Mixed effects linear regression models: finalfit model wrapper

Source: R/lmmixed.R

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

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.

Value

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

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")
#> P-value for lmer is estimate assuming t-distribution is normal. Bootstrap for final publication.
#>             explanatory      Coefficient (multilevel
#> 1 age.factor40-59 years  -0.79 (-1.65-0.07, p=0.035)
#> 2   age.factor60+ years -0.98 (-1.81--0.14, p=0.011)
#> 3        sex.factorMale  -0.19 (-0.62-0.24, p=0.195)
#> 4    obstruct.factorYes  -0.37 (-0.92-0.17, p=0.091)
#> 5      perfor.factorYes   0.23 (-1.01-1.48, p=0.357)