Test of Homogeneity of Covariance Matrices given by Srivastava 2007

Srivastava2007(x, ...)

## Arguments

x data as a data frame, list of matrices, grouped data frame, or resample object other options passed to covTest method

## Value

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

## Details

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

## References

Srivastava, M. S. (2007). Testing the equality of two covariance matrices and independence of two sub-vectors with fewer observations than the dimension. InInternational Conference on Advances in InterdisciplinaryStistics and Combinatorics, University of North Carolina at Greensboro, NC, USA.

## Examples

irisSpecies <- unique(iris$Species) iris_ls <- lapply(irisSpecies, function(x){as.matrix(iris[iris$Species == x, 1:4])}
)

names(iris_ls) <- irisSpecies

Srivastava2007(iris_ls)#>
#> 	Srivastava 2007 Homogeneity of Covariance Matrices Test
#>
#> data:  setosa, versicolor and virginica
#> Chi-Squared = -19.472, df = 2, p-value = 1
#> alternative hypothesis: true difference in covariance matrices is not equal to 0
#>