In multilevelmod: Model Wrappers for Multi-Level Models

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

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

count_mod %>% predict(data_new)

multilevelmod documentation built on June 17, 2022, 5:05 p.m.