UpdateIntSlope: Intercepts and coefficients of exposure

In gpapadog/LERCA: Local Exposure-Response Confounding Adjustment

Description

Updating the outcome model intercepts and coefficients for the exposure.

Usage

UpdateIntSlope(dta, cov_cols, current_cutoffs, current_coefs, current_vars,
  mu_priorY, Sigma_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, ...
cov_cols	The indices of the columns in dta corresponding to the potential confounders.
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).
current_vars	Matrix. Rows correspond to exposure/outcome model, and columns to experiments. Entries are the current variances.
mu_priorY	The mean of the normal prior on the coefficients of the outcome model. Numeric vector with entries corresponding to intercept, slope of exposure, and potential covariates. If left NULL, it is set to 0 for all parameters.
Sigma_priorY	The covariance matrix of the normal prior on the regression coefficients of the outcome model. If left NULL, it is set to diagonal with entries 100 ^ 2 for all parameters.

Value

A matrix with dimensions that correspond to the exposure/outcome model, and the intercept/slope coefficient.

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