F-Test: Compare Two Variances in R

1. whether the variance of group A (\(\sigma^2_A\)) is equal to the variance of group B (\(\sigma^2_B\))?
2. whether the variance of group A (\(\sigma^2_A\)) is less than the variance of group B (\(\sigma^2_B\))?
3. whether the variance of group A (\(\sigma^2_A\)) is greather than the variance of group B (\(\sigma^2_B\))?

Note that:

• Hypotheses 1) are called two-tailed tests
• Hypotheses 2) and 3) are called one-tailed tests

R function

The R function var.test() can be used to compare two variances as follow:

# Method 1
var.test(values ~ groups, data, 
         alternative = "two.sided")
# or Method 2
var.test(x, y, alternative = "two.sided")

Import and check your data into R

To import your data, use the following R code:

# If .txt tab file, use this
my_data <- read.delim(file.choose())
# Or, if .csv file, use this
my_data < - read.csv(file.choose())

Here, we’ll use the built-in R data set named ToothGrowth:

# Store the data in the variable my_data
my_data < - ToothGrowth

To have an idea of what the data look like, we start by displaying a random sample of 10 rows using the function sample_n()[in dplyr package]:

library("dplyr")
sample_n(my_data, 10)
    len supp dose
43 23.6   OJ  1.0
28 21.5   VC  2.0
25 26.4   VC  2.0
56 30.9   OJ  2.0
46 25.2   OJ  1.0
7  11.2   VC  0.5
16 17.3   VC  1.0
4   5.8   VC  0.5
48 21.2   OJ  1.0
37  8.2   OJ  0.5

We want to test the equality of variances between the two groups OJ and VC in the column “supp”.

Preleminary test to check F-test assumptions

F-test is very sensitive to departure from the normal assumption. You need to check whether the data is normally distributed before using the F-test.

Shapiro-Wilk test can be used to test whether the normal assumption holds. It’s also possible to use Q-Q plot (quantile-quantile plot) to graphically evaluate the normality of a variable. Q-Q plot draws the correlation between a given sample and the normal distribution.

If there is doubt about normality, the better choice is to use Levene’s test or Fligner-Killeen test, which are less sensitive to departure from normal assumption.

Compute F-test

# F-test
res.ftest <- var.test(len ~ supp, data = my_data)
res.ftest

    F test to compare two variances
data:  len by supp
F = 0.6386, num df = 29, denom df = 29, p-value = 0.2331
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
 0.3039488 1.3416857
sample estimates:
ratio of variances 
         0.6385951 

Interpretation of the result

< pan class = "success">The p-value of F-test is p = 0.2331433 which is greater than the significance level 0.05. In conclusion, there is no significant difference between the two variances.

Access to the values returned by var.test() function

The function var.test() returns a list containing the following components:

The format of the R code to use for getting these values is as follow:

# ratio of variances
res.ftest$estimate
ratio of variances 
         0.6385951 
# p-value of the test
res.ftest$p.value
[1] 0.2331433