glmuni: Binomial logistic regression univariable models: 'finalfit'...

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Binomial logistic regression univariable models: finalfit model wrapper

Description

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

Usage

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.

Details

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

Value

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

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)")

finalfit documentation built on Sept. 11, 2024, 9:01 p.m.