multilevelmod-package: parsnip methods for hierarchical models

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

parsnip methods for hierarchical models

Description

multilevelmod allows users to use the parsnip package to fit certain hierarchical models (e.g., linear, logistic, and Poisson regression). The package relies on the formula method to specify the random effects.

Details

As an example, the package includes simulated longitudinal data where subjects were measured over time points. The outcome was the number of counts and the predictors are the time point as well as an additional numeric covariate.

We can fit the model using lme4::glmer():

library(tidymodels)
library(multilevelmod)
library(poissonreg) # current required for poisson_reg()

# The lme4 package is required for this model.

tidymodels_prefer()

# Split out two subjects to show how prediction works
data_train <- 
  longitudinal_counts %>% 
  filter(!(subject %in% c("1", "2")))

data_new <- 
  longitudinal_counts %>% 
  filter(subject %in% c("1", "2"))

# Fit the model
count_mod <- 
  poisson_reg() %>% 
  set_engine("glmer") %>% 
  fit(y ~ time + x + (1 | subject), data = data_train)

count_mod
#> parsnip model object
#> 
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#>   Approximation) [glmerMod]
#>  Family: poisson  ( log )
#> Formula: y ~ time + x + (1 | subject)
#>    Data: data
#>       AIC       BIC    logLik  deviance  df.resid 
#>  4474.553  4494.104 -2233.277  4466.553       976 
#> Random effects:
#>  Groups  Name        Std.Dev.
#>  subject (Intercept) 0.9394  
#> Number of obs: 980, groups:  subject, 98
#> Fixed Effects:
#> (Intercept)         time            x  
#>     -0.5946       1.5145       0.2395

When making predictions, the basic predict() method does the trick:

count_mod %>% predict(data_new)
#> # A tibble: 20 x 1
#>    .pred
#>    <dbl>
#>  1  1.19
#>  2  1.42
#>  3  1.65
#>  4  1.83
#>  5  2.04
#>  6  2.66
#>  7  2.96
#>  8  3.43
#>  9  3.94
#> 10  4.64
#> 11  2.21
#> 12  2.60
#> 13  2.97
#> 14  3.38
#> 15  4.16
#> 16  4.90
#> 17  5.45
#> 18  6.20
#> 19  7.55
#> 20  8.64

Author(s)

Maintainer: Hannah Frick [e-mail] (ORCID)

Authors:

• Max Kuhn [e-mail] (ORCID)

Other contributors:

• Posit Software, PBC [copyright holder, funder]

See Also

Useful links:

• https://github.com/tidymodels/multilevelmod

• http://multilevelmod.tidymodels.org/

tidymodels/multilevelmod documentation built on Oct. 22, 2024, 10:21 a.m.