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

glmmulti_boot(.data, dependent, explanatory, R = 1000)





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


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


Number of draws.


A multivariable glm fitted model with bootstrapped confidence intervals. Output is of class glmboot.


Uses glm with finalfit modelling conventions. boot::boot is used to draw bootstrapped confidence intervals on fixed effect model coefficients. Output can be passed to fit2df.

See also

fit2df, finalfit_merge

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


library(finalfit) library(dplyr) ## Note number of draws set to 100 just for speed in this example explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "mort_5yr" colon_s %>% glmmulti_boot(dependent, explanatory, R=100) %>% fit2df(estimate_suffix="(multivariable (BS CIs))")
#> explanatory OR(multivariable (BS CIs)) #> 1 age.factor40-59 years 0.57 (0.35-0.91, p<0.001) #> 2 age.factor60+ years 0.81 (0.44-1.31, p=0.520) #> 3 sex.factorMale 0.98 (0.73-1.32, p=0.880) #> 4 obstruct.factorYes 1.25 (0.88-1.78, p=0.120) #> 5 perfor.factorYes 1.12 (0.35-2.69, p=0.780)