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.
Usage
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, ...)Arguments
- 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 isTRUE) or a term's variable labels (attribute value isFALSE).- 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(), likepreprocessorpostprocess.- mv, multi.resp
Logical, if
TRUEandmodelsis a multivariate response model from abrmsfitobject, then the labels for each dependent variable (multiple responses) are returned.
Value
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.
Details
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.
Examples
# 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)
}
#> Loading required package: sjmisc
#> Warning: there is no package called ‘sjmisc’
