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 |

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`

, `glmmixed`

,
`glmmulti_boot`

, `glmmulti`

,
`glmuni`

, `lmmulti`

,
`lmuni`

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")#> Warning: P-value for lmer is estimate assuming t-distribution is normal. Bootstrap for final publication.#> explanatory Coefficient (multilevel #> 2 age.factor40-59 years 0.45 (0.19-1.07, p=0.035) #> 3 age.factor60+ years 0.38 (0.16-0.87, p=0.011) #> 4 sex.factorMale 0.83 (0.54-1.27, p=0.195) #> 5 obstruct.factorYes 0.69 (0.40-1.19, p=0.091) #> 6 perfor.factorYes 1.26 (0.36-4.40, p=0.357)