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Subset Model Results for Binary and Categorical Exposures

Source: R/margot_subset_model.R
margot_subset_model.Rd

This function extracts and combines evaluation tables for specified outcome variables from model results, handling both binary and categorical exposure models.

Usage

margot_subset_model(
  model_results,
  outcome_vars = NULL,
  subset_condition = NULL,
  scale = "RD",
  contrast = NULL,
  debug = FALSE
)

Arguments

model_results

A list of model results from `model_causal_forest` or similar functions.

outcome_vars

Optional. A character vector of outcome variable names. If NULL, the function will use all models in the input.

subset_condition

A logical vector indicating the subset of data to use. Default is NULL.

scale

A character string indicating the scale to use. Default is "RD".

contrast

For categorical exposures, a single character string specifying the contrast to extract.

debug

Logical. If TRUE, print debug information. Default is FALSE.

Value

A data frame combining all custom evaluation tables for the specified outcomes and contrast.

Examples

if (FALSE) { # \dontrun{
# Example for binary exposure
model_cat_subset_pols_binary <- margot_subset_model(
  model_results = models_binary,
  outcome_vars = t2_outcome_vars_z,
  subset_condition = subset_condition_pols,
  scale = "RD",
  debug = FALSE
)

# Example for categorical exposure
model_cat_subset_pols_categorical <- margot_subset_model(
  model_results = models_cat,
  outcome_vars = t2_outcome_vars_z,
  subset_condition = subset_condition_pols,
  scale = "RD",
  contrast = "[6.0,7.0] - [1.0,5.0)",
  debug = TRUE
)
} # }