Batch process heterogeneity analyses across multiple outcome domains

runs the same planned subgroup contrasts (e.g., wealth, ethnicity, political orientation) over multiple outcome domains, with optional correction for multiple comparisons.

Usage

margot_planned_subgroups_batch(
  domain_models,
  X,
  base_defaults,
  label_mapping = NULL,
  subset_types,
  original_df,
  domain_names,
  subtitles,
  adjust = c("none", "bonferroni", "holm"),
  alpha = 0.05,
  ...
)

Arguments

domain_models

list of model sets; one element per outcome domain

X

model matrix (or data frame) of predictors used when the models were fitted

base_defaults

named list of default arguments passed to the downstream plotting / helper functions

label_mapping

named list of variable-to-label mappings; passed down to `margot_plot()`

subset_types

named list of subset specifications (e.g., list(wealth = subsets_standard_wealth))

original_df

the raw data frame containing all variables (needed for label recovery, plotting on the original scale, etc.)

domain_names

character vector naming each element in `domain_models`

subtitles

character vector of subtitles used in plot annotations; must be the same length as `domain_names`

adjust

character; correction method for multiple comparisons in plots. one of "none", "bonferroni", or "holm". default: "none".

alpha

numeric; significance threshold for correction. default: 0.05.

...

any additional arguments forwarded directly to [`margot_subset_batch()`]

Value

a nested list. the first level is the domain name; the second level is the subset type. each leaf contains the full list returned by `margot_subset_batch()`.