ml_glm_tidiers

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Tidying methods for Spark ML linear models

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

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

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

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

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

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

# S3 method for 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().

Aliases
  • ml_glm_tidiers
  • tidy.ml_model_generalized_linear_regression
  • tidy.ml_model_linear_regression
  • augment.ml_model_generalized_linear_regression
  • augment.ml_model_linear_regression
  • glance.ml_model_generalized_linear_regression
  • glance.ml_model_linear_regression
Documentation reproduced from package sparklyr, version 0.8.1-9001, License: Apache License 2.0 | file LICENSE

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