UpdateVariances: Exposure and outcome model residual variances

In gpapadog/LERCA: Local Exposure-Response Confounding Adjustment

Description

Updating the residual variance of the exposure and outcome models within each experiment.

Usage

UpdateVariances(dta, current_cutoffs, current_coefs, cov_cols,
  alpha_priorX, beta_priorX, alpha_priorY, beta_priorY)

Arguments

dta	A data set including a column of the exposure of interest as X, the outcome of interest as Y, and all potential confounders as C1, C2, ...
current_cutoffs	Numeric of length K. The current values for the points in the experiment configuraiton.
current_coefs	The current coefficients of the MCMC. Three dimensional array with dimensions corresponding to exposure/outcome model, experiment, and coefficients (intercept, slope, covariates).
cov_cols	The indices of the columns in dta corresponding to the potential confounders.
alpha_priorX	The shape parameter of the inverse gamma prior on the residual variance of the exposure model.
beta_priorX	The rate parameter of the inverse gamma prior on the residual variance of the exposure model.
alpha_priorY	The shape parameter of the inverse gamma prior on the residual variance of the outcome model.
beta_priorY	The rate parameter of the inverse gamma prior on the residual variance of the outcome model.

Value

Matrix with rows corresponding to the exposure/outcome model and columns corresponding to the experiment.

gpapadog/LERCA documentation built on June 4, 2019, 11:40 a.m.