# pimamh {mcsm}

### Description

This function implements a Langevin version of the Metropolis-Hastings algorithm on the posterior of a probit model, applied to the `Pima.tr`

dataset.

### Usage

pimamh(Niter = 10^4, scale = 0.01)

### Arguments

- Niter
- Number of MCMC iterations
- scale
- Scale of the Gaussian noise in the MCMC proposal

### Values

The function produces an `image`

plot of the log-posterior, along with the simulated values of the parameters represented as dots.

### Warning

This function is fragile since, as described in the book, too large a value of `scale`

may induce divergent behaviour and crashes with error messages

Error in if (log(runif(1)) > like(prop[1], prop[2]) - likecur - sum(dnorm(prop,..))) : missing value where TRUE/FALSE needed

### References

Chapter 6 of **EnteR Monte Carlo Statistical Methods**

### See Also

Pima.tr,pimax

### Examples

`## Not run:pimamh(10^4,scale=.01)## End(Not run)`

Documentation reproduced from package mcsm, version 1.0. License: GPL (>= 2)