postprocess_theta: Post-process Parameter Vector for Generalized Heckman Models

In ssmodels: Sample Selection Models

Post-process Parameter Vector for Generalized Heckman Models

Description

Internal helper function that assigns meaningful names to a vector of estimated parameters and applies transformations to sigma and rho parameters to obtain interpretable estimates.

Usage

postprocess_theta(theta_par, NXS, NXO, NE, NV, XS, XO, outcomeS, outcomeC)

Arguments

theta_par	A numeric vector containing the estimated parameters.
NXS	Integer. Number of covariates in the selection equation.
NXO	Integer. Number of covariates in the outcome equation.
NE	Integer. Number of covariates (or 1 for intercept-only) in the scale model.
NV	Integer. Number of covariates (or 1 for intercept-only) in the correlation model.
XS	Design matrix for the selection equation (used for naming).
XO	Design matrix for the outcome equation (used for naming).
outcomeS	Design matrix or variable for the scale (variance) model.
outcomeC	Design matrix or variable for the correlation model.

Details

The parameter vector theta_par is structured as follows:

• The first NXS elements are coefficients for the selection equation.

• The next NXO elements are coefficients for the outcome equation.

• The next NE elements are coefficients (or log-sigma if NE == 1) for the scale model.

• The next NV elements are coefficients (or atanh(rho) if NV == 1) for the correlation model.

For interpretation, the function applies:

• exp() transformation for sigma if NE == 1.

• tanh() transformation for rho if NV == 1.

Value

A named numeric vector with:

• Transformed sigma and rho values (if needed),

• Meaningful names assigned to all parameters.

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