Returns the coefficients from a model.

## Usage

```
# S3 method for class 'glmm'
get_parameters(x, effects = c("all", "fixed", "random"), ...)
# S3 method for class 'coxme'
get_parameters(x, effects = c("fixed", "random"), ...)
# S3 method for class 'nlmerMod'
get_parameters(
x,
effects = c("fixed", "random"),
component = c("all", "conditional", "nonlinear"),
...
)
# S3 method for class 'merMod'
get_parameters(x, effects = c("fixed", "random"), ...)
# S3 method for class 'glmmTMB'
get_parameters(
x,
effects = c("fixed", "random"),
component = c("all", "conditional", "zi", "zero_inflated", "dispersion"),
...
)
# S3 method for class 'glimML'
get_parameters(x, effects = c("fixed", "random", "all"), ...)
```

## Arguments

- x
A fitted model.

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

- ...
Currently not used.

- component
Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model or the dispersion term? Applies to models with zero-inflated and/or dispersion formula. Note that the

*conditional*component is also called*count*or*mean*component, depending on the model. There are three convenient shortcuts:`component = "all"`

returns all possible parameters. If`component = "location"`

, location parameters such as`conditional`

or`zero_inflated`

are returned (everything that are fixed or random effects - depending on the`effects`

argument - but no auxiliary parameters). For`component = "distributional"`

(or`"auxiliary"`

), components like`sigma`

or`dispersion`

(and other auxiliary parameters) are returned.

## Value

If `effects = "fixed"`

, a data frame with two columns: the
parameter names and the related point estimates. If `effects = "random"`

, a list of data frames with the random effects (as returned by
`ranef()`

), unless the random effects have the same simplified
structure as fixed effects (e.g. for models from **MCMCglmm**).

## 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.

## 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
```