Binomial logistic regression multivariable models: finalfit model wrapper

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

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

Arguments

.data	Data frame.
dependent	Character vector usually of length 1, but can take more than 1 dependent: name of depdendent variable (must have 2 levels).
explanatory	Character vector of any length: name(s) of explanatory variables.
family	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.

Value

A list of multivariable glm fitted model outputs. Output is of class glmlist.

Details

Uses glm with finalfit modelling conventions. Output can be passed to fit2df. Note that this function can take multiple dependent variables as well, but performs multiple individual models, not a multivariate analysis.

See also

fit2df, finalfit_merge

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

Examples

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=" (univariable)")
#> Waiting for profiling to be done...
#>             explanatory          OR (univariable)
#> 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)