Using finalfit conventions, produces a multivariable binomial logistic regression model for a set of explanatory variables against a binary dependent.

glmmulti(.data, dependent, explanatory, family = "binomial", ...)



Data frame.


Character vector of length 1: name of depdendent variable (must have 2 levels).


Character vector of any length: name(s) of explanatory variables.


Character vector quoted or unquoted of the error distribution and link function to be used in the model, see glm.


Other arguments to pass to glm.


A multivariable glm fitted model.


Uses glm with finalfit modelling conventions. Output can be passed to fit2df.

See also

fit2df, finalfit_merge

Other finalfit model wrappers: coxphmulti(), coxphuni(), crrmulti(), crruni(), glmmixed(), glmmulti_boot(), glmuni(), lmmixed(), lmmulti(), lmuni(), svyglmmulti(), svyglmuni()


library(finalfit) library(dplyr) explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "mort_5yr" colon_s %>% glmmulti(dependent, explanatory) %>% fit2df(estimate_suffix=" (multivariable)")
#> Waiting for profiling to be done...
#> explanatory OR (multivariable) #> 1 age.factor40-59 years 0.57 (0.34-0.98, p=0.041) #> 2 age.factor60+ years 0.81 (0.48-1.36, p=0.426) #> 3 sex.factorMale 0.98 (0.75-1.28, p=0.902) #> 4 obstruct.factorYes 1.25 (0.90-1.76, p=0.186) #> 5 perfor.factorYes 1.12 (0.51-2.44, p=0.770)