This function estimates univariable regression models and returns them in a publication-ready table. It can create regression models holding either a covariate or an outcome constant.
tbl_uvregression(data, ...)
# S3 method for class 'data.frame'
tbl_uvregression(
data,
y = NULL,
x = NULL,
method,
method.args = list(),
exponentiate = FALSE,
label = NULL,
include = everything(),
tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
hide_n = FALSE,
show_single_row = NULL,
conf.level = 0.95,
estimate_fun = ifelse(exponentiate, label_style_ratio(), label_style_sigfig()),
pvalue_fun = label_style_pvalue(digits = 1),
formula = "{y} ~ {x}",
add_estimate_to_reference_rows = FALSE,
conf.int = TRUE,
...
)
# S3 method for class 'survey.design'
tbl_uvregression(
data,
y = NULL,
x = NULL,
method,
method.args = list(),
exponentiate = FALSE,
label = NULL,
include = everything(),
tidy_fun = broom.helpers::tidy_with_broom_or_parameters,
hide_n = FALSE,
show_single_row = NULL,
conf.level = 0.95,
estimate_fun = ifelse(exponentiate, label_style_ratio(), label_style_sigfig()),
pvalue_fun = label_style_pvalue(digits = 1),
formula = "{y} ~ {x}",
add_estimate_to_reference_rows = FALSE,
conf.int = TRUE,
...
)(data.frame, survey.design)
A data frame or a survey design object.
Additional arguments passed to broom.helpers::tidy_plus_plus().
(expression, string)
Model outcome (e.g. y=recurrence or y=Surv(time, recur)) or
covariate (e.g. x=trt.
All other column specified in include will be regressed against the constant y or x.
Specify one and only one of y or x.
(string/function)
Regression method or function, e.g. lm, glm, survival::coxph, survey::svyglm, etc.
Methods may be passed as functions (method=lm) or as strings (method='lm').
(named list)
Named list of arguments passed to method.
(scalar logical)
Logical indicating whether to exponentiate the coefficient estimates.
Default is FALSE.
(formula-list-selector)
Used to change variables labels, e.g. list(age = "Age", stage = "Path T Stage")
(tidy-select)
Variables to include in output. Default is everything().
(function)
Tidier function for the model. Default is to use broom::tidy().
If an error occurs, the tidying of the model is attempted with
parameters::model_parameters(), if installed.
(scalar logical)
Hide N column. Default is FALSE
(tidy-select)
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) here.
(scalar real)
Confidence level for confidence interval/credible interval. Defaults to 0.95.
(function)
Function to round and format coefficient estimates.
Default is label_style_sigfig() when the coefficients are not transformed, and
label_style_ratio() when the coefficients have been exponentiated.
(function)
Function to round and format p-values. Default is label_style_pvalue().
(string)
String of the model formula.
Uses glue::glue() syntax. Default is "{y} ~ {x}", where {y}
is the dependent variable, and {x} represents a single covariate. For a
random intercept model, the formula may be formula = "{y} ~ {x} + (1 | gear)".
(scalar logical)
Add a reference value. Default is FALSE.
(scalar logical)
Logical indicating whether or not to include a confidence
interval in the output. Default is TRUE.
A tbl_uvregression object
x and y argumentsFor 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.
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.
"parsnip/workflows": If the model was prepared using parsnip/workflows,
the original model fit is extracted and the original x= argument
is replaced with the model fit. This will typically go unnoticed; however,if you've
provided a custom tidier in tidy_fun= the tidier will be applied to the model
fit object and not the parsnip/workflows object.
"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)
"gam": Uses the internal tidier tidy_gam() to print both parametric and smooth terms.
"lmerMod", "glmerMod", "glmmTMB", "glmmadmb", "stanreg", "brmsfit": These mixed effects
models use broom.mixed::tidy(x, effects = "fixed"). Specify tidy_fun = broom.mixed::tidy
to print the random components.
See tbl_regression vignette for detailed examples
# Example 1 ----------------------------------
tbl_uvregression(
trial,
method = glm,
y = response,
method.args = list(family = binomial),
exponentiate = TRUE,
include = c("age", "grade")
)
Characteristic
N
OR
95% CI
p-value
Abbreviations: CI = Confidence Interval, OR = Odds Ratio
# Example 2 ----------------------------------
# rounding pvalues to 2 decimal places
library(survival)
tbl_uvregression(
trial,
method = coxph,
y = Surv(ttdeath, death),
exponentiate = TRUE,
include = c("age", "grade", "response"),
pvalue_fun = label_style_pvalue(digits = 2)
)
Characteristic
N
HR
95% CI
p-value
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio