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, ...)
One or more fitted regression models. May also be glm's or mixed models.
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
).
Desired target case. Labels will automatically converted into the
specified character case. See to_any_case()
for
more details on this argument.
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
.
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"