Vetted tidy models
Below is a list of models vetted for use
in tbl_regression and tbl_uvregression. If a model is passed to these
functions and the model is not listed below and a tidy() function is
not specified in the tidy_fun= argument, the model object will be passed
to broom::tidy or broom.mixed::tidy.
lme4::glmer
lme4::lmer
geepack::geeglm
If broom::tidy or broom.mixed::tidy supports a class of model not listed
above, please submit a GitHub Issue.
The model can be added to the list of vetted models. Unit tests will be
put in place to ensure continued support for the model.
In some cases you may need to use a custom function to tidy regression model
results. A user may pass a custom tidier function to tbl_regression and
tbl_uvregression in the tidy_fun= argument. The function passed, however,
must be a proper tidier. For example, it must return a tibble that is one
line per variable in the regression model; it must accept arguments
exponentiate=, conf.level=, and conf.int= since these arguments will
be passed to the function internally (we recommend including ... to
capture any arguments you may not need). See below for an example where the
confidence limits for a linear regression model are calculated using Wald's method.
my_tidy <- function(x, exponentiate = FALSE, conf.level = 0.95, ...) {
tidy <-
dplyr::bind_cols(
broom::tidy(x, conf.int = FALSE),
# calculate the confidence intervals, and save them in a tibble
stats::confint.default(x) %>%
tibble::as_tibble() %>%
rlang::set_names(c("conf.low", "conf.high"))
)
# exponentiating, if requested
if (exponentiate == TRUE) {
tidy <- dplyr::mutate_at(vars(estimate, conf.low, conf.high), exp)
}
tidy
}
lm(age ~ grade + response, trial) %>%
my_tidy()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.