Test of Homogeneity of Covariance Matrices given by Ahmad 2017

Ahmad2017(x, ...)



data as a data frame, list of matrices, grouped data frame, or resample object


other options passed to covTest method


A list with class "htest" containing the following components:

statistic the value of homogeneity of covariance test statistic
parameter the degrees of freedom for the chi-squared statistic
p.value the p=value for the test
estimate the estimated covariances if less than 5 dimensions
null.value the specified hypothesized value of the covariance difference
alternative a character string describing the alternative hyposthesis
method a character string indicating what type of homogeneity of covariance test was performed


The homogeneityCovariances function is a wrapper function that formats the data for the specific covTest functions.


Ahmad, R. (2017). Location-invariant test of homogeneity of large-dimensional covariance matrices. Journal of Statistical Theory and Practice, 11(4):731-745. 10.1080/15598608.2017.1308895


irisSpecies <- unique(iris$Species) iris_ls <- lapply(irisSpecies, function(x){as.matrix(iris[iris$Species == x, 1:4])} ) names(iris_ls) <- irisSpecies Ahmad2017(iris_ls)
#> #> Ahmad 2017 Homogeneity of Covariance Matrices Test #> #> data: setosa, versicolor and virginica #> Standard Normal = 1.1193, Mean = 0, Variance = 1, p-value = 0.1315 #> alternative hypothesis: true difference in covariance matrices is not equal to 0 #>