Github user JeremyNixon commented on a diff in the pull request:

    https://github.com/apache/spark/pull/13000#discussion_r63272747
  
    --- Diff: examples/src/main/r/ml.R ---
    @@ -25,30 +25,102 @@ library(SparkR)
     sc <- sparkR.init(appName="SparkR-ML-example")
     sqlContext <- sparkRSQL.init(sc)
     
    -# Train GLM of family 'gaussian'
    +############################ spark.glm and glm 
##############################################
    +
    +# Fit a generalized linear model with spark.glm
     training1 <- suppressWarnings(createDataFrame(sqlContext, iris))
     test1 <- training1
    -model1 <- glm(Sepal_Length ~ Sepal_Width + Species, training1, family = 
"gaussian")
    +model1 <- spark.glm(training1, Sepal_Length ~ Sepal_Width + Species, 
family = "gaussian")
     
     # Model summary
     summary(model1)
     
     # Prediction
     predictions1 <- predict(model1, test1)
    -head(select(predictions1, "Sepal_Length", "prediction"))
    +showDF(predictions1)
    +
    +# Fit a generalized linear model with glm (R-compliant)
    +sameModel <- glm(Sepal_Length ~ Sepal_Width + Species, training1, family = 
"gaussian")
    +summary(sameModel)
    +
    +############################ spark.survreg 
##############################################
    +
    +# Use the ovarian dataset available in R survival package
    +library(survival)
     
    -# Train GLM of family 'binomial'
    -training2 <- filter(training1, training1$Species != "setosa")
    +# Fit an accelerated failure time (AFT) survival regression model with 
spark.survreg
    +training2 <- suppressWarnings(createDataFrame(sqlContext, ovarian))
     test2 <- training2
    -model2 <- glm(Species ~ Sepal_Length + Sepal_Width, data = training2, 
family = "binomial")
    --- End diff --
    
    It may be worth keeping in the classification example for glm - users who 
come to the docs to see what's possible and who aren't familiar with link 
functions or don't assume that a binomial link function exists may not realize 
that it's possible to do classification with the algorithm. 


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