The Glmer Function and the Effects Package: Looping with Challenges

Introduction

The lme4 package in R is a comprehensive implementation of linear mixed models, which are commonly used in statistical modeling to analyze data with complex structures. One of the powerful features of the lme4 package is its integration with the effects package, which provides functions for estimating effects within linear mixed models. In this article, we will explore how to use the glmer function from the lme4 package in conjunction with the effects package and discuss a common challenge that arises when looping over multiple objects.

Background

The glmer function is used to fit general linear mixed models (GLMMs) using generalized least squares (GLS). It takes as input a response variable, predictor variables, a random component, and other model specification arguments. The effects package provides functions for estimating effects within these GLMMs.

When working with multiple objects, it’s common to use the lapply() or sapply() functions to loop over the elements of an object, applying a function to each element without explicitly assigning them to variables. However, when using glmer and effects, this can sometimes lead to unexpected behavior.

The Problem

In the example provided in the Stack Overflow question, we see that when using lapply() with the glmer function, the model fails to find objects inside the loop, leading to an error message stating that object ‘y’ is not found. This problem persists even after manually placing the data frame into the workspace.

Solution

To overcome this challenge, one approach is to use a for loop instead of lapply. A for loop assigns objects to the global environment each time it runs, so you wouldn’t have to place them there manually. In the example provided in the question, using a for loop fixes the problem and allows us to run the effects::effect() function successfully.

However, this approach comes with its own set of considerations. In particular, we need to assign the output to something when using a for loop instead of lapply.

Assignment in For Loops

In R, variables that are assigned within a for loop do not retain their values outside the loop by default. This is because the assignment statement overwrites any existing variable with the same name.

For example, consider the following code:

for (k in levels(dat$treat)) {
  y <- subset(dat, treat == k)
  mod <- glmer(value ~ var + (1|rand), data = y, family = binomial)
  effects::effect("var", mod)
}

In this case, we would get an error message stating that the object ‘y’ is not found when trying to access it outside of the loop.

To avoid this issue, we can reassign the output within a single scope or by using a different variable name for the model inside the loop. Here’s how you can modify the code:

for (k in levels(dat$treat)) {
  y <- subset(dat, treat == k)
  mod <- glmer(value ~ var + (1|rand), data = y, family = binomial)
  effects::effect("var", mod)
}

However, using for loops can be less efficient than lapply. Therefore, it is recommended to use lapply() unless the for loop simplifies your code or improves performance.

General Solution: Using `lapply` with `assign()` and `list`

One possible solution when using lapply is to assign results inside a list object instead of directly assigning them to variables. Here’s how you can do it:

results <- lapply(levels(dat$treat), function(k) {
  y <- subset(dat, treat == k)
  mod <- glmer(value ~ var + (1|rand), data = y, family = binomial)
  effects::effect("var", mod)
})

In this approach, results are returned as a named list where the names correspond to the elements of levels(dat$treat). By using named lists, you can easily access individual results after running the loop.

Conclusion

Looping over multiple objects with GLMMs and estimating effects can be challenging when using certain methods like lapply. However, there are workarounds available, including using for loops or working with lists to keep track of intermediate results.

By following this article’s guidance, you’ll learn how to use the glmer function from the lme4 package in conjunction with the effects package when looping over multiple objects and how to overcome common challenges that arise during this process.

Last modified on 2023-05-14