Is there a way to display standard errors with ml_linear_regression in sparklyr?

user_14341263 0
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When running a linear regression using sparklyr, such as:

cached_cars %>%
  ml_linear_regression(mpg ~ .) %>%
  summary()

The results do not include standard errors

Deviance Residuals:
     Min       1Q   Median       3Q      Max 
-3.47339 -1.37936 -0.06554  1.05105  4.39057 

Coefficients:
(Intercept) cyl_cyl_8.0 cyl_cyl_4.0        disp          hp        drat
16.15953652  3.29774653  1.66030673  0.01391241 -0.04612835  0.02635025
          wt        qsec          vs          am       gear        carb 
 -3.80624757  0.64695710  1.74738689  2.61726546 0.76402917  0.50935118  

R-Squared: 0.8816
Root Mean Squared Error: 2.041
  1. Is there a way to display standard errors when running this regression?
  2. Is there a way to cluster standard errors in sparklyr?
  3. I have also been trying to run a linear model with multiple group fixed effects in sparklyr. In base R, I have done so with felm. Does anyone have experience doing this in sparklyr?

Solutions using SparkR are also highly appreciated.

sparkr sparklyr linear-regression r
2 Answers

I received a useful answer to my first question at community.rstudio.com.

The answer from yitaoli is the following:

library(sparklyr)

spark_version <- "2.4.4" # This is the version of Spark I ran this example code with,
# but I think everything that follows should work in all versions of Spark anyways

sc <- spark_connect(master = "local", version = spark_version)

cached_cars <- copy_to(sc, mtcars)
model <- cached_cars %>%
  ml_linear_regression(mpg ~ .)

coeff_std_errs <- invoke(model$model$.jobj, "summary") %>%
  invoke("coefficientStandardErrors")

print(coeff_std_errs)
user_143412630
  1. I think what you are looking for is tidy(). So in your case:
regression1 <- cached_cars %>%
  ml_linear_regression(mpg ~ .) 
tidy(regression1)

As for clustered standard errors and fixed effects I do not know.

user_141218090
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