RHT2 | R Documentation |

Robust Hotelling T^2 Test for One Sample in high Dimensional Data

```
RHT2(data, mu0, alpha = 0.75, d, q)
```

`data` |
the data. It must be matrix or data.frame. |

`mu0` |
the mean vector which will be used to test the null hypothesis. |

`alpha` |
numeric parameter controlling the size of the subsets over which the determinant is minimized. Allowed values are between 0.5 and 1 and the default is 0.75. |

`d` |
the constant in Equation (11) in the study by Bulut (2021). |

`q` |
the second degree of freedom value of the approximate F distribution in Equation (11) in the study by Bulut (2021). |

`RHT2`

function performs a robust Hotelling T^2 test in high dimensional test based on the minimum regularized covariance determinant estimators.
This function needs the q and d values. These values can be obtained `simRHT2`

function.
For more detailed information, you can see the study by Bulut (2021).

a list with 3 elements:

`T2` |
The Robust Hotelling T^2 value in high dimensional data |

`Fval` |
The F value based on T2 |

`pval` |
The p value based on the approximate F distribution |

Hasan BULUT <hasan.bulut@omu.edu.tr>

Bulut, H (2021). A robust Hotelling test statistic for one sample case in high dimensional data, Communication in Statistics: Theory and Methods.

```
library(rrcov)
data(octane)
mu.clean<-colMeans(octane[-c(25,26,36,37,38,39),])
RHT2(data=octane,mu0=mu.clean,alpha=0.84,d=1396.59,q=1132.99)
```

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