[SPARK-13925][ML][SPARKR] Expose R-like summary statistics in SparkR::glm for more family and link functions

## What changes were proposed in this pull request?
Expose R-like summary statistics in SparkR::glm for more family and link functions.
Note: Not all values in R [summary.glm](http://stat.ethz.ch/R-manual/R-patched/library/stats/html/summary.glm.html) are exposed, we only provide the most commonly used statistics in this PR. More statistics can be added in the followup work.

## How was this patch tested?
Unit tests.

SparkR Output:
```
Deviance Residuals:
(Note: These are approximate quantiles with relative error <= 0.01)
     Min        1Q    Median        3Q       Max
-0.95096  -0.16585  -0.00232   0.17410   0.72918

Coefficients:
                    Estimate  Std. Error  t value  Pr(>|t|)
(Intercept)         1.6765    0.23536     7.1231   4.4561e-11
Sepal_Length        0.34988   0.046301    7.5566   4.1873e-12
Species_versicolor  -0.98339  0.072075    -13.644  0
Species_virginica   -1.0075   0.093306    -10.798  0

(Dispersion parameter for gaussian family taken to be 0.08351462)

    Null deviance: 28.307  on 149  degrees of freedom
Residual deviance: 12.193  on 146  degrees of freedom
AIC: 59.22

Number of Fisher Scoring iterations: 1
```
R output:
```
Deviance Residuals:
     Min        1Q    Median        3Q       Max
-0.95096  -0.16522   0.00171   0.18416   0.72918

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)
(Intercept)        1.67650    0.23536   7.123 4.46e-11 ***
Sepal.Length       0.34988    0.04630   7.557 4.19e-12 ***
Speciesversicolor -0.98339    0.07207 -13.644  < 2e-16 ***
Speciesvirginica  -1.00751    0.09331 -10.798  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.08351462)

    Null deviance: 28.307  on 149  degrees of freedom
Residual deviance: 12.193  on 146  degrees of freedom
AIC: 59.217

Number of Fisher Scoring iterations: 2
```

cc mengxr

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12393 from yanboliang/spark-13925.

1 files changed, 46 insertions, 6 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 475a308385..f66323e36c 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
@@ -30,19 +30,59 @@ private[r] class GeneralizedLinearRegressionWrapper private (
   private val glm: GeneralizedLinearRegressionModel =
     pipeline.stages(1).asInstanceOf[GeneralizedLinearRegressionModel]
 
+  lazy val rFeatures: Array[String] = if (glm.getFitIntercept) {
+    Array("(Intercept)") ++ features
+  } else {
+    features
+  }
+
   lazy val rCoefficients: Array[Double] = if (glm.getFitIntercept) {
-    Array(glm.intercept) ++ glm.coefficients.toArray
+    Array(glm.intercept) ++ glm.coefficients.toArray ++
+      rCoefficientStandardErrors ++ rTValues ++ rPValues
   } else {
-    glm.coefficients.toArray
+    glm.coefficients.toArray ++ rCoefficientStandardErrors ++ rTValues ++ rPValues
   }
 
-  lazy val rFeatures: Array[String] = if (glm.getFitIntercept) {
-    Array("(Intercept)") ++ features
+  private lazy val rCoefficientStandardErrors = if (glm.getFitIntercept) {
+    Array(glm.summary.coefficientStandardErrors.last) ++
+      glm.summary.coefficientStandardErrors.dropRight(1)
   } else {
-    features
+    glm.summary.coefficientStandardErrors
+  }
+
+  private lazy val rTValues = if (glm.getFitIntercept) {
+    Array(glm.summary.tValues.last) ++ glm.summary.tValues.dropRight(1)
+  } else {
+    glm.summary.tValues
   }
 
-  def transform(dataset: DataFrame): DataFrame = {
+  private lazy val rPValues = if (glm.getFitIntercept) {
+    Array(glm.summary.pValues.last) ++ glm.summary.pValues.dropRight(1)
+  } else {
+    glm.summary.pValues
+  }
+
+  lazy val rDispersion: Double = glm.summary.dispersion
+
+  lazy val rNullDeviance: Double = glm.summary.nullDeviance
+
+  lazy val rDeviance: Double = glm.summary.deviance
+
+  lazy val rResidualDegreeOfFreedomNull: Long = glm.summary.residualDegreeOfFreedomNull
+
+  lazy val rResidualDegreeOfFreedom: Long = glm.summary.residualDegreeOfFreedom
+
+  lazy val rAic: Double = glm.summary.aic
+
+  lazy val rNumIterations: Int = glm.summary.numIterations
+
+  lazy val rDevianceResiduals: DataFrame = glm.summary.residuals()
+
+  lazy val rFamily: String = glm.getFamily
+
+  def residuals(residualsType: String): DataFrame = glm.summary.residuals(residualsType)
+
+  def transform(dataset: Dataset[_]): DataFrame = {
     pipeline.transform(dataset).drop(glm.getFeaturesCol)
   }
 }