When to Use aov() vs. anova() in R

One of the fundamental tasks in statistical analysis is understanding the differences between groups. ANOVA (Analysis of Variance) is a robust statistical technique that seeks to compare the means of different groups to determine if there’s a significant difference. In R, two commonly used functions for running ANOVAs are aov() and anova(). However, these two functions are not interchangeable and serve slightly different purposes.

In this article, we will delve into the intricacies of both functions, helping you understand when it’s appropriate to use each.

Table of Contents

1. The Basics: What is ANOVA?
2. Introducing the aov() Function
3. Introducing the anova() Function
4. Key Differences between aov() and anova()
5. Practical Examples
6. Considerations for Complex Designs
7. Common Mistakes and How to Avoid Them
8. Conclusion

1. The Basics: What is ANOVA?

ANOVA is a hypothesis-testing procedure that tests whether means of different groups are equal. It examines the impact of one or more factors by comparing the means of different levels of those factors.

2. Introducing the aov( ) Function

The aov() function is part of base R and is designed to handle balanced and unbalanced designs for factorial ANOVAs, as well as nested and repeated measures designs.

Syntax

The basic syntax of aov() is quite straightforward:

aov(formula, data)

Example Usage

data(mtcars)
aov_model <- aov(mpg ~ gear, data = mtcars)
summary(aov_model)

3. Introducing the anova( ) Function

Contrary to common belief, the anova() function is not a tool to perform ANOVA but rather a method to compare models, particularly those fit by maximum likelihood estimation methods.

Syntax

The basic syntax for comparing two models using anova() is:

anova(model1, model2)

Example Usage

model1 <- lm(mpg ~ gear, data = mtcars)
model2 <- lm(mpg ~ gear + hp, data = mtcars)
anova(model1, model2)

4. Key Differences between aov( ) and anova( )

• Purpose: aov() is used to fit ANOVA models, whereas anova() is used to compare nested models.
• Input: aov() requires a formula and data frame, while anova() requires fitted model objects.
• Output: aov() returns an object of class “aov” that can be summarized to get the ANOVA table, whereas anova() returns a table showing the comparison of models.

5. Practical Examples

Using aov( ) for a Simple One-way ANOVA

data(mtcars)
aov_model <- aov(mpg ~ gear, data = mtcars)
summary(aov_model)

Using anova( ) to Compare Models

model1 <- lm(mpg ~ gear, data = mtcars)
model2 <- lm(mpg ~ gear + hp, data = mtcars)
anova(model1, model2)

6. Considerations for Complex Designs

• Balanced Designs: aov() is generally recommended for balanced designs, where each group has the same number of observations.
• Model Comparison: anova() is more suitable for comparing different statistical models to identify which model explains the variability in the data better.

7. Common Mistakes and How to Avoid Them

1. Using anova() to Perform ANOVA: Remember, anova() is for model comparison.
2. Unbalanced Designs with aov(): Although it can handle unbalanced designs, it’s often recommended to use specialized packages like lme4 for such scenarios.

8. Conclusion

Deciding between aov() and anova() hinges primarily on the objective of your analysis. If you aim to perform ANOVA on a balanced design, aov() is your best bet. If you intend to compare the fits of different models, particularly nested models, then anova() is more appropriate.

Understanding the strengths, limitations, and appropriate use-cases for these functions is crucial for effective data analysis. By following the guidelines outlined in this comprehensive article, you should be well-equipped to choose the appropriate function for your analytical needs in R.