Returns predictive distributions — layer_predictive

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

Details

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. Outside of that specific case, it is recommended to use layer_residual_quantiles(), or if you are working with a model that produces distributional predictions, use layer_quantile_distn().