Github user actuaryzhang commented on the issue:
https://github.com/apache/spark/pull/16729
@felixcheung OK, new implementation of # 3. Now works in two ways:
1. `family = "tweedie"` + `variancePower` + `linkPower`
2. When `statmod` is available, `tweedie()`
Please take another look. Thanks.
```
# 1. Use variancePower and linkPower directly
> model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species,
+ family = "tweedie", variancePower = 1.2, linkPower =
0.0)
> summary(model)$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6455411 0.07672839 8.413327 3.330669e-14
Sepal_Length 0.1169143 0.01508433 7.750714 1.425526e-12
Species_versicolor -0.3224752 0.02345653 -13.747781 0.000000e+00
Species_virginica -0.3282173 0.03042303 -10.788450 0.000000e+00
# 2. Use statmod
> library(statmod)
> model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species, family
= tweedie(1.2, 0))
> summary(model)$coefficients
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6455411 0.07672839 8.413327 3.330669e-14
Sepal_Length 0.1169143 0.01508433 7.750714 1.425526e-12
Species_versicolor -0.3224752 0.02345653 -13.747781 0.000000e+00
Species_virginica -0.3282173 0.03042303 -10.788450 0.000000e+00
```
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