dlmtree.control.mcmc

MCMC control settings for dlmtree model fitting

Estimation of distributed lag models (DLMs) based on a Bayesian additive regression trees framework. Includes several extensions of DLMs: treed DLMs and distributed lag mixture models (Mork and Wilson, 2023) <doi:10.1111/biom.13568>; treed distributed lag nonlinear models (Mork and Wilson, 2022) <doi:10.1093/biostatistics/kxaa051>; heterogeneous DLMs (Mork, et. al., 2024) <doi:10.1080/01621459.2023.2258595>; monotone DLMs (Mork and Wilson, 2024) <doi:10.1214/23-BA1412>. The package also includes visualization tools and a 'shiny' interface to check model convergence and to help interpret results.

Daniel Mork

dlmtree

Bayesian Treed Distributed Lag Models

Seongwon Im

Ander Wilson

dlmtree.control.mcmc function

<dl><dt>n.trees</dt>
<dd>integer for number of trees in ensemble.</dd>
<dt>n.burn</dt>
<dd>integer for length of MCMC burn-in.</dd>
<dt>n.iter</dt>
<dd>integer for number of MCMC iterations to run model after burn-in.</dd>
<dt>n.thin</dt>
<dd>integer MCMC thinning factor, i.e. keep every tenth iteration.</dd></dl>

Arguments

MCMC control settings for dlmtree model fitting — dlmtree.control.mcmc

<dl>

<dt>n.trees</dt>
<dd>integer for number of trees in ensemble.</dd>


<dt>n.burn</dt>
<dd>integer for length of MCMC burn-in.</dd>


<dt>n.iter</dt>
<dd>integer for number of MCMC iterations to run model after burn-in.</dd>


<dt>n.thin</dt>
<dd>integer MCMC thinning factor, i.e. keep every tenth iteration.</dd>

</dl>

dlmtree.control.mcmc: MCMC control settings for dlmtree model fitting

Description

Usage

Value

Arguments