Unique combinations of the predictors in a model

Returns one row of the model data for each unique combination of the variables used to predict the delay distribution parameters. Passing this to delay_parameter_draws() gives one set of draws per combination rather than one per observation, which is the same result with far fewer draws.

Usage

epidist_strata(object, vars = NULL)

Arguments

object

A model fit with epidist().

vars

A character vector of variables to take unique combinations of. If NULL, the default, the variables in the distributional parameter formulas are used.

Value

A tibble with one row per unique combination of vars, with the combination columns first.

Details

The variables are taken from the right hand side of each distributional parameter formula. The remaining columns are kept from the first row of the model data in which each combination occurs. This keeps the model variables that brms requires in newdata, such as the relative observation time and the censoring windows for the latent and marginal models. Those variables do not enter the distributional parameters, so the values kept do not change the draws.

A model with only an intercept has no predictors and so returns a single row. A continuous predictor has as many combinations as it has distinct values, so consider passing vars and a grid of your own instead.

See also

Other postprocess: add_summaries(), delay_parameter_draws()

Examples

if (FALSE) { # \dontrun{
# `fit` is a model fitted with `epidist()`
epidist_strata(fit)
} # }