Most regression models are handled by tbl_regression.default()
,
which uses broom::tidy()
to perform initial tidying of results. There are,
however, some model types that have modified default printing behavior.
Those methods are listed below.
# S3 method for survreg
tbl_regression(
x,
tidy_fun = function(x, ...) broom::tidy(x, ...) %>% dplyr::filter(.data$term !=
"Log(scale)"),
...
)# S3 method for mira
tbl_regression(x, tidy_fun = pool_and_tidy_mice, ...)
# S3 method for mipo
tbl_regression(x, ...)
# S3 method for lmerMod
tbl_regression(
x,
tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
...
)
# S3 method for glmerMod
tbl_regression(
x,
tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
...
)
# S3 method for glmmTMB
tbl_regression(
x,
tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
...
)
# S3 method for glmmadmb
tbl_regression(
x,
tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
...
)
# S3 method for stanreg
tbl_regression(
x,
tidy_fun = function(x, ...) broom.mixed::tidy(x, ..., effects = "fixed"),
...
)
# S3 method for multinom
tbl_regression(x, ...)
Regression model object
Option to specify a particular tidier function if the
model is not a vetted model or you need to implement a
custom method. Default is NULL
arguments passed to tbl_regression.default()
The default method for tbl_regression()
model summary uses broom::tidy(x)
to perform the initial tidying of the model object. There are, however,
a few models that use modifications.
"survreg"
: The scale parameter is removed, broom::tidy(x) %>% dplyr::filter(term != "Log(scale)")
"multinom"
: This multinomial outcome is complex, with one line per covariate per outcome (less the reference group)
"lmerMod"
, "glmerMod"
, "glmmTMB"
, "glmmadmb"
, "stanreg"
: These mixed effects
models use broom.mixed::tidy(x, effects = "fixed")
. Specify tidy_fun = broom.mixed::tidy
to print the random components.