PySpark/SparkR doc doesn't explain RandomForestClassifier.featureSubsetStrategy well

In Scala doc (https://spark.apache.org/docs/2.3.0/api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier), we have:

The number of features to consider for splits at each tree node. Supported options:

• "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees > 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
• "all": use all features
• "onethird": use 1/3 of the features
• "sqrt": use sqrt(number of features)
• "log2": use log2(number of features)
• "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

These various settings are based on the following references:

• log2: tested in Breiman (2001)
• sqrt: recommended by Breiman manual for random forests
• The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.

The entire paragraph is missing in PySpark doc (https://spark.apache.org/docs/2.3.0/api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier.featureSubsetStrategy). And same issue for SparkR (https://github.com/apache/spark/blob/master/R/pkg/R/mllib_tree.R#L365).