MakeArrays: Arrays to save MCMC posterior samples.

In gpapadog/BAC: Bayesian Adjustment for Confounding

Description

Arrays to save MCMC posterior samples.

Usage

MakeArrays(
  chains,
  Nsims,
  num_conf,
  starting_alphas,
  starting_coefs,
  starting_vars,
  omega = 50000,
  mu_priorX,
  mu_priorY,
  Sigma_priorX,
  Sigma_priorY,
  alpha_priorX,
  alpha_priorY,
  beta_priorX,
  beta_priorY
)

Arguments

chains	The number of MCMC chains.
Nsims	The number of posterior samples per chain.
num_conf	The number of potential confounders.
starting_alphas	Array of dimensions: model (exposure or outcome), chains, potential confounders. Entries 0/1 represent exclusion/inclusion of a covariate in the model. If NULL, values are set from the prior.
starting_coefs	Array with the starting values of all coefficients. Dimensions are: Exposure/Outcome model, chains, and covariate (intercept, coefficient of exposure, covariates). The coefficient of exposure should be NA for the exposure model. If NULL, values are set from the normal with mean zero and variance that of the prior divided by 50 ^ 2.
starting_vars	Array including the starting values for the residual variances. Dimensions correspond to: Exposure/Outcome model, and chains. If NULL, values are set from an inverse gamma with parameters alpha and beta set to the prior values times 200.
omega	The omega of the BAC prior on inclusion indicators.
mu_priorX	The mean of the normal prior on the coefficients of the exposure model, where the first element corresponds to the intercept, and the remaining to the coefficients of the columns in D.
mu_priorY	The mean of the normal prior on the coefficients of the outcome model, where the first element corresponds to the intercept, the second to the exposure, and the remaining to the coefficients in from of the columns in D.
Sigma_priorX	The covariance matrix of the normal prior on the coefficients of the exposure model.
Sigma_priorY	The covariance matrix of the normal prior on the coefficients of the outcome model.
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.