mi package: Overcoming the Pool Function Error

The mi package, developed by Peter Hoffmann and colleagues, is a powerful tool for missing data imputation in R. It provides an efficient and flexible approach to handle complex datasets with various types of missing information. However, like any other software, it’s not immune to errors and quirks. In this article, we’ll delve into the issue of the pool function giving an error when used within a specific context.

Understanding the mi package and Pool Function

The mi package is designed to handle missing data in a Bayesian framework. It uses a linear mixed-effects model (LMM) to impute missing values based on observed data and prior knowledge of the distribution. The pool function, introduced in version 3.0 of the package, allows users to specify the model for imputation.

The pool function takes two primary arguments:

The formula describing the relationships between variables
Data frame containing the variable values

In our case, we’re using a simple linear regression with additional predictor variables:

analysis <- pool(ppvtr.36 ~ first + b.marr + income + momage + momed + momrace,
        data = imputations)

Here, ppvtr.36 is the response variable, and first, b.marr, income, momage, momed, and momrace are predictor variables.

The Error: Missing Interpretation

When we run the code with the 06 prefix removed, the error message indicates that an object named INTERP of mode function was not found:

Error in get(as.character(FUN), mode = "function", envir = envir) : 
  object 'INTERP' of mode 'function' was not found

This seems to be a peculiar issue, as the code itself looks correct. We’ll explore possible explanations and solutions below.

Why Does this Happen?

The error message suggests that the pool function is unable to find an object named INTERP in its internal environment. The get() function is used within R to retrieve an object from a specified environment. However, there seems to be a mismatch between what the function expects and what’s actually available.

Context: Global Environment and Local Scope

One possible explanation for this issue lies in the interaction between the global environment (envir) and local scope within the pool function. The pool() function calls other functions internally, which might rely on the global environment to access certain objects or variables.

When we use the 06 prefix before calling the pool function, it’s possible that this prefix creates a new, separate environment where the internal functions are defined. This new environment may not be accessible from the original global environment, leading to the missing interpretation error.

Removing the Prefix: A Solution

Fortunately, removing the 06 prefix solves the issue:

analysis <- pool(ppvtr.36 ~ first + b.marr + income + momage + momed + momrace,
        data = imputations)

This corrected code works as expected.

Conclusion and Advice

In this article, we’ve explored an error involving the pool function within the mi package. We discovered that removing the 06 prefix before calling the pool function resolves the issue, likely due to differences in global environments between versions of R or packages.

When working with new software or updates, always be mindful of potential interactions and changes in behavior. Removing or inspecting prefixes, suffixes, or other modifications can often help resolve conflicts.

For those interested in learning more about the mi package or missing data imputation in R, I recommend checking out the official documentation, examples, and tutorials provided by the authors. Practice using different packages and techniques to better understand how they work and how to overcome potential challenges.

Additional Context: Best Practices

Here are some additional best practices for working with the mi package:

Make sure to thoroughly read and follow the example code in the documentation.
Experiment with different imputation models, including linear regression, generalized additive models (GAMs), and Bayesian approaches like the mipool function.
Be cautious when specifying predictor variables or interactions within your model; some may not be supported or might introduce errors.
Consider using other packages for missing data imputation if you encounter issues with the mi package.

We hope this article has provided a detailed explanation of the error and its solution. If you have any further questions or concerns, feel free to ask!

Last modified on 2023-09-05