glmmixed {finalfit} | R Documentation |
finalfit
model wrapperUsing finalfit
conventions, produces mixed effects binomial logistic
regression models for a set of explanatory variables against a binary dependent.
glmmixed(.data, dependent, explanatory, random_effect, ...)
.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 |
... |
Other arguments to pass to |
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
A list of multivariable lme4::glmer
fitted model outputs.
Output is of class glmerMod
.
Other finalfit model wrappers:
coxphmulti()
,
coxphuni()
,
crrmulti()
,
crruni()
,
glmmulti_boot()
,
glmmulti()
,
glmuni()
,
lmmixed()
,
lmmulti()
,
lmuni()
,
svyglmmulti()
,
svyglmuni()
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)")