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From yanboliang <...@git.apache.org>
Subject [GitHub] spark pull request #16344: [SPARK-18929][ML] Add Tweedie distribution in GLM
Date Sun, 22 Jan 2017 16:40:33 GMT
Github user yanboliang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16344#discussion_r97227237
  
    --- Diff: mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala
---
    @@ -1052,6 +1217,120 @@ class GeneralizedLinearRegressionSuite
         assert(summary.solver === "irls")
       }
     
    +  test("glm summary: tweedie family with weight") {
    +    /*
    +      R code:
    +
    +      library(statmod)
    +      df <- as.data.frame(matrix(c(
    +        1.0, 1.0, 0.0, 5.0,
    +        0.5, 2.0, 1.0, 2.0,
    +        1.0, 3.0, 2.0, 1.0,
    +        0.0, 4.0, 3.0, 3.0), 4, 4, byrow = TRUE))
    +
    +      f <- glm(V1 ~ -1 + V3 + V4, data = df, weights = V2,
    +          family = tweedie(var.power = 1.6, link.power = 0))
    +
    +      Deviance Residuals:
    +            1        2        3        4
    +       0.6210  -0.0515   1.6935  -3.2539
    +
    +      Coefficients:
    +         Estimate Std. Error t value Pr(>|t|)
    +      V3  -0.4087     0.5205  -0.785    0.515
    +      V4  -0.1212     0.4082  -0.297    0.794
    +
    +      (Dispersion parameter for Tweedie family taken to be 3.830036)
    +
    +          Null deviance: 20.702  on 4  degrees of freedom
    +      Residual deviance: 13.844  on 2  degrees of freedom
    +      AIC: NA
    +
    +      Number of Fisher Scoring iterations: 11
    +
    +      residuals(model, type="pearson")
    --- End diff --
    
    Typos here? I guess you paste irrelevant results for the following ```residuals```, they
should be consistent with L1279-L1281.


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