Returns a list with the names of all terms, including response
value and random effects, "as is". This means, on-the-fly tranformations
or arithmetic expressions like `log()`

, `I()`

, `as.factor()`

etc. are
preserved.

## Usage

```
find_terms(x, ...)
# S3 method for default
find_terms(x, flatten = FALSE, as_term_labels = FALSE, verbose = TRUE, ...)
```

## Arguments

- x
A fitted model.

- ...
Currently not used.

- flatten
Logical, if

`TRUE`

, the values are returned as character vector, not as list. Duplicated values are removed.- as_term_labels
Logical, if

`TRUE`

, extracts model formula and tries to access the`"term.labels"`

attribute. This should better mimic the`terms()`

behaviour even for those models that do not have such a method, but may be insufficient, e.g. for mixed models.- verbose
Toggle warnings.

## Value

A list with (depending on the model) following elements (character vectors):

`response`

, the name of the response variable`conditional`

, the names of the predictor variables from the*conditional*model (as opposed to the zero-inflated part of a model)`random`

, the names of the random effects (grouping factors)`zero_inflated`

, the names of the predictor variables from the*zero-inflated*part of the model`zero_inflated_random`

, the names of the random effects (grouping factors)`dispersion`

, the name of the dispersion terms`instruments`

, the names of instrumental variables

Returns `NULL`

if no terms could be found (for instance, due to
problems in accessing the formula).

## Note

The difference to `find_variables()`

is that `find_terms()`

may return a variable multiple times in case of multiple transformations
(see examples below), while `find_variables()`

returns each variable
name only once.

## Examples

```
data(sleepstudy, package = "lme4")
m <- suppressWarnings(lme4::lmer(
log(Reaction) ~ Days + I(Days^2) + (1 + Days + exp(Days) | Subject),
data = sleepstudy
))
find_terms(m)
#> $response
#> [1] "log(Reaction)"
#>
#> $conditional
#> [1] "Days" "I(Days^2)"
#>
#> $random
#> [1] "Days" "exp(Days)" "Subject"
#>
# sometimes, it is necessary to retrieve terms from "term.labels" attribute
m <- lm(mpg ~ hp * (am + cyl), data = mtcars)
find_terms(m, as_term_labels = TRUE)
#> $conditional
#> [1] "hp" "am" "cyl" "hp:am" "hp:cyl"
#>
```