Returns the requested auxiliary parameters from models, like dispersion, sigma, or beta...

## Arguments

- x
A model.

- type
The name of the auxiliary parameter that should be retrieved.

`"sigma"`

is available for most models,`"dispersion"`

for models of class`glm`

,`glmerMod`

or`glmmTMB`

as well as`brmsfit`

.`"beta"`

and other parameters are currently only returned for`brmsfit`

models. See 'Details'.- summary
Logical, indicates whether the full posterior samples (

`summary = FALSE`

)) or the summarized centrality indices of the posterior samples (`summary = TRUE`

)) should be returned as estimates.- centrality
Only for models with posterior samples, and when

`summary = TRUE`

. In this case,`centrality = "mean"`

would calculate means of posterior samples for each parameter, while`centrality = "median"`

would use the more robust median value as measure of central tendency.- verbose
Toggle warnings.

- ...
Currently not used.

## Details

Currently, only sigma and the dispersion parameter are returned, and only for a limited set of models.

### Sigma Parameter

See `get_sigma()`

.

### Dispersion Parameter

There are many different definitions of "dispersion", depending on the context.
`get_auxiliary()`

returns the dispersion parameters that usually can
be considered as variance-to-mean ratio for generalized (linear) mixed
models. Exceptions are models of class `glmmTMB`

, where the dispersion
equals σ^{2}.
In detail, the computation of the dispersion parameter for generalized linear
models is the ratio of the sum of the squared working-residuals and the
residual degrees of freedom. For mixed models of class `glmer`

, the
dispersion parameter is also called φ
and is the ratio of the sum of the squared Pearson-residuals and the residual
degrees of freedom. For models of class `glmmTMB`

, dispersion is
σ^{2}.

### brms models

For models of class `brmsfit`

, there are different options for the
`type`

argument. See a list of supported auxiliary parameters here:
`find_parameters.BGGM()`

.

## Examples

```
# from ?glm
clotting <- data.frame(
u = c(5, 10, 15, 20, 30, 40, 60, 80, 100),
lot1 = c(118, 58, 42, 35, 27, 25, 21, 19, 18),
lot2 = c(69, 35, 26, 21, 18, 16, 13, 12, 12)
)
model <- glm(lot1 ~ log(u), data = clotting, family = Gamma())
get_auxiliary(model, type = "dispersion") # same as summary(model)$dispersion
#> [1] 0.002446059
```