layer_predictive_distn: Returns predictive distributions

In cmu-delphi/epipredict: Basic epidemiology forecasting methods

Returns predictive distributions

Description

This function calculates an approximation to a parametric predictive distribution. Predictive distributions from linear models require ⁠x* (X'X)^{-1} x*⁠ along with the degrees of freedom. This function approximates both. It should be reasonably accurate for models fit using lm when the new point ⁠x*⁠ isn't too far from the bulk of the data.

Usage

layer_predictive_distn(
  frosting,
  ...,
  dist_type = c("gaussian", "student_t"),
  truncate = c(-Inf, Inf),
  name = ".pred_distn",
  id = rand_id("predictive_distn")
)

Arguments

frosting	a frosting postprocessor
...	Unused, include for consistency with other layers.
dist_type	Gaussian or Student's t predictive intervals
truncate	Do we truncate the distribution to an interval
name	character. The name for the output column.
id	a random id string

Value

an updated frosting postprocessor with additional columns of the residual quantiles added to the prediction

Examples

library(dplyr)
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)

f <- frosting() %>%
  layer_predict() %>%
  layer_predictive_distn() %>%
  layer_naomit(.pred)
wf1 <- wf %>% add_frosting(f)

p <- forecast(wf1)
p

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