Returns the names of the predictor variables for the
different parts of a model (like fixed or random effects, zero-inflated
component, ...). Unlike `find_parameters()`

, the names from
`find_predictors()`

match the original variable names from the data
that was used to fit the model.

```
find_predictors(x, ...)
# S3 method for default
find_predictors(
x,
effects = c("fixed", "random", "all"),
component = c("all", "conditional", "zi", "zero_inflated", "dispersion",
"instruments", "correlation", "smooth_terms"),
flatten = FALSE,
verbose = TRUE,
...
)
```

## Arguments

x |
A fitted model. |

... |
Currently not used. |

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

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. |

flatten |
Logical, if `TRUE` , the values are returned
as character vector, not as list. Duplicated values are removed. |

verbose |
Toggle warnings. |

## Value

A list of character vectors that represent the name(s) of the
predictor variables. Depending on the combination of the arguments
`effects`

and `component`

, the returned list has following
elements:

`conditional`

, the "fixed effects" terms from the model

`random`

, the "random effects" terms from the model

`zero_inflated`

, the "fixed effects" terms from the zero-inflation component of the model

`zero_inflated_random`

, the "random effects" terms from the zero-inflation component of the model

`dispersion`

, the dispersion terms

`instruments`

, for fixed-effects regressions like `ivreg`

, `felm`

or `plm`

, the instrumental variables

`correlation`

, for models with correlation-component like `gls`

, the variables used to describe the correlation structure

## Examples

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