| Title: | Simple Metropolis-Hastings MCMC Algorithm |
| Version: | 0.1.1 |
| Description: | A very bare-bones interface to use the Metropolis-Hastings Monte Carlo Markov Chain algorithm. It is suitable for teaching and testing purposes. |
| Imports: | mvtnorm |
| Suggests: | coda, mockery, testthat (≥ 3.0.0), knitr, rmarkdown |
| License: | GPL-3 |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| Language: | en-GB |
| RoxygenNote: | 7.1.1.9001 |
| VignetteBuilder: | knitr |
| URL: | https://github.com/Bisaloo/simpleMH |
| BugReports: | https://github.com/Bisaloo/simpleMH/issues |
| NeedsCompilation: | no |
| Packaged: | 2024-02-06 18:00:49 UTC; hugo |
| Author: | Hugo Gruson  [aut,
cre, cph] |
| Maintainer: | Hugo Gruson <hugo.gruson+R@normalesup.org> |
| Repository: | CRAN |
| Date/Publication: | 2024-02-06 18:20:02 UTC |
Simple Metropolis-Hastings MCMC
Description
Simple Metropolis-Hastings MCMC
Usage
simpleMH(f, inits, theta.cov, max.iter, coda = FALSE, ...)
Arguments
f |
function that returns a single scalar value proportional to the log probability density to sample from. |
inits |
numeric vector with the initial values for the parameters to estimate |
theta.cov |
covariance matrix of the parameters to estimate. |
max.iter |
maximum number of function evaluations |
coda |
logical. Should the samples be returned as coda::mcmc
object? (defaults to |
... |
further arguments passed to |
Value
if
coda = FALSEa list with:-
samples: A two dimensional array of samples with dimensions
generationxparameter -
log.p: A numeric vector with the log density evaluate at each generation.
-
if
coda = TRUEa list with:-
samples: A object of class coda::mcmc containing all samples.
-
log.p: A numeric vector with the log density evaluate at each generation.
-
Examples
p.log <- function(x) {
B <- 0.03
return(-x[1]^2/200 - 1/2*(x[2]+B*x[1]^2-100*B)^2)
}
simpleMH(p.log, inits=c(0, 0), theta.cov = diag(2), max.iter=3000)