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, ...)`

- .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`

.

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 (-1.65-0.07, 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)
```