glmmixed {finalfit}

Mixed effects binomial logistic regression models: finalfit model wrapper

Description

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

Usage

glmmixed(.data, dependent, explanatory, random_effect, ...)

Arguments

.data

Dataframe.

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.

random_effect

Character vector of length 1, either, (1) name of random intercept variable, e.g. "var1", (automatically convered to "(1 | var1)"); or, (2) the full lme4 specification, e.g. "(var1 | var2)". Note parenthesis MUST be included in (2) but NOT included in (1).

...

Other arguments to pass to lme4::glmer.

Details

Uses lme4::glmer with finalfit modelling conventions. Output can be passed to fit2df. This is only currently set-up to take a single random effect as a random intercept. Can be updated in future to allow multiple random intercepts, random gradients and interactions on random effects if there is a need

Value

A list of multivariable lme4::glmer fitted model outputs. Output is of class glmerMod.

See Also

fit2df, finalfit_merge

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

Examples

library(finalfit)
library(dplyr)

explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
random_effect = "hospital"
dependent = "mort_5yr"

colon_s %>%
  glmmixed(dependent, explanatory, random_effect) %>%
	 fit2df(estimate_suffix=" (multilevel)")