finalfit (version 1.0.8)

lmmixed: 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, ...)

Value

A list of multivariable lme4::lmer fitted model outputs. Output is of class lmerMod.

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.

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")