finalfit conventions, produces multiple univariable binomial logistic
regression models for a set of explanatory variables against a binary dependent.
glmuni(.data, dependent, explanatory, family = "binomial", weights = "", ...)
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
Character vector of length 1: name of variabe for weighting. 'Prior weights' to be used in the fitting process.
Other arguments to pass to
A list of univariable
glm fitted model outputs.
Output is of class
library(finalfit) library(dplyr) explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "mort_5yr" colon_s %>% glmuni(dependent, explanatory) %>% fit2df(estimate_suffix=" (univariable)") #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> Waiting for profiling to be done... #> explanatory OR (univariable) #> 1 age.factor40-59 years 0.54 (0.32-0.92, p=0.023) #> 2 age.factor60+ years 0.75 (0.45-1.25, p=0.270) #> 3 sex.factorMale 0.98 (0.76-1.27, p=0.889) #> 4 obstruct.factorYes 1.25 (0.90-1.74, p=0.189) #> 5 perfor.factorYes 1.18 (0.54-2.55, p=0.672)