Fit an epi_workflow object

This is the fit() method for an epi_workflow object that estimates parameters for a given model from a set of data. Fitting an epi_workflow involves two main steps, which are preprocessing the data and fitting the underlying parsnip model.

Usage

# S3 method for class 'epi_workflow'
fit(object, data, ..., control = workflows::control_workflow())

Arguments

object

an epi_workflow object

data

an epi_df of predictors and outcomes to use when fitting the epi_workflow

...

Not used

control

A workflows::control_workflow() object

Value

The epi_workflow object, updated with a fit parsnip model in the object$fit$fit slot.

See also

workflows::fit-workflow

Examples

jhu <- case_death_rate_subset %>%
  filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))

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

wf <- epi_workflow(r, parsnip::linear_reg()) %>% fit(jhu)
wf
#> 
#> ══ Epi Workflow [trained] ══════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> Postprocessor: None
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 
#> 2 Recipe steps.
#> 1. step_epi_lag()
#> 2. step_epi_ahead()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> 
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#> 
#> Coefficients:
#>       (Intercept)   lag_0_death_rate   lag_7_death_rate  lag_14_death_rate  
#>           0.32848           -0.01957           -0.02176           -0.05895  
#> 
#>