# RHT2: Robust Hotelling T^2 Test for One Sample in High Dimensional... In MVTests: Multivariate Hypothesis Tests

 RHT2 R Documentation

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

### Description

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

### Usage

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

### Arguments

 `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).

### Details

`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).

### Value

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

### Author(s)

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

### References

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

### Examples

``````
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)
``````

MVTests documentation built on Nov. 3, 2023, 5:11 p.m.