Extract Model Components for Selection Models — extract_model

This internal utility function extracts key components—such as model frames, matrices, and response variables—from formulas and a data set. It is used by models like HeckmanCL, HeckmanGe, HeckmanSK, HeckmanBS, and HeckmantS. Additionally, it can handle covariate matrices for modeling dispersion (sigma) and correlation (rho) structures.

Usage

extract_model_components(
  selection,
  outcome,
  data,
  outcomeS = NULL,
  outcomeC = NULL,
  drop.levels = TRUE
)

Arguments

selection: A formula for the selection equation.
outcome: A formula for the outcome equation.
data: A data frame containing all variables.
outcomeS: Optional formula or matrix for the dispersion model (sigma).
outcomeC: Optional formula or matrix for the correlation model (rho).
drop.levels: Logical. If TRUE, drops unused factor levels.

Value

A list with the following components:

XS: Model matrix for the selection equation.
YS: Response vector for the selection equation.
NXS: Number of covariates in the selection model.
XO: Model matrix for the outcome equation.
YO: Response vector for the outcome equation.
NXO: Number of covariates in the outcome model.
Msigma: Matrix for the dispersion model (or NULL if not provided).
NE: Number of covariates for the dispersion model (0 if not provided).
Mrho: Matrix for the correlation model (or NULL if not provided).
NV: Number of covariates for the correlation model (0 if not provided).
YSLevels: Factor levels of the binary selection response.

Details

If provided, outcomeS and outcomeC can be formulas or matrices for modeling dispersion and correlation structures, respectively. The function ensures that the selection equation response is binary.