predict-epi_workflow: Predict from an epi_workflow

In cmu-delphi/epipredict: Basic epidemiology forecasting methods

Predict from an epi_workflow

Description

This is the predict() method for a fit epi_workflow object. The nice thing about predicting from an epi_workflow is that it will:

• Preprocess new_data using the preprocessing method specified when the workflow was created and fit. This is accomplished using hardhat::forge(), which will apply any formula preprocessing or call recipes::bake() if a recipe was supplied.

• Call parsnip::predict.model_fit() for you using the underlying fit parsnip model.

• Ensure that the returned object is an epiprocess::epi_df where possible. Specifically, the output will have time_value and geo_value columns as well as the prediction.

Usage

## S3 method for class 'epi_workflow'
predict(object, new_data, type = NULL, opts = list(), ...)

Arguments

object	An epi_workflow that has been fit by workflows::fit.workflow()
new_data	A data frame containing the new predictors to preprocess and predict on
type	A single character value or NULL. Possible values are "numeric", "class", "prob", "conf_int", "pred_int", "quantile", "time", "hazard", "survival", or "raw". When NULL, predict() will choose an appropriate value based on the model's mode.
opts	A list of optional arguments to the underlying predict function that will be used when type = "raw". The list should not include options for the model object or the new data being predicted.
...	Additional parsnip-related options, depending on the value of type. Arguments to the underlying model's prediction function cannot be passed here (use the opts argument instead). Possible arguments are: interval: for type equal to "survival" or "quantile", should interval estimates be added, if available? Options are "none" and "confidence". level: for type equal to "conf_int", "pred_int", or "survival", this is the parameter for the tail area of the intervals (e.g. confidence level for confidence intervals). Default value is 0.95. std_error: for type equal to "conf_int" or "pred_int", add the standard error of fit or prediction (on the scale of the linear predictors). Default value is FALSE. quantile: for type equal to quantile, the quantiles of the distribution. Default is (1:9)/10. eval_time: for type equal to "survival" or "hazard", the time points at which the survival probability or hazard is estimated.

Value

A data frame of model predictions, with as many rows as new_data has. If new_data is an epi_df or a data frame with time_value or geo_value columns, then the result will have those as well.

Examples

jhu <- covid_case_death_rates

r <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = 7) %>%
  step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
  step_epi_naomit()

wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
latest <- jhu %>% dplyr::filter(time_value >= max(time_value) - 14)

preds <- predict(wf, latest)
preds

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