RHT2: Robust Hotelling T^2 Test for One Sample in High Dimensional...

In MVTests: Multivariate Hypothesis Tests

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 <[e-mail]>

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.