apply_frosting: Apply postprocessing to a fitted workflow

In cmu-delphi/epipredict: Basic epidemiology forecasting methods

Apply postprocessing to a fitted workflow

Description

This function is intended for internal use. It implements postprocessing inside of the predict() method for a fitted workflow.

Usage

apply_frosting(workflow, ...)

## Default S3 method:
apply_frosting(workflow, components, ...)

## S3 method for class 'epi_workflow'
apply_frosting(workflow, components, new_data, type = NULL, opts = list(), ...)

Arguments

workflow	An object of class workflow
...	additional arguments passed on to methods
components	a list of components containing model information. These will be updated and returned by the layer. These should be mold - the output of calling hardhat::mold() on the workflow. This contains information about the preprocessing, including the recipe. forged - the output of calling hardhat::forge() on the workflow. This should have predictors and outcomes for the new_data. It will have three components predictors, outcomes (if these were in the new_data), and extras (usually has the rest of the data, including keys). keys - we put the keys (time_value, geo_value, and any others) here for ease.
new_data	a data frame containing the new predictors to preprocess and predict on
type, opts	forwarded (along with ...) to predict.model_fit() and slather() for supported layers

cmu-delphi/epipredict documentation built on March 5, 2025, 12:17 p.m.