Compute the observed quantile value for each term within grouped simulation results

Computes the specified quantile of x within each group, typically inside evaluate_model_results() for simulation evaluation pipelines.

Usage

eval_quantile(x, term = NULL, na.rm = FALSE)

Arguments

x

A numeric vector of estimates or statistics.

term

A named numeric vector with quantile probabilities for each term. For example, c("(Intercept)" = 0.05, x = 0.95). If NULL (default), computes the median (0.5).

na.rm

Logical; whether to remove missing values when computing the quantile. Defaults to FALSE.

Value

A numeric scalar representing the observed quantile of x within the current group.

Details

This function is designed to be used inside dplyr::summarise() within a grouped tidyverse pipeline, typically after grouping by term.

If term is provided, the current grouping must include a term variable matching the names in term. If a term in the group is not found in the provided term mapping, the function will return NA with a warning.

Examples

library(dplyr)
library(purrr)
library(broom.mixed)

sim_models <- tibble(
  id = 1:50,
  model = map(1:50, ~ lm(mpg ~ wt, data = mtcars))
) |>
  extract_model_results()

sim_models |>
  filter(term == "wt") |>
  group_by(term) |>
  evaluate_model_results(
    lower_quantile = eval_quantile(
      estimate,
      term = c("wt" = 0.05)
    ),
    upper_quantile = eval_quantile(
      estimate,
      term = c("wt" = 0.95)
    )
  )
#> # A tibble: 1 × 7
#>   term  n_models mean_estimate mean_std.error power lower_quantile
#>   <chr>    <int>         <dbl>          <dbl> <dbl>          <dbl>
#> 1 wt          50         -5.34          0.559     1          -5.34
#> # ℹ 1 more variable: upper_quantile <dbl>