Compare Multiple Sample Variances in R - Easy Guides - Wiki (2024)

• Statistical tests for comparing variances
• Statistical hypotheses
• Import and check your data into R
• Compute Bartlett’s test in R
• Compute Levene’s test in R
• Compute Fligner-Killeen test in R
• Infos

There are many solutions to test for the equality (hom*ogeneity) 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.

  See Also

  Levene’s Test in R Programming - GeeksforGeeksLevene's Test for hom*ogeneity of VarianceKapitel 13 Statistische Tests | R für Psychos

• Fligner-Killeen test: a non-parametric test which is very robust against departures from normality.

The 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.

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.

To import your data, use the following R code:

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

Here, we’ll use ToothGrowth and PlantGrowth data sets:

# Load the datadata(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 PlantGrowthdplyr::sample_n(PlantGrowth, 10)

 weight group24 5.50 trt212 4.17 trt125 5.37 trt226 5.29 trt22 5.58 ctrl14 3.59 trt122 5.12 trt213 4.41 trt111 4.81 trt121 6.31 trt2

# PlantGrowth data structurestr(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 ToothGrowthdplyr::sample_n(ToothGrowth, 10)

 len supp dose28 21.5 VC 2.040 9.7 OJ 0.534 9.7 OJ 0.56 10.0 VC 0.551 25.5 OJ 2.014 17.3 VC 1.03 7.3 VC 0.518 14.5 VC 1.050 27.3 OJ 1.046 25.2 OJ 1.0

# ToothGrowth data structurestr(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.

Bartlett’s test is used for testing hom*ogeneity 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:

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 hom*ogeneity of variancesdata: weight by groupBartlett'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 hom*ogeneity of variancesdata: len by interaction(supp, dose)Bartlett's K-squared = 6.9273, df = 5, p-value = 0.2261

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 variableleveneTest(weight ~ group, data = PlantGrowth)

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

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

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

The Fligner-Killeen test is one of the many tests for hom*ogeneity 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 hom*ogeneity of variancesdata: weight by groupFligner-Killeen:med chi-squared = 2.3499, df = 2, p-value = 0.3088

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