Binomial logistic regression univariable models: finalfit model wrapper

Using 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 = "", ...)

Arguments

.data

Data frame.

dependent

Character vector of length 1: 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.

weights

Character vector of length 1: name of variabe for weighting. 'Prior weights' to be used in the fitting process.

...

Other arguments to pass to glm.

Value

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

Details

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(), glmmulti(), 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 %>%
  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)