[SPARK-16710][SPARKR][ML] spark.glm should support weightCol

## What changes were proposed in this pull request?
Training GLMs on weighted dataset is very important use cases, but it is not supported by SparkR currently. Users can pass argument ```weights``` to specify the weights vector in native R. For ```spark.glm```, we can pass in the ```weightCol``` which is consistent with MLlib.

## How was this patch tested?
Unit test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14346 from yanboliang/spark-16710.

1 files changed, 3 insertions, 1 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
index 5642abc645..0d3181d0ac 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala
@@ -68,7 +68,8 @@ private[r] object GeneralizedLinearRegressionWrapper
       family: String,
       link: String,
       tol: Double,
-      maxIter: Int): GeneralizedLinearRegressionWrapper = {
+      maxIter: Int,
+      weightCol: String): GeneralizedLinearRegressionWrapper = {
     val rFormula = new RFormula()
       .setFormula(formula)
     val rFormulaModel = rFormula.fit(data)
@@ -84,6 +85,7 @@ private[r] object GeneralizedLinearRegressionWrapper
       .setFitIntercept(rFormula.hasIntercept)
       .setTol(tol)
       .setMaxIter(maxIter)
+      .setWeightCol(weightCol)
     val pipeline = new Pipeline()
       .setStages(Array(rFormulaModel, glr))
       .fit(data)