| RHT2 {MVTests} | 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)