diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index f23e1c7f1f..8d3b4388ae 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -32,6 +32,12 @@ setClass("PipelineModel", representation(model = "jobj"))
#' @param family Error distribution. "gaussian" -> linear regression, "binomial" -> logistic reg.
#' @param lambda Regularization parameter
#' @param alpha Elastic-net mixing parameter (see glmnet's documentation for details)
+#' @param standardize Whether to standardize features before training
+#' @param solver The solver algorithm used for optimization, this can be "l-bfgs", "normal" and
+#' "auto". "l-bfgs" denotes Limited-memory BFGS which is a limited-memory
+#' quasi-Newton optimization method. "normal" denotes using Normal Equation as an
+#' analytical solution to the linear regression problem. The default value is "auto"
+#' which means that the solver algorithm is selected automatically.
#' @return a fitted MLlib model
#' @rdname glm
#' @export
@@ -79,9 +85,15 @@ setMethod("predict", signature(object = "PipelineModel"),
#'
#' Returns the summary of a model produced by glm(), similarly to R's summary().
#'
-#' @param x A fitted MLlib model
-#' @return a list with a 'coefficient' component, which is the matrix of coefficients. See
-#' summary.glm for more information.
+#' @param object A fitted MLlib model
+#' @return a list with 'devianceResiduals' and 'coefficients' components for gaussian family
+#' or a list with 'coefficients' component for binomial family. \cr
+#' For gaussian family: the 'devianceResiduals' gives the min/max deviance residuals
+#' of the estimation, the 'coefficients' gives the estimated coefficients and their
+#' estimated standard errors, t values and p-values. (It only available when model
+#' fitted by normal solver.) \cr
+#' For binomial family: the 'coefficients' gives the estimated coefficients.
+#' See summary.glm for more information. \cr
#' @rdname summary
#' @export
#' @examples
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 437bd4756c..a744b76be7 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -286,24 +286,37 @@ head(teenagers)
# Machine Learning
-SparkR allows the fitting of generalized linear models over DataFrames using the [glm()](api/R/glm.html) function. Under the hood, SparkR uses MLlib to train a model of the specified family. Currently the gaussian and binomial families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', '+', and '-'. The example below shows the use of building a gaussian GLM model using SparkR.
+SparkR allows the fitting of generalized linear models over DataFrames using the [glm()](api/R/glm.html) function. Under the hood, SparkR uses MLlib to train a model of the specified family. Currently the gaussian and binomial families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', ':', '+', and '-'.
+
+The [summary()](api/R/summary.html) function gives the summary of a model produced by [glm()](api/R/glm.html).
+
+* For gaussian GLM model, it returns a list with 'devianceResiduals' and 'coefficients' components. The 'devianceResiduals' gives the min/max deviance residuals of the estimation; the 'coefficients' gives the estimated coefficients and their estimated standard errors, t values and p-values. (It only available when model fitted by normal solver.)
+* For binomial GLM model, it returns a list with 'coefficients' component which gives the estimated coefficients.
+
+The examples below show the use of building gaussian GLM model and binomial GLM model using SparkR.
+
+## Gaussian GLM model
{% highlight r %}
# Create the DataFrame
df <- createDataFrame(sqlContext, iris)
-# Fit a linear model over the dataset.
+# Fit a gaussian GLM model over the dataset.
model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian")
-# Model coefficients are returned in a similar format to R's native glm().
+# Model summary are returned in a similar format to R's native glm().
summary(model)
+##$devianceResiduals
+## Min Max
+## -1.307112 1.412532
+##
##$coefficients
-## Estimate
-##(Intercept) 2.2513930
-##Sepal_Width 0.8035609
-##Species_versicolor 1.4587432
-##Species_virginica 1.9468169
+## Estimate Std. Error t value Pr(>|t|)
+##(Intercept) 2.251393 0.3697543 6.08889 9.568102e-09
+##Sepal_Width 0.8035609 0.106339 7.556598 4.187317e-12
+##Species_versicolor 1.458743 0.1121079 13.01195 0
+##Species_virginica 1.946817 0.100015 19.46525 0
# Make predictions based on the model.
predictions <- predict(model, newData = df)
@@ -317,3 +330,24 @@ head(select(predictions, "Sepal_Length", "prediction"))
##6 5.4 5.385281
{% endhighlight %}
+
+## Binomial GLM model
+
+
+{% highlight r %}
+# Create the DataFrame
+df <- createDataFrame(sqlContext, iris)
+training <- filter(df, df$Species != "setosa")
+
+# Fit a binomial GLM model over the dataset.
+model <- glm(Species ~ Sepal_Length + Sepal_Width, data = training, family = "binomial")
+
+# Model coefficients are returned in a similar format to R's native glm().
+summary(model)
+##$coefficients
+## Estimate
+##(Intercept) -13.046005
+##Sepal_Length 1.902373
+##Sepal_Width 0.404655
+{% endhighlight %}
+
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index ca55d5915e..f7c44f0a51 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -145,6 +145,9 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
/**
* Set the solver algorithm used for optimization.
* In case of linear regression, this can be "l-bfgs", "normal" and "auto".
+ * "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton
+ * optimization method. "normal" denotes using Normal Equation as an analytical
+ * solution to the linear regression problem.
* The default value is "auto" which means that the solver algorithm is
* selected automatically.
* @group setParam