sample_lm_hs: Sample linear regression parameters assuming horseshoe prior

In countSTAR: Flexible Modeling of Count Data

Sample linear regression parameters assuming horseshoe prior

Description

Sample the parameters for a linear regression model assuming a horseshoe prior for the (non-intercept) coefficients. The number of predictors p may exceed the number of observations n.

Usage

sample_lm_hs(y, X, params, XtX = NULL, X_test = NULL)

Arguments

y	n x 1 vector of data
X	n x p matrix of predictors
params	the named list of parameters containing mu n x 1 vector of conditional means (fitted values) sigma the conditional standard deviation coefficients a named list of parameters that determine mu
XtX	the p x p matrix of crossprod(X) (one-time cost); if NULL, compute within the function
X_test	matrix of predictors at test points (default is NULL)

Value

The updated named list params with draws from the full conditional distributions of sigma and coefficients (along with updated mu and mu_test if applicable).

Note

The parameters in coefficients are:

• beta the p x 1 vector of regression coefficients

• sigma_beta p x 1 vector of regression coefficient standard deviations (local scale parameters)

• xi_sigma_beta p x 1 vector of parameter-expansion variables for sigma_beta

• lambda_beta the global scale parameter

• xi_lambda_beta parameter-expansion variable for lambda_beta components of beta

countSTAR documentation built on July 9, 2023, 5:12 p.m.