Github user hhbyyh commented on the pull request:
https://github.com/apache/spark/pull/11549#issuecomment-193015522
Since we already have a `glm` in SparkR which is based on
`LogisticRegressionModel` and `LinearRegressionModel`. There're three ways to
extend it as I understand:
1. Change the current glm to use `GeneralizedLinearRegression`. Create
another `lm` interface for sparkR, and use LR as the model.
2. Keep glm R interface. and replace its implementation with GLM. This
means R can not invoke LR anymore.
2. Keep glm R interface, and combine the implementation with both LR and
GLM based on different solver parameter.
I'd prefer to use option 1. And I'm gonna send one PR(WIP) for solution 2,
which can later be adjusted to 1 or 3.
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