In m-clark/mixedup: Miscellaneous functions for mixed models

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mixedup Logo

mixedup

a package for extracting clean results from mixed models

This package provides extended functionality for mixed models. The goal of mixedup is to solve little problems I have had that slip through the cracks from the various modeling packages and others in trying to get presentable output. Basically the idea is to create (tidy) objects that are easy to use and essentially ready for presentation, as well as consistent across packages and across functions. Such objects would be things like variance components and random effects. I use several of these packages (including mgcv) for mixed models, and typically have to do some notable post processing to get some viable output even with broom::tidy, and this effort often isn't applicable if I switch to another package for the same type of model. These functions attempt to address this issue.

For more details and examples see https://m-clark.github.io/mixedup/.

Installation

You can install mixedup from GitHub with remotes. Use the second approach if you don't already have rstanarm or brms (they aren't required to use in general).

remotes::install_github('m-clark/mixedup')

# if you don't already have rstanarm and/or brms

withr::with_envvar(c(R_REMOTES_NO_ERRORS_FROM_WARNINGS = "true"), 
  remotes::install_github('m-clark/mixedup')
)

Supported models

lme4
glmmTMB
nlme
mgcv
rstanarm
brms

Feature list

Extract Variance Components
Extract Random Effects
Extract Fixed Effects
Extract Random Coefficients
Extract Heterogeneous Variances
Extract Correlation Structure
Extract Model Data
Summarize Model
Find Typical

Not all features are available to the various modeling packages (e.g. autocorrelation for lme4), and some functionality may just not be supported for this package, but most functions are applicable to the packages listed.

Examples

Setup

In the following I suppress the package startup and other information that isn't necessary for demo.

brm_model <- mixedup:::brms_model
rstanarm_model <- mixedup:::rstanarm_model

library(lme4)

lmer_model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)

library(glmmTMB)

tmb_model <- glmmTMB(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)

library(nlme)

nlme_model <-  nlme(
  height ~ SSasymp(age, Asym, R0, lrc),
  data = Loblolly,
  fixed = Asym + R0 + lrc ~ 1,
  random = Asym ~ 1,
  start = c(Asym = 103, R0 = -8.5, lrc = -3.3)
)

library(brms)

# brm_model = brm(
#   Reaction ~ Days + (1 + Days | Subject), 
#   data = sleepstudy, 
#   refresh = -1,
#   verbose = FALSE,
#   open_progress = FALSE,
#   cores = 4,
#   iter = 1000
# )

library(rstanarm)

# rstanarm_model = stan_glmer(
#   Reaction ~ Days + (1 + Days | Subject), 
#   data = sleepstudy, 
#   refresh = -1,
#   verbose = FALSE,
#   show_messages = FALSE,
#   open_progress = FALSE,
#   cores = 4,
#   iter = 1000
# )

library(mgcv)

gam_model = gam(
  Reaction ~  Days +
    s(Subject, bs = 're') +
    s(Days, Subject, bs = 're'),
  data = lme4::sleepstudy,
  method = 'REML'
)

Extract Output from a Mixed Model

library(mixedup)

extract_random_effects(tmb_model)

extract_fixed_effects(nlme_model)

extract_random_coefs(lmer_model)

extract_vc(brm_model, ci_level = .8)

summarize_model(lmer_model, cor_re = TRUE, digits = 1)

find_typical(gam_model, probs = c(.25, .50, .75))

Consistent output

mods = list(
  tmb  = tmb_model,
  lmer = lmer_model, 
  brm  = brm_model,
  stan = rstanarm_model,
  gam  = gam_model
)

purrr::map_df(mods, extract_vc, .id = 'model')