UpdateVariances: MCMC update of the residual variance.

In gpapadog/BAC: Bayesian Adjustment for Confounding

Description

MCMC update of the residual variance.

Usage

UpdateVariances(
  X,
  Y,
  D,
  current_alphas,
  current_coefs,
  alpha_priorX,
  alpha_priorY,
  beta_priorX,
  beta_priorY
)

Arguments

X	Vector of the treatment
Y	Vector of the outcome.
D	Matrix or data frame where the columns correspond to all possible predictors of Y, and the rows correspond to the units.
current_alphas	Matrix of inclusion indicators with rows corresponding to exposure and outcome models and columns to covariates.
current_coefs	Matrix of coefficients with rows corresponding to exposure and outcome models and columns to intercept, exposure and covariates.
alpha_priorX	The value of alpha in the inverse gamma prior for the residual variance of the exposure model.
alpha_priorY	The value of alpha in the inverse gamma prior for the residual variance of the outcome model.
beta_priorX	The value of beta in the inverse gamma prior for the residual variance of the exposure model.
beta_priorY	The value of beta in the inverse gamma prior for the residual variance of the outcome model.

gpapadog/BAC documentation built on Feb. 15, 2021, 6:37 a.m.