lmmixed: Mixed effects linear regression models: 'finalfit' model...

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Mixed effects linear regression models: finalfit model wrapper

Description

Using finalfit conventions, produces mixed effects linear regression models for a set of explanatory variables against a continuous dependent.

Usage

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

Arguments

.data	Dataframe.
dependent	Character vector of length 1, name of depdendent variable (must be continuous vector).
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)2 but NOT included in (1).
...	Other arguments to pass to lme4::lmer.

Details

Uses lme4::lmer 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::lmer fitted model outputs. Output is of class lmerMod.

See Also

fit2df

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

Examples

library(finalfit)
library(dplyr)

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

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

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