Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/14229#discussion_r74675732
--- Diff: R/pkg/R/mllib.R ---
@@ -605,6 +701,69 @@ setMethod("spark.survreg", signature(data =
"SparkDataFrame", formula = "formula
return(new("AFTSurvivalRegressionModel", jobj = jobj))
})
+#' Latent Dirichlet Allocation
+#'
+#' \code{spark.lda} fits a Latent Dirichlet Allocation model on a
SparkDataFrame. Users can call
+#' \code{summary} to get a summary of the fitted LDA model,
\code{spark.posterior} to compute
+#' posterior probabilities on new data, \code{spark.perplexity} to compute
log perplexity on new
+#' data and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @param data A SparkDataFrame for training
+#' @param features Features column name, default "features". Either Vector
format column or String
+#' format column are accepted.
+#' @param k Number of topics, default 10
+#' @param maxIter Maximum iterations, default 20
+#' @param optimizer Optimizer to train an LDA model, "online" or "em",
default "online"
+#' @param subsamplingRate (For online optimizer) Fraction of the corpus to
be sampled and used in
+# each iteration of mini-batch gradient descent, in range (0, 1],
default 0.05
+#' @param topicConcentration concentration parameter (commonly named
\code{beta} or \code{eta}) for
+#' the prior placed on topic distributions over terms, default -1
to set automatically on the
+#' Spark side. Use \code{summary} to retrieve the effective
topicConcentration.
+#' @param docConcentration concentration parameter (commonly named
\code{alpha}) for the
+#' prior placed on documents distributions over topics
(\code{theta}), default -1 to set
+#' automatically on the Spark side. Use \code{summary} to retrieve
the effective
+#' docConcentration.
+#' @param customizedStopWords stopwords that need to be removed from the
given corpus. Only effected
+#' given training data with string format column.
+#' @param maxVocabSize maximum vocabulary size, default 1 << 18
+#' @return \code{spark.lda} returns a fitted Latent Dirichlet Allocation
model
+#' @rdname spark.lda
+#' @aliases spark.lda,SparkDataFrame
--- End diff --
add `-method`
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