layer_predict: Prediction layer for postprocessing

In cmu-delphi/epipredict: Basic epidemiology forecasting methods

Prediction layer for postprocessing

Description

Implements prediction on a fitted epi_workflow. One may want different types of prediction, and to potentially apply this after some amount of postprocessing. This would typically be the first layer in a frosting postprocessor.

Usage

layer_predict(
  frosting,
  type = NULL,
  opts = list(),
  ...,
  id = rand_id("predict_default")
)

Arguments

frosting	a frosting object
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.
id	a string identifying the layer

Value

An updated frosting object

See Also

parsnip::predict.model_fit()

Examples

jhu <- covid_case_death_rates %>%
  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) %>%
  step_epi_naomit()

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

# Predict layer alone
f <- frosting() %>% layer_predict()
wf1 <- wf %>% add_frosting(f)

p1 <- predict(wf1, latest)
p1

# Prediction with interval
f <- frosting() %>% layer_predict(type = "pred_int")
wf2 <- wf %>% add_frosting(f)

p2 <- predict(wf2, latest)
p2

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