Simulates continuous distributions of random vectors using
Markov chain Monte Carlo (MCMC). Users specify the distribution by an
R function that evaluates the log unnormalized density. Algorithms
are random walk Metropolis algorithm (function metrop), simulated
tempering (function temper), and morphometric random walk Metropolis
(Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>,
function morph.metrop),
which achieves geometric ergodicity by change of variable.

Markov Chain Monte Carlo

package-ubuntu-20.04-r-4.0.5#cran#mcmc-0.9-7

<dl>
<dt>x1</dt>
<dd>
quantitative predictor.
</dd>
<dt>x2</dt>
<dd>
quantitative predictor.
</dd>
<dt>x3</dt>
<dd>
quantitative predictor.
</dd>
<dt>y</dt>
<dd>
Bernoulli response.
</dd>
</dl>

library(mcmc)
data(foo)
out <- glm(y ~ x1 + x2 + x3, family = binomial, data = foo)
summary(out)

package-ubuntu-20.04-r-4.0.5#cran#mcmc-0.9-7#docs#foo

foo

Description

Usage

Format

Examples

mcmc

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