# Get model parameters from Bayesian models

Source:`R/get_parameters_bayesian.R`

`get_parameters.BGGM.Rd`

Returns the coefficients (or posterior samples for Bayesian models) from a model.

## Usage

```
# S3 method for BGGM
get_parameters(
x,
component = c("correlation", "conditional", "intercept", "all"),
summary = FALSE,
centrality = "mean",
...
)
# S3 method for MCMCglmm
get_parameters(
x,
effects = c("fixed", "random", "all"),
summary = FALSE,
centrality = "mean",
...
)
# S3 method for BFBayesFactor
get_parameters(
x,
effects = c("all", "fixed", "random"),
component = c("all", "extra"),
iterations = 4000,
progress = FALSE,
verbose = TRUE,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for stanmvreg
get_parameters(
x,
effects = c("fixed", "random", "all"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for brmsfit
get_parameters(
x,
effects = "fixed",
component = "all",
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for stanreg
get_parameters(
x,
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for bayesx
get_parameters(
x,
component = c("conditional", "smooth_terms", "all"),
summary = FALSE,
centrality = "mean",
...
)
# S3 method for bamlss
get_parameters(
x,
component = c("all", "conditional", "smooth_terms", "location", "distributional",
"auxiliary"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for sim.merMod
get_parameters(
x,
effects = c("fixed", "random", "all"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for sim
get_parameters(x, parameters = NULL, summary = FALSE, centrality = "mean", ...)
```

## Arguments

- x
A fitted model.

- component
Should all predictor variables, predictor variables for the conditional model, the zero-inflated part of the model, the dispersion term or the instrumental variables be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variable (so called fixed-effects regressions). May be abbreviated. Note that the

*conditional*component is also called*count*or*mean*component, depending on the model.- 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.- ...
Currently not used.

- effects
Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.

- iterations
Number of posterior draws.

- progress
Display progress.

- verbose
Toggle messages and warnings.

- parameters
Regular expression pattern that describes the parameters that should be returned.

## Value

The posterior samples from the requested parameters as data frame.
If `summary = TRUE`

, returns a data frame with two columns: the
parameter names and the related point estimates (based on `centrality`

).

## Details

In most cases when models either return different "effects" (fixed,
random) or "components" (conditional, zero-inflated, ...), the arguments
`effects`

and `component`

can be used.

## BFBayesFactor Models

Note that for `BFBayesFactor`

models (from the **BayesFactor** package),
posteriors are only extracted from the first numerator model (i.e.,
`model[1]`

). If you want to apply some function `foo()`

to another
model stored in the `BFBayesFactor`

object, index it directly, e.g.
`foo(model[2])`

, `foo(1/model[5])`

, etc.
See also `bayestestR::weighted_posteriors()`

.

## Examples

```
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
get_parameters(m)
#> Parameter Estimate
#> 1 (Intercept) 38.7460642
#> 2 wt -3.2463673
#> 3 cyl -1.3641033
#> 4 vs 0.5241721
```