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 0.2395When making predictions, the basic predict() method does the trick:
## # A tibble: 20 × 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 8.64Author
Maintainer: Hannah Frick hannah@rstudio.com (ORCID)
Authors:
Max Kuhn max@rstudio.com (ORCID)
Other contributors:
RStudio [copyright holder, funder]