Test of Homogeneity of Covariance Matrices given by Srivastava et al. 2014
Srivastava2014(x, ...)
| 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.
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
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 #>