# Low-level Functions to generate draws from the posterior distribution of model coefficients

Source:`R/samplers.R`

`post_draws.Rd`

Low-level Functions to generate draws from the posterior distribution of model coefficients

Generate posterior draws from a fitted model

## Usage

```
post_draws(model, ...)
# S3 method for default
post_draws(
model,
n,
method = c("gaussian", "mh", "inla", "user"),
mu = NULL,
sigma = NULL,
n_cores = 1L,
burnin = 1000,
thin = 1,
t_df = 40,
rw_scale = 0.25,
index = NULL,
frequentist = FALSE,
unconditional = FALSE,
parametrized = TRUE,
mvn_method = c("mvnfast", "mgcv"),
draws = NULL,
seed = NULL,
...
)
generate_draws(model, ...)
# S3 method for gam
generate_draws(
model,
n,
method = c("gaussian", "mh", "inla"),
mu = NULL,
sigma = NULL,
n_cores = 1L,
burnin = 1000,
thin = 1,
t_df = 40,
rw_scale = 0.25,
index = NULL,
frequentist = FALSE,
unconditional = FALSE,
mvn_method = c("mvnfast", "mgcv"),
seed = 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.

- method
character; which algorithm to use to sample from the posterior. Currently implemented methods are:

`"gaussian"`

and`"mh"`

.`"gaussian"`

calls`gaussian_draws()`

which uses a Gaussian approximation to the posterior distribution.`"mh"`

uses a simple Metropolis Hasting sampler which alternates static proposals based on a Gaussian approximation to the posterior, with random walk proposals. Note, setting`t_df`

to a low value will result in heavier-tailed statistic proposals. See`mgcv::gam.mh()`

for more details.- mu
numeric; user-supplied mean vector (vector of model coefficients). Currently ignored.

- sigma
matrix; user-supplied covariance matrix for

`mu`

. Currently ignored.- n_cores
integer; number of CPU cores to use when generating multivariate normal distributed random values. Only used if

`mvn_method = "mvnfast"`

**and**`method = "gaussian"`

.- 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`

.- frequentist
logical; if

`TRUE`

, the frequentist covariance matrix of the parameter estimates is used. If`FALSE`

, the Bayesian posterior covariance matrix of the parameters is used. See`mgcv::vcov.gam()`

.- unconditional
logical; if

`TRUE`

the Bayesian smoothing parameter uncertainty corrected covariance matrix is used,*if available*for`model`

. See`mgcv::vcov.gam()`

.- parametrized
logical; use parametrized coefficients and covariance matrix, which respect the linear inequality constraints of the model. Only for

`scam::scam()`

model fits.- mvn_method
character; one of

`"mvnfast"`

or`"mgcv"`

. The default is uses`mvnfast::rmvn()`

, which can be considerably faster at generate large numbers of MVN random values than`mgcv::rmvn()`

, but which might not work for some marginal fits, such as those where the covariance matrix is close to singular.- draws
matrix; user supplied posterior draws to be used when

`method = "user"`

.- seed
numeric; the random seed to use. If

`NULL`

, a random seed will be generated without affecting the current state of R's RNG.