README.md
In tidymodels/multilevelmod: Model Wrappers for Multi-Level Models

multilevelmod

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

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(meme courtesy of @ChelseaParlett)

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

install.packages("multilevelmod")

For the development version:

# install.packages("pak")
pak::pak("tidymodels/multilevelmod")

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 |

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) 251.3    6.5 
#> Days         10.4    1.7 
#> 
#> Auxiliary parameter(s):
#>       Median MAD_SD
#> sigma 25.9    1.6  
#> 
#> Error terms:
#>  Groups   Name        Std.Dev. Corr
#>  Subject  (Intercept) 24.1         
#>           Days         6.9     0.07
#>  Residual             26.0         
#> Num. levels: Subject 18 
#> 
#> ------
#> * For help interpreting the printed output see ?print.stanreg
#> * For info on the priors used see ?prior_summary.stanreg

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