Turn a regression model object into a markdown-ready tibble.

This function uses broom::tidy from the broom or broom.mixed packages to perform the initial model formatting. Review the fmt_regression vignette for detailed examples.

fmt_regression(x, exponentiate = FALSE, label = NULL,
  include = names(stats::model.frame(x)), show_yesno = NULL,
  conf.level = 0.95, intercept = FALSE, beta_fun = fmt_beta,
  pvalue_fun = fmt_pvalue)

Arguments

x	regression model object
exponentiate	logical argument passed directly to tidy function Default is FALSE
label	list of labels to write in the output. list(age60 = "Age > 60")
include	names of variables to include in output. Default is all variables.
show_yesno	Vector of names of categorical and factor variables that are c("No", "Yes"), c("no", "yes"), or c("NO", "YES") default to dichotomous printing (i.e. only Yes shown). To force both levels to be shown include the column name in show_yesno, e.g. show_yesno = c("highgrade", "female")
conf.level	confidence level passed directly to tidy function. Default is 0.95.
intercept	logical argument indicates whether to include the intercept in the output. Default is FALSE
beta_fun	function to round and format beta coefficients. Default is fmt_beta
pvalue_fun	function to round and format p-values. Default is fmt_pvalue

Examples

mod1 <- lm(hp ~ mpg + factor(cyl), mtcars)
fmt_regression(mod1)
#> 
#> N = 32      Coefficient 95% CI      p-value    
#> mpg         -3.33       -7.70, 1.04 0.13       
#> factor(cyl)                                    
#> 4           Ref.                               
#> 6           16.6        -30.9, 64.1 0.5        
#> 8           88.1        29.1, 147   0.005      

mod2 <- glm(response ~ age + grade + stage, trial, family = binomial(link = "logit"))
fmt_regression(mod2, exponentiate = TRUE)
#> 
#> N = 183    OR         95% CI     p-value   
#> Age, yrs   1.00       0.98, 1.02 0.9       
#> Grade                                      
#> I          Ref.                            
#> II         0.55       0.25, 1.19 0.13      
#> III        0.79       0.38, 1.64 0.5       
#> T Stage                                    
#> T1         Ref.                            
#> T2         1.49       0.64, 3.49 0.4       
#> T3         0.65       0.26, 1.58 0.3       
#> T4         0.51       0.22, 1.14 0.10      

library(lme4)
#> Loading required package: Matrix
mod_glmer <- glmer(am ~ hp + (1 | gear), mtcars, family = binomial)
fmt_regression(mod_glmer, exponentiate = TRUE)
#> 
#> N = 32           exp(Coefficient) 95% CI           p-value         
#> hp               0.99             0.95, 1.03       0.5