[SPARK-16144][SPARKR] update R API doc for mllib

R/pkg/R/generics.R

@@ -1247,7 +1247,6 @@ setGeneric("spark.glm", function(data, formula, ...) { standardGeneric("spark.gl
 #' @export
 setGeneric("glm")
 
-#' predict
 #' @rdname predict
 #' @export
 setGeneric("predict", function(object, ...) { standardGeneric("predict") })
@@ -1272,7 +1271,6 @@ setGeneric("spark.naiveBayes", function(data, formula, ...) { standardGeneric("s
 #' @export
 setGeneric("spark.survreg", function(data, formula, ...) { standardGeneric("spark.survreg") })
 
-#' write.ml
 #' @rdname write.ml
 #' @export
 setGeneric("write.ml", function(object, path, ...) { standardGeneric("write.ml") })

R/pkg/R/mllib.R

@@ -53,6 +53,29 @@ setClass("AFTSurvivalRegressionModel", representation(jobj = "jobj"))
 #' @note KMeansModel since 2.0.0
 setClass("KMeansModel", representation(jobj = "jobj"))
 
+#' Saves the MLlib model to the input path
+#'
+#' Saves the MLlib model to the input path. For more information, see the specific
+#' MLlib model below.
+#' @rdname write.ml
+#' @name write.ml
+#' @export
+#' @seealso \link{spark.glm}, \link{glm}
+#' @seealso \link{spark.kmeans}, \link{spark.naiveBayes}, \link{spark.survreg}
+#' @seealso \link{read.ml}
+NULL
+
+#' Makes predictions from a MLlib model
+#'
+#' Makes predictions from a MLlib model. For more information, see the specific
+#' MLlib model below.
+#' @rdname predict
+#' @name predict
+#' @export
+#' @seealso \link{spark.glm}, \link{glm}
+#' @seealso \link{spark.kmeans}, \link{spark.naiveBayes}, \link{spark.survreg}
+NULL
+
 #' Generalized Linear Models
 #'
 #' Fits generalized linear model against a Spark DataFrame. Users can print, make predictions on the
@@ -145,7 +168,7 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDat
           })
 
 #  Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary().
-#'
+
 #' @param object A fitted generalized linear model
 #' @return \code{summary} returns a summary object of the fitted model, a list of components
 #'         including at least the coefficients, null/residual deviance, null/residual degrees
@@ -185,7 +208,7 @@ setMethod("summary", signature(object = "GeneralizedLinearRegressionModel"),
           })
 
 #  Prints the summary of GeneralizedLinearRegressionModel
-#'
+
 #' @rdname spark.glm
 #' @param x Summary object of fitted generalized linear model returned by \code{summary} function
 #' @export
@@ -343,7 +366,7 @@ setMethod("fitted", signature(object = "KMeansModel"),
           })
 
 #  Get the summary of a k-means model
-#'
+
 #' @param object A fitted k-means model
 #' @return \code{summary} returns the model's coefficients, size and cluster
 #' @rdname spark.kmeans
@@ -370,7 +393,7 @@ setMethod("summary", signature(object = "KMeansModel"),
           })
 
 #  Predicted values based on a k-means model
-#'
+
 #' @return \code{predict} returns the predicted values based on a k-means model
 #' @rdname spark.kmeans
 #' @export
@@ -463,7 +486,7 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
           })
 
 #  Saves the generalized linear 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.
@@ -481,7 +504,7 @@ setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel", pat
           })
 
 #  Save fitted MLlib 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.
@@ -506,6 +529,7 @@ setMethod("write.ml", signature(object = "KMeansModel", path = "character"),
 #' @rdname read.ml
 #' @name read.ml
 #' @export
+#' @seealso \link{write.ml}
 #' @examples
 #' \dontrun{
 #' path <- "path/to/model"