R: Tidying methods for Spark ML linear models

ml_glm_tidiers {sparklyr} | R Documentation

## Tidying methods for Spark ML linear models

### Description

These methods summarize the results of Spark ML models into tidy forms.

### Usage

    ## S3 method for class 'ml_model_generalized_linear_regression'
    tidy(x, exponentiate = FALSE,
      ...)

    ## S3 method for class 'ml_model_linear_regression'
    tidy(x, ...)

    ## S3 method for class 'ml_model_generalized_linear_regression'
    augment(x, newdata = NULL,
      type.residuals = c("working", "deviance", "pearson", "response"), ...)

    ## S3 method for class 'ml_model_linear_regression'
    augment(x, newdata = NULL,
      type.residuals = c("working", "deviance", "pearson", "response"), ...)

    ## S3 method for class 'ml_model_generalized_linear_regression'
    glance(x, ...)

    ## S3 method for class 'ml_model_linear_regression'
    glance(x, ...)

### Arguments

`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()`.

[Package sparklyr version 0.8.0 Index]