summarize_beta

Given an array of posterior samples of coefficient function evaluations, returns the posterior mean and 95% credible interval for each evaluation.

Fits linear varying coefficient (VC) models, which assert a linear relationship between an outcome and several covariates but allow that relationship (i.e., the coefficients or slopes in the linear regression) to change as functions of additional variables known as effect modifiers, by approximating the coefficient functions with Bayesian Additive Regression Trees. Implements a Metropolis-within-Gibbs sampler to simulate draws from the posterior over coefficient function evaluations. VC models with independent observations or repeated observations can be fit. For more details see Deshpande et al. (2024) <doi:10.1214/24-BA1470>.

Sameer K. Deshpande

VCBART

Fit Varying Coefficient Models with Bayesian Additive Regression
Trees

Ray Bai

Cecilia Balocchi

Jennifer Starling

Jordan Weiss

summarize_beta function

<dl> <dt>beta_samples</dt>
<dd>An array, returned by <code>VCBART_ind</code>, <code>VCBART_cs</code>, or <code>predict_betas</code> of posterior samples of coefficient function evaluations</dd></dl>

Arguments

Compute posterior mean and 95% credible interval for evaluations of each coefficient function. — summarize_beta

<dl>

 <dt>beta_samples</dt>
<dd>An array, returned by <code>VCBART_ind</code>, <code>VCBART_cs</code>, or <code>predict_betas</code> of posterior samples of coefficient function evaluations</dd>

</dl>

summarize_beta: Compute posterior mean and 95% credible interval for evaluations of each coefficient function.

Description

Usage

Value

Arguments