`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 |

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`

, `glmmulti_boot`

,
`glmmulti`

, `glmuni`

,
`lmmixed`

, `lmmulti`

,
`lmuni`

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) #> 2 age.factor40-59 years 0.75 (0.39-1.44, p=0.382) #> 3 age.factor60+ years 1.03 (0.55-1.96, p=0.916) #> 4 sex.factorMale 0.80 (0.58-1.11, p=0.180) #> 5 obstruct.factorYes 1.23 (0.82-1.83, p=0.320) #> 6 perfor.factorYes 1.03 (0.43-2.51, p=0.940)