This function extracts the different variance components of a mixed model and returns the result as a data frame.

## Arguments

- model
A mixed effects model (including

`stanreg`

models).- component
Should all parameters, parameters for the conditional model, for the zero-inflation part of the model, or the dispersion model be returned? Applies to models with zero-inflation and/or dispersion component.

`component`

may be one of`"conditional"`

,`"zi"`

,`"zero-inflated"`

,`"dispersion"`

or`"all"`

(default). May be abbreviated.

## Value

A data frame with random effects statistics for the variance components, including number of levels per random effect group, as well as complete observations in the model.

## Details

The variance components are obtained from `insight::get_variance()`

and
are denoted as following:

### Within-group (or residual) variance

The residual variance, σ^{2}_{ε},
is the sum of the distribution-specific variance and the variance due to additive dispersion.
It indicates the *within-group variance*.

### Between-group random intercept variance

The random intercept variance, or *between-group* variance
for the intercept (τ_{00}),
is obtained from `VarCorr()`

. It indicates how much groups
or subjects differ from each other.

### Between-group random slope variance

The random slope variance, or *between-group* variance
for the slopes (τ_{11})
is obtained from `VarCorr()`

. This measure is only available
for mixed models with random slopes. It indicates how much groups
or subjects differ from each other according to their slopes.

### Random slope-intercept correlation

The random slope-intercept correlation
(ρ_{01})
is obtained from `VarCorr()`

. This measure is only available
for mixed models with random intercepts and slopes.

**Note:** For the within-group and between-group variance, variance
and standard deviations (which are simply the square root of the variance)
are shown.

## Examples

```
if (require("lme4")) {
data(sleepstudy)
model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
random_parameters(model)
}
#> # Random Effects
#>
#> Within-Group Variance 654.94 (25.59)
#> Between-Group Variance
#> Random Intercept (Subject) 612.1 (24.74)
#> Random Slope (Subject.Days) 35.07 (5.92)
#> Correlations
#> Subject.Days 0.07
#> N (groups per factor)
#> Subject 18
#> Observations 180
```