# Posterior samples using a Gaussian approximation to the posterior distribution

Source:`R/samplers.R`

`mh_draws.Rd`

Posterior samples using a Gaussian approximation to the posterior distribution

## Usage

```
mh_draws(model, ...)
# S3 method for gam
mh_draws(
model,
n,
burnin = 1000,
thin = 1,
t_df = 40,
rw_scale = 0.25,
index = NULL,
...
)
```

## Arguments

- model
a fitted R model. Currently only models fitted by

`mgcv::gam()`

or`mgcv::bam()`

, or return an object that*inherits*from such objects are supported. Here, "inherits" is used in a loose fashion; models fitted by`scam::scam()`

are support even though those models don't strictly inherit from class`"gam"`

as far as`inherits()`

is concerned.- ...
arguments passed to methods.

- n
numeric; the number of posterior draws to take.

- burnin
numeric; the length of any initial burn in period to discard. See

`mgcv::gam.mh()`

.- thin
numeric; retain only

`thin`

samples. See`mgcv::gam.mh()`

.- t_df
numeric; degrees of freedom for static multivariate

*t*proposal. See`mgcv::gam.mh()`

.- rw_scale
numeric; factor by which to scale posterior covariance matrix when generating random walk proposals. See

`mgcv::gam.mh()`

.- index
numeric; vector of indices of coefficients to use. Can be used to subset the mean vector and covariance matrix extracted from

`model`

.