Aitchison's test for two mean vectors and/or covariance matrices | R Documentation |

Aitchison's test for two mean vectors and/or covariance matrices.

ait.test(x1, x2, type = 1, alpha = 0.05)

`x1` |
A matrix containing the compositional data of the first sample. Zeros are not allowed. |

`x2` |
A matrix containing the compositional data of the second sample. Zeros are not allowed. |

`type` |
The type of hypothesis test to perform. Type=1 refers to testing the equality of the mean vectors and the covariance matrices. Type=2 refers to testing the equality of the covariance matrices. Type=2 refers to testing the equality of the mean vectors. |

`alpha` |
The significance level, set to 0.05 by default. |

The test is described in Aitchison (2003). See the references for more information.

A vector with the test statistic, the p-value, the critical value and the degrees of freedom of the chi-square distribution.

Michail Tsagris.

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

John Aitchison (2003). The Statistical Analysis of Compositional Data, p. 153-157. Blackburn Press.

```
comp.test, maovjames, el.test2, eel.test2,
```

x1 <- as.matrix(iris[1:50, 1:4]) x1 <- x1 / rowSums(x1) x2 <- as.matrix(iris[51:100, 1:4]) x2 <- x2 / rowSums(x2) ait.test(x1, x2, type = 1) ait.test(x1, x2, type = 2) ait.test(x1, x2, type = 3)

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