Bayesian kernel machine regression (from the 'bkmr'
package) is a Bayesian semi-parametric generalized linear model
approach under identity and probit links. There are a number of
functions in this package that extend Bayesian kernel machine
regression fits to allow multiple-chain inference and diagnostics,
which leverage functions from the 'future', 'rstan', and 'coda'
packages.  Reference: Bobb, J. F., Henn, B. C., Valeri, L., & Coull,
B. A. (2018). Statistical software for analyzing the health effects of
multiple concurrent exposures via Bayesian kernel machine regression.
; <doi:10.1186/s12940-018-0413-y>.

Parallel Chain Tools for Bayesian Kernel Machine Regression

package-ubuntu-20.04-r-4.0.5#cran#bkmrhat-1.0.2

<p>Posterior samples of E(Y|h(Z),X,beta) by chain
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p>bkmrfit.list object from <code><a href="kmbayes_parallel.html">kmbayes_parallel</a></code></p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>arguments to <code><a href="../../bkmr/html/CalcPIPs.html">CalcPIPs</a></code></p>
</td>
</tr>
</table>

<p>data.frame with all chains together
</p>

package-ubuntu-20.04-r-4.0.5#cran#bkmrhat-1.0.2#docs#SamplePred_parallel

`x`	bkmrfit.list object from `kmbayes_parallel`
`...`	arguments to `CalcPIPs`

SamplePred_parallel

Description

Usage

Arguments

Value

bkmrhat

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