BSTN_SAS | R Documentation |
Fit Bayesian Skewed Tensor Normal (BSTN) model with tensor spike-and-slab prior to GAAD data Lee et al. (2021+) Bayesian Regression Analysis of Skewed Tensor Response
BSTN_SAS(Y, X, vecy, n.burn = 10, n.save = 100, thin = 1)
Y |
t x s x b x n array of skewed tensor response |
X |
p x n matrix of covariates |
vecy |
tsb x n matrix: vectorized tensor |
n.burn |
burn-in period |
n.save |
number of posterior samples |
thin |
thinning size |
Returns a list with the following components: rho: posterior samples of correlation of each mode of tensor response (3 x n.save) \item sigma.sq: posterior samples of variance parameter (b x n.save) \item lam.est: posterior samples of skewness parameters (b x n.save) \item est.est: posterior samples of common effects of covariates (p x n.save) \item omega: posterior samples of (zeros/ones) that particular element is included in the gamma.est (t x s x b x p x n.save) \item gamma.est: posterior samples of sparsity elements that identify different effects of each tooth-sites (t x s x b x p x n.save)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.