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 #>