# Compare Multiple Sample Variances in R

This article describes **statistical tests** for comparing the **variances** of two or more samples. Equal variances across samples is called **homogeneity** of **variances**.

Some statistical tests, such as two independent samples T-test and ANOVA test, assume that variances are equal across groups. The **Bartlett’s test**, **Levene’s test** or **Fligner-Killeen’s test** can be used to verify that assumption.

# Statistical tests for comparing variances

There are many solutions to test for the equality (**homogeneity**) of variance across groups, including:

**F-test**: Compare the variances of two samples. The data must be normally distributed.**Bartlett’s test**: Compare the variances of k samples, where k can be more than two samples. The data must be normally distributed. The Levene test is an alternative to the Bartlett test that is less sensitive to departures from normality.**Levene’s test**: Compare the variances of k samples, where k can be more than two samples. It’s an alternative to the Bartlett’s test that is less sensitive to departures from normality.**Fligner-Killeen test**: a non-parametric test which is very robust against departures from normality.

**F-test**has been described in our previous article: F-test to compare equality of two variances. In the present article, we’ll describe the tests for comparing more than two variances.

# Statistical hypotheses

For all these tests (**Bartlett’s test**, **Levene’s test** or **Fligner-Killeen’s test**),

- the null hypothesis is that all populations variances are equal;
- the alternative hypothesis is that at least two of them differ.

# 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 ToothGrowth and PlantGrowth data sets:

```
# Load the data
data(ToothGrowth)
data(PlantGrowth)
```

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]. First, install dplyr package if you don’t have it: **install.packages(“dplyr”)**.

Show 10 random rows:

```
set.seed(123)
# Show PlantGrowth
dplyr::sample_n(PlantGrowth, 10)
```

```
weight group
24 5.50 trt2
12 4.17 trt1
25 5.37 trt2
26 5.29 trt2
2 5.58 ctrl
14 3.59 trt1
22 5.12 trt2
13 4.41 trt1
11 4.81 trt1
21 6.31 trt2
```

```
# PlantGrowth data structure
str(PlantGrowth)
```

```
'data.frame': 30 obs. of 2 variables:
$ weight: num 4.17 5.58 5.18 6.11 4.5 4.61 5.17 4.53 5.33 5.14 ...
$ group : Factor w/ 3 levels "ctrl","trt1",..: 1 1 1 1 1 1 1 1 1 1 ...
```

```
# Show ToothGrowth
dplyr::sample_n(ToothGrowth, 10)
```

```
len supp dose
28 21.5 VC 2.0
40 9.7 OJ 0.5
34 9.7 OJ 0.5
6 10.0 VC 0.5
51 25.5 OJ 2.0
14 17.3 VC 1.0
3 7.3 VC 0.5
18 14.5 VC 1.0
50 27.3 OJ 1.0
46 25.2 OJ 1.0
```

```
# ToothGrowth data structure
str(ToothGrowth)
```

```
'data.frame': 60 obs. of 3 variables:
$ len : num 4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
$ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
$ dose: num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
```

Note that, R considers the column “dose” [in ToothGrowth data set] as a numeric vector. We want to convert it as a grouping variable (factor).

`ToothGrowth$dose <- as.factor(ToothGrowth$dose)`

We want to test the equality of variances between groups.

# Compute Bartlett’s test in R

**Bartlett’s test**is used for testing homogeneity of variances in k samples, where k can be more than two. It’s adapted for normally distributed data. The

**Levene test**, described in the next section, is a more robust alternative to the Bartlett test when the distributions of the data are non-normal.

The R function **bartlett.test**() can be used to compute Barlett’s test. The simplified format is as follow:

`bartlett.test(formula, data)`

**formula**: a formula of the form values ~ groups**data**: a matrix or data frame

The function returns a list containing the following component:

**statistic**: Bartlett’s K-squared test statistic**parameter**: the degrees of freedom of the approximate chi-squared distribution of the test statistic.**p.value**: the p-value of the test

To perform the test, we’ll use the *PlantGrowth* data set, which contains the weight of plants obtained under 3 treatment groups.

**Bartlett’s test with one independent variable**:

```
res <- bartlett.test(weight ~ group, data = PlantGrowth)
res
```

```
Bartlett test of homogeneity of variances
data: weight by group
Bartlett's K-squared = 2.8786, df = 2, p-value = 0.2371
```

From the output, it can be seen that the p-value of 0.2370968 is not less than the significance level of 0.05. This means that there is no evidence to suggest that the variance in plant growth is statistically significantly different for the three treatment groups.

**Bartlett’s test with multiple independent variables**: the**interaction**() function must be used to collapse multiple factors into a single variable containing all combinations of the factors.

`bartlett.test(len ~ interaction(supp,dose), data=ToothGrowth)`

```
Bartlett test of homogeneity of variances
data: len by interaction(supp, dose)
Bartlett's K-squared = 6.9273, df = 5, p-value = 0.2261
```

# Compute Levene’s test in R

As mentioned above, Levene’s test is an alternative to Bartlett’s test when the data is not normally distributed.

The function **leveneTest**() [in **car** package] can be used.

```
library(car)
# Levene's test with one independent variable
leveneTest(weight ~ group, data = PlantGrowth)
```

```
Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 2 1.1192 0.3412
27
```

```
# Levene's test with multiple independent variables
leveneTest(len ~ supp*dose, data = ToothGrowth)
```

```
Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 5 1.7086 0.1484
54
```

# Compute Fligner-Killeen test in R

The **Fligner-Killeen test** is one of the many tests for homogeneity of variances which is most robust against departures from normality.

The R function **fligner.test**() can be used to compute the test:

`fligner.test(weight ~ group, data = PlantGrowth)`

```
Fligner-Killeen test of homogeneity of variances
data: weight by group
Fligner-Killeen:med chi-squared = 2.3499, df = 2, p-value = 0.3088
```

# Infos

This analysis has been performed using **R software** (ver. 3.2.4).

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