# Find names of model parameters from mixed models

Source:`R/find_parameters_mixed.R`

`find_parameters.glmmTMB.Rd`

Returns the names of model parameters, like they typically
appear in the `summary()`

output.

## Usage

```
# S3 method for glmmTMB
find_parameters(
x,
effects = c("all", "fixed", "random"),
component = c("all", "conditional", "zi", "zero_inflated", "dispersion"),
flatten = FALSE,
...
)
# S3 method for merMod
find_parameters(x, effects = c("all", "fixed", "random"), flatten = FALSE, ...)
```

## 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.

- 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.- flatten
Logical, if

`TRUE`

, the values are returned as character vector, not as list. Duplicated values are removed.- ...
Currently not used.

## Value

A list of parameter names. The returned list may have following elements:

`conditional`

, the "fixed effects" part from the model.`random`

, the "random effects" part from the model.`zero_inflated`

, the "fixed effects" part from the zero-inflation component of the model.`zero_inflated_random`

, the "random effects" part from the zero-inflation component of the model.`dispersion`

, the dispersion parameters (auxiliary parameter)

## Examples

```
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_parameters(m)
#> $conditional
#> [1] "(Intercept)" "wt" "cyl" "vs"
#>
```