This function estimates univariate regression models and returns them in a publication-ready 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.

  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}",
  show_yesno = NULL,
  exclude = NULL



Data frame to be used in univariate regression modeling. Data frame includes the outcome variable(s) and the independent variables.


Regression method (e.g. lm, glm, survival::coxph, and more).


Model outcome (e.g. y = recurrence or y = Surv(time, recur)). All other column in data will be regressed on y. Specify one and only one of y or x


Model covariate (e.g. x = trt). All other columns in data will serve as the outcome in a regression model with x as a covariate. Output table is best when x is a continuous or dichotomous variable displayed on a single row. Specify one and only one of y or x


List of additional arguments passed on to the regression function defined by method.


Logical indicating whether to exponentiate the coefficient estimates. Default is FALSE.


List of formulas specifying variables labels, e.g. list(age ~ "Age", stage ~ "Path T Stage")


Variables to include in output. Input may be a vector of quoted variable names, unquoted variable names, or tidyselect select helper functions. Default is everything().


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


Hide N column. Default is FALSE


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--quoted and unquoted variable name accepted.


Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.


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.


Function to round and format p-values. Default is style_pvalue. The function must have a numeric vector input (the numeric, exact p-value), and return a string that is the rounded/formatted p-value (e.g. pvalue_fun = function(x) style_pvalue(x, digits = 2) or equivalently, purrr::partial(style_pvalue, digits = 2)).


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)".






A tbl_uvregression object

Example Output

Example 1

Example 2

Setting Defaults

If you prefer to consistently use a different function to format p-values or estimates, you can set options in the script or in the user- or project-level startup file, '.Rprofile'. The default confidence level can also be set.


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.

  1. Survival regression models including time dependent covariates.

  2. Random- or mixed-effects regression models with clustered data.

  3. GEE regression models with clustered data.

This list is not exhaustive, and care should be taken for each number reported.

See also


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) )