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, ...)Dataframe.
Character vector of length 1, name of depdendent variable (must be continuous vector).
Character vector of any length: name(s) of explanatory variables.
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.
A list of multivariable lme4::lmer fitted model
outputs. Output is of class lmerMod.
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.
Other finalfit model wrappers:
coxphmulti(),
coxphuni(),
crrmulti(),
crruni(),
glmmixed(),
glmmulti_boot(),
glmmulti(),
glmuni(),
lmmulti(),
lmuni(),
svyglmmulti(),
svyglmuni()
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 to 0.07, p=0.035)
#> 2 age.factor60+ years -0.98 (-1.81 to -0.14, p=0.011)
#> 3 sex.factorMale -0.19 (-0.62 to 0.24, p=0.195)
#> 4 obstruct.factorYes -0.37 (-0.92 to 0.17, p=0.091)
#> 5 perfor.factorYes 0.23 (-1.01 to 1.48, p=0.357)