init_lm_hs: Initialize linear regression parameters assuming a horseshoe...

In countSTAR: Flexible Modeling of Count Data

Initialize linear regression parameters assuming a horseshoe prior

Description

Initialize 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

init_lm_hs(y, X, X_test = NULL)

Arguments

y	n x 1 vector of data
X	n x p matrix of predictors
X_test	n0 x p matrix of predictors at test points (default is NULL)

Value

a named list params containing at least

1. mu: vector of conditional means (fitted values)

2. sigma: the conditional standard deviation

3. coefficients: a named list of parameters that determine mu

Additionally, if X_test is not NULL, then the list includes an element mu_test, the vector of conditional means at the test points

Note

The parameters in coefficients are:

• beta: the p x 1 vector of regression coefficients

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

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

• lambda_beta: the global scale parameter

• xi_lambda_beta: the parameter-expansion variable for lambda_beta components of beta

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