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Predict from an epi_workflow

Source: R/epi_workflow.R
predict-epi_workflow.Rd

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
#> An `epi_df` object, 56 x 3 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * other_keys = geo_value, time_value
#> * as_of     = 2022-05-31
#> 
#> # A tibble: 56 × 3
#>    geo_value time_value     .pred
#>  * <chr>     <date>         <dbl>
#>  1 ak        2021-12-31  0.377   
#>  2 al        2021-12-31  0.280   
#>  3 ar        2021-12-31  0.452   
#>  4 as        2021-12-31 -0.000458
#>  5 az        2021-12-31  0.656   
#>  6 ca        2021-12-31  0.293   
#>  7 co        2021-12-31  0.569   
#>  8 ct        2021-12-31  0.642   
#>  9 dc        2021-12-31  1.02    
#> 10 de        2021-12-31  0.734   
#> # ℹ 46 more rows