Test of Homogeneity of Covariance Matrices given by Srivastava et al. 2014

Srivastava2014(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., Yanagihara, H., and Kubokawa T. (2014). Tests for covariance matrices in high dimension with less sample size. Journal of Multivariate Analysis, 130:289-309. 10.1016/j.jmva.2014.06.003

## Examples

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

names(iris_ls) <- irisSpecies

Srivastava2014(iris_ls)#>
#> 	Srivastava et al. 2014 Homogeneity of Covariance Matrices Test
#>
#> data:  setosa, versicolor and virginica
#> Chi-Squared = 1007.8, df = 1, p-value < 2.2e-16
#> alternative hypothesis: true difference in covariance matrices is not equal to 0
#>