R/tbl_uvregression.R
tbl_uvregression.Rd
This function estimates univariate regression models and returns them in a publicationready table. It can create univariate regression models holding either a covariate or outcome constant.
For models holding outcome constant, the function takes as arguments a data frame,
the type of regression model, and the outcome variable y=
. Each column in the
data frame is regressed on the specified outcome. The tbl_uvregression
function arguments are similar to the tbl_regression arguments. Review the
tbl_uvregression vignette
for detailed examples.
You may alternatively hold a single covariate constant. For this, pass a data
frame, the type of regression model, and a single
covariate in the x=
argument. Each column of the data frame will serve as
the outcome in a univariate regression model. Take care using the x
argument
that each of the columns in the data frame are appropriate for the same type
of model, e.g. they are all continuous variables appropriate for lm, or
dichotomous variables appropriate for logistic regression with glm.
tbl_uvregression( data, method, y = NULL, x = NULL, method.args = NULL, exponentiate = FALSE, label = NULL, include = everything(), tidy_fun = NULL, hide_n = FALSE, show_single_row = NULL, conf.level = NULL, estimate_fun = NULL, pvalue_fun = NULL, formula = "{y} ~ {x}", add_estimate_to_reference_rows = NULL, show_yesno = NULL, exclude = NULL )
data  Data frame to be used in univariate regression modeling. Data frame includes the outcome variable(s) and the independent variables. 

method  Regression method (e.g. lm, glm, survival::coxph, and more). 
y  Model outcome (e.g. 
x  Model covariate (e.g. 
method.args  List of additional arguments passed on to the regression
function defined by 
exponentiate  Logical indicating whether to exponentiate the
coefficient estimates. Default is 
label  List of formulas specifying variables labels,
e.g. 
include  Variables to include in output. Input may be a vector of
quoted variable names, unquoted variable names, or tidyselect select helper
functions. Default is 
tidy_fun  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 
hide_n  Hide N column. Default is 
show_single_row  By default categorical variables are printed on multiple rows. If a variable is dichotomous (e.g. Yes/No) and you wish to print the regression coefficient on a single row, include the variable name(s) herequoted and unquoted variable name accepted. 
conf.level  Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval. 
estimate_fun  Function to round and format coefficient estimates. Default is style_sigfig when the coefficients are not transformed, and style_ratio when the coefficients have been exponentiated. 
pvalue_fun  Function to round and format pvalues.
Default is style_pvalue.
The function must have a numeric vector input (the numeric, exact pvalue),
and return a string that is the rounded/formatted pvalue (e.g.

formula  String of the model formula.
Uses glue::glue syntax. Default is 
add_estimate_to_reference_rows  add a reference value. Default is FALSE 
show_yesno  DEPRECATED 
exclude  DEPRECATED 
A tbl_uvregression
object
Example 1
Example 2
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.
The N reported in the output is the number of observations
in the data frame model.frame(x)
. Depending on the model input, this N
may represent different quantities. In most cases, it is the number of people or
units in your model. Here are some common exceptions.
Survival regression models including time dependent covariates.
Random or mixedeffects regression models with clustered data.
GEE regression models with clustered data.
This list is not exhaustive, and care should be taken for each number reported.
See tbl_regression vignette for detailed examples
Other tbl_uvregression tools:
add_global_p()
,
add_nevent.tbl_uvregression()
,
add_q()
,
bold_italicize_labels_levels
,
inline_text.tbl_uvregression()
,
modify
,
tbl_merge()
,
tbl_stack()
Daniel D. Sjoberg
# Example 1  tbl_uv_ex1 < tbl_uvregression( trial[c("response", "age", "grade")], method = glm, y = response, method.args = list(family = binomial), exponentiate = TRUE ) # Example 2  # rounding pvalues to 2 decimal places library(survival) tbl_uv_ex2 < tbl_uvregression( trial[c("ttdeath", "death", "age", "grade", "response")], method = coxph, y = Surv(ttdeath, death), exponentiate = TRUE, pvalue_fun = function(x) style_pvalue(x, digits = 2) )