Test of Homogeneity of Covariance Matrices given by Ishii and Aoshima 2016

Ishii2016(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

Ishii, A., Yata, K., and Aoshima, M. (2016). Asymptotic properties of the first pricipal component and equality tests of covariance matrices in high-dimesion, low-sample-size context. Journal of Statistical Planning and Inference, 170:186-199. 10.1016/j.jspi.2015.10.007

## Examples

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

names(iris_ls) <- irisSpecies

Ishii2016(iris_ls)#>
#> 	Ishii and Aoshima 2016 Homogeneity of Covariance Matrices Test
#>
#> data:  setosa, versicolor and virginica
#> F = 4.6285, df1 = 3, df2 = 1, p-value = 0.3263
#> alternative hypothesis: true difference in covariance matrices is not equal to 0
#>