This function retrieves variable labels from model terms. In case of categorical variables, where one variable has multiple dummies, variable name and category value is returned.

```
term_labels(
models,
mark.cat = FALSE,
case = NULL,
prefix = c("none", "varname", "label"),
...
)
get_term_labels(
models,
mark.cat = FALSE,
case = NULL,
prefix = c("none", "varname", "label"),
...
)
response_labels(models, case = NULL, multi.resp = FALSE, mv = FALSE, ...)
get_dv_labels(models, case = NULL, multi.resp = FALSE, mv = FALSE, ...)
```

- models
One or more fitted regression models. May also be glm's or mixed models.

- mark.cat
Logical, if

`TRUE`

, the returned vector has an attribute with logical values, which indicate whether a label indicates the value from a factor category (attribute value is`TRUE`

) or a term's variable labels (attribute value is`FALSE`

).- case
Desired target case. Labels will automatically converted into the specified character case. See

`to_any_case()`

for more details on this argument.- prefix
Indicates whether the value labels of categorical variables should be prefixed, e.g. with the variable name or variable label. May be abbreviated. See 'Examples',

- ...
Further arguments passed down to

`to_any_case()`

, like`preprocess`

or`postprocess`

.- mv, multi.resp
Logical, if

`TRUE`

and`models`

is a multivariate response model from a`brmsfit`

object, then the labels for each dependent variable (multiple responses) are returned.

For `term_labels()`

, a (named) character vector with
variable labels of all model terms, which can be used, for instance,
as axis labels to annotate plots.

For `response_labels()`

,
a character vector with variable labels from all dependent variables
of `models`

.

Typically, the variable labels from model terms are returned. However,
for categorical terms that have estimates for each category, the
value labels are returned as well. As the return value is a named
vector, you can easily use it with ggplot2's `scale_*()`

functions to annotate plots.

```
# use data set with labelled data
data(efc)
fit <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc)
term_labels(fit)
#> c160age
#> "carer' age"
#> c12hour
#> "average number of hours of care per week"
#> c161sex
#> "carer's gender"
#> c172code
#> "carer's level of education"
# make "education" categorical
if (require("sjmisc")) {
efc$c172code <- to_factor(efc$c172code)
fit <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc)
term_labels(fit)
# prefix value of categorical variables with variable name
term_labels(fit, prefix = "varname")
# prefix value of categorical variables with value label
term_labels(fit, prefix = "label")
# get label of dv
response_labels(fit)
}
#> [1] "Total score BARTHEL INDEX"
```