`finalfit`

model wrapper`R/glmmixed.R`

`glmmixed.Rd`

Using `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

`lme4`

specification, e.g. "(var1 | var2)". Note parenthesis MUST be included in (2) but NOT included in (1).- ...
Other arguments to pass to

`lme4::glmer`

.

A list of multivariable `lme4::glmer`

fitted model outputs.
Output is of class `glmerMod`

.

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

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)")
#> explanatory OR (multilevel)
#> 1 age.factor40-59 years 0.75 (0.39-1.44, p=0.382)
#> 2 age.factor60+ years 1.03 (0.55-1.96, p=0.916)
#> 3 sex.factorMale 0.80 (0.58-1.11, p=0.180)
#> 4 obstruct.factorYes 1.23 (0.82-1.83, p=0.320)
#> 5 perfor.factorYes 1.03 (0.43-2.51, p=0.940)
```