How to run GLMER with SMOTE and resampling - r

Question

I want to create GLMER models with the SMOTE algorithm and resampling. This means that I need to create a recipe with step_smote() and use fit_resamples(). After reading this post, and this post, I learned that I need to use add_model() and add_variable() in order to create a workflow object, and that add_recipe() cannot be used in this process. This means I cannot use step_smote(). The first link addresses this limitation. Now the question is how I can use the SMOTE algorithm in this situation. In addition, I want to use a few other step_***() functions such as step_dummy() and step_rm(). In short, I want to use a recipe and cross validation in GLMER modeling processes. Is there any way to make this happen? I decided to use the example in this post so that everyone can reproduce the same situation. Note that solutions do not have to be in tidymodels ways.
library(tidyverse)
library(tidymodels)
library(themis)
# I use data from https://juliasilge.com/blog/himalayan-climbing/
members <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv")
# These are the same processes from the post above.
members_df <- members %>%
filter(season != "Unknown", !is.na(sex), !is.na(citizenship)) %>%
select(peak_id, year, season, sex, age, citizenship, hired, success, died) %>%
mutate(died = case_when(died ~ "died", TRUE ~ "survived")) %>%
mutate_if(is.character, factor) %>%
mutate_if(is.logical, as.integer)
# Since the dataset is large, I subset it here for this question.
set.seed(386)
bind_rows(filter(members_df, died == "died"),
filter(members_df, died == "survived") %>% sample_n(2000)) -> members_df
# Create data sets
set.seed(123)
members_split <- initial_split(members_df, strata = died)
members_train <- training(members_split)
members_test <- testing(members_split)
# Create CV folds
set.seed(123)
members_folds <- vfold_cv(members_train, v = 10, repeats = 5, strata = died)
# Here I wanna use glmer. Create model specification
logistic_reg() %>%
set_engine("glmer", family = binomial,
control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 2e5))) %>%
set_mode("classification") -> members_spec
# As far as I learned from these links, I cannot use a recipe in order to run GLMER. But I create a recipe, anyway.
# The key is that I want to apply SMOTE algorithm to glmer modeling in tidymodels ways.
recipe(died ~ ., data = members_train) %>%
step_rm(citizenship) %>%
step_impute_median(age) %>%
step_other(peak_id) %>%
step_dummy(all_nominal(), -died) %>%
step_smote(died) -> members_recipe
# This works as long as users do not have to use the recipe package.
# This is addressed in https://github.com/tidymodels/multilevelmod/issues/4
workflow() %>%
add_model(spec = members_spec,
formula = died ~ year + season + sex + age + (1|peak_id)) %>%
add_variables(outcomes = died,
predictors = c(year, season, sex, age, peak_id)) -> members_workflow
# This is the WRONG way.
workflow() %>%
add_model(spec = members_spec,
formula = died ~ year + season + sex + age + (1|peak_id)) %>%
add_recipe(members_recipe) -> wrong_workflow
# This is another WRONG way. This causes an error message.
workflow() %>%
add_model(spec = members_spec,
formula = died ~ year + season + sex + age + (1|peak_id)) %>%
add_variables(outcomes = died,
predictors = c(year, season, sex, age, peak_id)) %>%
add_recipe(members_recipe)
# Ideally, the workflow object here includes a recipe. But can we make it?
set.seed(777)
fit_resamples(object = wrong_workflow, # This workflow object needs to include a recipe
resamples = members_folds,
control = control_resamples(save_pred = TRUE, save_workflow = TRUE)) -> members_res