glmmulti_boot: Binomial logistic regression multivariable models with...

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Binomial logistic regression multivariable models with bootstrapped confidence intervals: finalfit model wrapper

Description

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

Usage

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

Arguments

.data	Dataframe.
dependent	Character vector length 1: name of depdendent variable (must have 2 levels).
explanatory	Character vector of any length: name(s) of explanatory variables.
R	Number of draws.

Details

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.

Value

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

See Also

fit2df, finalfit_merge

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

Examples

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))")

finalfit documentation built on Nov. 17, 2023, 1:09 a.m.