In this article, we will learn about the aov() function in R, what it is and how to use it to fit the model analysis of variance? Let’s go into detail now.
What is the aov() function in R?
The aov() function is used to fit the model analysis of variance by the ‘lm’ call for each stratum.
The following is the basic syntax of the aov() function:
aov(formula, data)
Parameters:
- formula: a formula specifying the model.
- data: a data frame. The default is NULL.
How to use the aov() function in R?
We will learn how to use the aov() function to fit the model analysis of variance and then use the summary() function to view the results in the analysis of variance summary table.
We used data from an orange tree planting experiment with the tree’s age (days) and the corresponding stem diameter.
Fit the model analysis of variance
The aov() function takes as its first parameter the ‘formula’, which is a symbolic description of the model. For example, y ~ x is a formula that says y depends on x. The second parameter is the ‘data’, which defines the dataframe where x and y are found.
We performed a one-way analysis of variance to see the difference in growth rates of two orange trees.
Example:
# Create the 'orange' data frame
orange <- data.frame(
tree = gl(2, 7),
age = c(120, 480, 660, 1000, 1230, 1370, 1580, 120, 480, 660, 1000, 1230, 1370, 1580),
diameter = c(30, 58, 87, 115, 120, 142, 145, 33, 54, 89, 120, 124, 147, 151)
)
print(orange)
# Fit the model analysis of variance
aov(diameter ~ age + tree, orange)Output:
tree age diameter
1 1 120 30
2 1 480 58
3 1 660 87
4 1 1000 115
5 1 1230 120
6 1 1370 142
7 1 1580 145
8 2 120 33
9 2 480 54
10 2 660 89
11 2 1000 120
12 2 1230 124
13 2 1370 147
14 2 1580 151
Call:
aov(formula = diameter ~ age + tree, data = orange)
Terms:
age tree Residuals
Sum of Squares 22932.406 31.500 779.023
Deg. of Freedom 1 1 11
Residual standard error: 8.415476
Estimated effects may be unbalancedView a summary of the model
Next, we use the summary() function to see a summary of the model’s performance and coefficients.
Example:
# Create the 'orange' data frame
orange <- data.frame(
tree = gl(2, 7),
age = c(120, 480, 660, 1000, 1230, 1370, 1580, 120, 480, 660, 1000, 1230, 1370, 1580),
diameter = c(30, 58, 87, 115, 120, 142, 145, 33, 54, 89, 120, 124, 147, 151)
)
# Fit the model analysis of variance
(orangeAovModel <- aov(diameter ~ age + tree, orange))
# See a summary of the model
summary(orangeAovModel)Output:
Call:
aov(formula = diameter ~ age + tree, data = orange)
Terms:
age tree Residuals
Sum of Squares 22932.406 31.500 779.023
Deg. of Freedom 1 1 11
Residual standard error: 8.415476
Estimated effects may be unbalanced
Df Sum Sq Mean Sq F value Pr(>F)
age 1 22932 22932 323.811 1.65e-09 ***
tree 1 31 31 0.445 0.519
Residuals 11 779 71
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Summary
This article shared the aov() function in R and how to use it to fit the model analysis of variance. We hope the information in this article has been helpful to you. Thank you for reading.
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Hello, my name’s Bruce Warren. You can call me Bruce. I’m interested in programming languages, so I am here to share my knowledge of programming languages with you, especially knowledge of C, C++, Java, JS, PHP.
Name of the university: KMA
Major: ATTT
Programming Languages: C, C++, Java, JS, PHP
