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multilevelmod

multilevelmod enables the use of multi-level models (a.k.a mixed-effects models, Bayesian hierarchical models, etc.) with the parsnip package.

(meme courtesy of @ChelseaParlett)

Installation

You can install the released version of multilevelmod from CRAN with:

install.packages("multilevelmod")

For the development version:

devtools::install_github("tidymodels/multilevelmod")

Available Engines

The multilevelmod package provides engines for the models in the following table.

model	engine	mode
linear_reg	stan_glmer	regression
linear_reg	lmer	regression
linear_reg	glmer	regression
linear_reg	gee	regression
linear_reg	lme	regression
linear_reg	gls	regression
logistic_reg	gee	classification
logistic_reg	glmer	classification
logistic_reg	stan_glmer	classification
poisson_reg	gee	regression
poisson_reg	glmer	regression
poisson_reg	stan_glmer	regression

Example

Loading mixedlevelmod will trigger it to add a few modeling engines to the parsnip model database. For Bayesian models, there are now stan-glmer engines for linear_reg(), logistic_reg(), and poisson_reg().

To use these, the function parsnip::fit() function should be used instead of parsnip::fit_xy() so that the model terms can be specified using the lme/lme4 syntax.

The sleepstudy data is used as an example:

library(multilevelmod)
set.seed(1234)
data(sleepstudy, package = "lme4")

mixed_model_spec <- linear_reg() %>% set_engine("lmer")

mixed_model_fit <- 
  mixed_model_spec %>% 
  fit(Reaction ~ Days + (Days | Subject), data = sleepstudy)

mixed_model_fit
#> parsnip model object
#> 
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Reaction ~ Days + (Days | Subject)
#>    Data: data
#> REML criterion at convergence: 1743.628
#> Random effects:
#>  Groups   Name        Std.Dev. Corr
#>  Subject  (Intercept) 24.741       
#>           Days         5.922   0.07
#>  Residual             25.592       
#> Number of obs: 180, groups:  Subject, 18
#> Fixed Effects:
#> (Intercept)         Days  
#>      251.41        10.47

For a Bayesian model:

hier_model_spec <- linear_reg() %>% set_engine("stan_glmer")

hier_model_fit <- 
  hier_model_spec %>% 
  fit(Reaction ~ Days + (Days | Subject), data = sleepstudy)

hier_model_fit
#> parsnip model object
#> 
#> stan_glmer
#>  family:       gaussian [identity]
#>  formula:      Reaction ~ Days + (Days | Subject)
#>  observations: 180
#> ------
#>             Median MAD_SD
#> (Intercept) 251.5    6.5 
#> Days         10.5    1.7 
#> 
#> Auxiliary parameter(s):
#>       Median MAD_SD
#> sigma 25.9    1.6  
#> 
#> Error terms:
#>  Groups   Name        Std.Dev. Corr
#>  Subject  (Intercept) 24           
#>           Days         7       0.08
#>  Residual             26           
#> Num. levels: Subject 18 
#> 
#> ------
#> * For help interpreting the printed output see ?print.stanreg
#> * For info on the priors used see ?prior_summary.stanreg

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
If you think you have encountered a bug, please submit an issue.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
Check out further details on contributing guidelines for tidymodels packages and how to get help.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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