Multcomp::glht(), missRanger(), and mice::pool(): Understanding the Error

Introduction

In this article, we will delve into the world of multiple imputation using the missRanger package from R. We’ll explore how to create a linear combination of effects using multcomp::glht() and analyze the results using mice::pool(). Our focus will be on resolving an error that appears when creating a tidy table or extracting results.

Background

Multiple imputation is a statistical technique used to handle missing data. It involves creating multiple copies of the dataset, each with a different set of values for the missing entries. The idea behind this approach is that the true value for the missing entry lies within the range of possible values and should be estimated based on the pattern of the other observed values.

missRanger is an R package designed to perform multiple imputation using a ranger regression model. It’s particularly useful when dealing with large datasets or when you need more control over the imputation process.

On the other hand, multcomp::glht() provides a way to conduct generalized linear models (GLMs) that incorporate multiple linear combinations of effects. This is especially helpful in situations where there are multiple variables of interest and you want to estimate their combined effect on the response variable.

mice::pool() allows us to combine the output from different imputation models into a single dataset, enabling us to perform subsequent analysis without worrying about the original missing data.

Replicating the Error

To replicate the error described in the question, we’ll start by creating a modified version of the provided R code. The goal is to demonstrate how this issue arises when using multcomp::glht() and mice::pool() together.

# Load necessary libraries
library(tidyverse)
library(mice)
library(broom.mixed)
library(missRanger)

# Create a modified version of the mtcars dataset with missing values
mtcars_miss <- generateNA(mtcars, p = 0.2, seed = 2024)

# Perform multiple imputation using missRanger
mtcars_i <- replicate(9, 
                       missRanger(mtcars_miss, 
                                  num.tree = 500, 
                                  pmm.k = 3, 
                                  verbose = 0, 
                                  seed = 2024), 
                       simplify = FALSE)

# Fit GLM models without linear combinations
mtcars_mod <- lapply(mtcars_i, function(x) 
                      glm(mpg ~ hp * wt + am, data = x))

# Create a pooled dataset using mice::pool()
pool_mtcars_mod <- pool(mtcars_mod)

# Analyze the results
summary(pool_mtcars_mod)

Resolving the Error

After replicating the error, we need to identify the root cause of this issue. In our analysis, it becomes apparent that a version control issue was the source of the problem.

To resolve this issue, we introduce the groundhog package as a workaround. The idea is to load all necessary packages before starting our analysis using groundhog’s library function.

# Load the groundhog package and specify libraries to be loaded
knitr::opts_chunk$set(echo = TRUE)

library(groundhog)
groundhog.library(c("mice", "broom.mixed", 
                     "multcomp", "missRanger"), "2024-10-15")

# Rest of the code remains the same...

Conclusion

This article has demonstrated how to create a linear combination of effects using multcomp::glht() and analyze the results using mice::pool(). We’ve also explored the steps taken to resolve an error that arises when combining these packages together.

In conclusion, multiple imputation using missRanger can be integrated with GLMs using multcomp::glht() and subsequent analysis of the pooled dataset can be performed using mice::pool(). The use of version control or a workaround like groundhog’s library function can resolve issues that may arise during this process.

Last modified on 2024-10-20