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

Ishii2016(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.

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

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 #>