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https://issues.apache.org/jira/browse/SPARK-18501?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15676291#comment-15676291
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Apache Spark commented on SPARK-18501:
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User 'yanboliang' has created a pull request for this issue:
https://github.com/apache/spark/pull/15930
> SparkR spark.glm error on collinear data
> -----------------------------------------
>
> Key: SPARK-18501
> URL: https://issues.apache.org/jira/browse/SPARK-18501
> Project: Spark
> Issue Type: Bug
> Components: ML, SparkR
> Reporter: Yanbo Liang
> Assignee: Yanbo Liang
>
> Spark {{GeneralizedLinearRegression}} can handle collinear data since the
> underlying {{WeightedLeastSquares}} can be solved by local "l-bfgs"(rather
> than "normal"). But the SparkR wrapper {{spark.glm}} throw errors when
> fitting on collinear data:
> {code}
> > df <- read.df("data/mllib/sample_binary_classification_data.txt", source =
> > "libsvm")
> > model <- spark.glm(df, label ~ features, family = binomial(link = "logit”))
> > summary(model)
> Error in `rownames<-`(`*tmp*`, value = c("(Intercept)", "features_0", :
> length of 'dimnames' [1] not equal to array extent
> {code}
> After depth study of this error, I found it was caused the standard error of
> coefficients, t value and p value are not available when the underlying
> {{WeightedLeastSquares}} was solved by local "l-bfgs". So the coefficients
> matrix was generated failed.
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