ml_glm_tidiers
Tidying methods for Spark ML linear models
Description
These methods summarize the results of Spark ML models into tidy forms.
Usage
tidyml_model_generalized_linear_regression(x, exponentiate = FALSE, …)
tidyml_model_linear_regression(x, …)
augmentml_model_generalized_linear_regression( x, newdata = NULL, type.residuals = c(“working”, “deviance”, “pearson”, “response”), … )
augmentml_model_linear_regression( x, newdata = NULL, type.residuals = c(“working”, “deviance”, “pearson”, “response”), … )
glanceml_model_generalized_linear_regression(x, …)
glanceml_model_linear_regression(x, …)
Arguments
| Argument | Description |
|---|---|
| x | a Spark ML model. |
| exponentiate | For GLM, whether to exponentiate the coefficient estimates (typical for logistic regression.) |
| … | extra arguments (not used.) |
| newdata | a tbl_spark of new data to use for prediction. |
| type.residuals | type of residuals, defaults to "working". Must be set to |
"working" when newdata is supplied.
Details
The residuals attached by augment are of type “working” by default, which is different from the default of “deviance” for residuals() or sdf_residuals().