extract_model_components: Extract Model Components for Selection Models

In ssmodels: Sample Selection Models

Extract Model Components for Selection Models

Description

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.

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.

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.

ssmodels documentation built on June 8, 2025, 10:49 a.m.