "GeneralizedLinearRegressionModel"),
             return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
           })
 
-#' Predicted values based on model
-#'
-#' Makes predictions from a naive Bayes model or a model produced by spark.naiveBayes(),
-#' similarly to R package e1071's predict.
-#'
-#' @param object A fitted naive Bayes model
-#' @rdname predict
+# Makes predictions from a naive Bayes model or a model produced by spark.naiveBayes(),
+# similarly to R package e1071's predict.
+
+#' @rdname spark.naiveBayes
+#' @return \code{predict} returns a SparkDataFrame containing predicted labeled in a column named
+#' "prediction"
 #' @export
-#' @examples
-#' \dontrun{
-#' model <- spark.naiveBayes(trainingData, y ~ x)
-#' predicted <- predict(model, testData)
-#' showDF(predicted)
-#'}
 #' @note predict(NaiveBayesModel) since 2.0.0
 setMethod("predict", signature(object = "NaiveBayesModel"),
           function(object, newData) {
             return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
           })
 
-#' Get the summary of a naive Bayes model
-#'
-#' Returns the summary of a naive Bayes model produced by spark.naiveBayes(),
-#' similarly to R's summary().
-#'
-#' @param object A fitted MLlib model
-#' @return a list containing 'apriori', the label distribution, and 'tables', conditional
-#          probabilities given the target label
-#' @rdname summary
+# Returns the summary of a naive Bayes model produced by \code{spark.naiveBayes}
+
+#' @param object A naive Bayes model fitted by \code{spark.naiveBayes}
+#' @return \code{summary} returns a list containing \code{apriori}, the label distribution, and
+#'         \code{tables}, conditional probabilities given the target label
+#' @rdname spark.naiveBayes
 #' @export
-#' @examples
-#' \dontrun{
-#' model <- spark.naiveBayes(trainingData, y ~ x)
-#' summary(model)
-#'}
 #' @note summary(NaiveBayesModel) since 2.0.0
 setMethod("summary", signature(object = "NaiveBayesModel"),
           function(object, ...) {
@@ -390,23 +376,41 @@ setMethod("predict", signature(object = "KMeansModel"),
             return(dataFrame(callJMethod(object@jobj, "transform", newData@sdf)))
           })
 
-#' Fit a Bernoulli naive Bayes model
+#' Naive Bayes Models
 #'
-#' Fit a Bernoulli naive Bayes model on a Spark DataFrame (only categorical data is supported).
+#' \code{spark.naiveBayes} fits a Bernoulli naive Bayes model against a SparkDataFrame.
+#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#' Only categorical data is supported.
 #'
-#' @param data SparkDataFrame for training
+#' @param data A \code{SparkDataFrame} of observations and labels for model fitting
 #' @param formula A symbolic description of the model to be fitted. Currently only a few formula
 #'               operators are supported, including '~', '.', ':', '+', and '-'.
 #' @param smoothing Smoothing parameter
-#' @return a fitted naive Bayes model
+#' @return \code{spark.naiveBayes} returns a fitted naive Bayes model
 #' @rdname spark.naiveBayes
+#' @name spark.naiveBayes
 #' @seealso e1071: \url{https://cran.r-project.org/web/packages/e1071/}
 #' @export
 #' @examples
 #' \dontrun{
 #' df <- createDataFrame(infert)
+#'
+#' # fit a Bernoulli naive Bayes model
 #' model <- spark.naiveBayes(df, education ~ ., smoothing = 0)
-#'}
+#'
+#' # get the summary of the model
+#' summary(model)
+#'
+#' # make predictions
+#' predictions <- predict(model, df)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
 #' @note spark.naiveBayes since 2.0.0
 setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "formula"),
           function(data, formula, smoothing = 1.0, ...) {
@@ -416,25 +420,15 @@ setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "form
             return(new("NaiveBayesModel", jobj = jobj))
           })
 
-#' Save fitted MLlib model to the input path
-#'
-#' Save the Bernoulli naive Bayes model to the input path.
-#'
-#' @param object A fitted Bernoulli naive Bayes model
+# Saves the Bernoulli naive Bayes model to the input path.
+
 #' @param path The directory where the model is saved
 #' @param overwrite Overwrites or not if the output path already exists. Default is FALSE
 #'                  which means throw exception if the output path exists.
 #'
-#' @rdname write.ml
-#' @name write.ml
+#' @rdname spark.naiveBayes
 #' @export
-#' @examples
-#' \dontrun{
-#' df <- createDataFrame(infert)
-#' model <- spark.naiveBayes(df, education ~ ., smoothing = 0)
-#' path <- "path/to/model"
-#' write.ml(model, path)
-#' }
+#' @seealso \link{read.ml}
 #' @note write.ml(NaiveBayesModel, character) since 2.0.0
 setMethod("write.ml", signature(object = "NaiveBayesModel", path = "character"),
           function(object, path, overwrite = FALSE) {