| Title: | Robust Test for Complete Independence in High-Dimensions |
|---|---|
| Description: | Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. So, the Bulut's statistic can be performed robustly by using RDnp function. |
| Authors: | Hasan BULUT [aut,cre] |
| Maintainer: | Hasan BULUT <[email protected]> |
| License: | GPL-2 |
| Version: | 1.3 |
| Built: | 2025-02-22 03:24:20 UTC |
| Source: | https://github.com/cran/RDnp |
A Test Statistic for Independence in High-Dimensional Datasets
Dnp_Test(X)Dnp_Test(X)
X |
the data. It must be matrix. |
Dnp_Test function tests the complete independence in high-dimensional
data sets. This statistic was proposed by Najarzadeh (2021).
a list with 2 elements:
TestValue |
The value of test statistic |
pval |
The p value |
robust |
Logical. Indicates whether the results are based on robust
statistic. Here, it returns |
Hasan BULUT <[email protected]>
Najarzadeg, D (2021). Testing independece in high-dimensional multivariate normal data, Communication in Statistics: Theory and Methods. 50 (14): 3421-3435.
# Under H0 library(MASS) data_H0<-mvrnorm(n = 20,mu = rep(0,30),Sigma = diag(30)) Dnp_Test(data_H0) # Under H1 library(MASS) data_H1<-mvrnorm(n = 20,mu = rep(0,30),Sigma = (diag(30)+1)) Dnp_Test(data_H1)# Under H0 library(MASS) data_H0<-mvrnorm(n = 20,mu = rep(0,30),Sigma = diag(30)) Dnp_Test(data_H0) # Under H1 library(MASS) data_H1<-mvrnorm(n = 20,mu = rep(0,30),Sigma = (diag(30)+1)) Dnp_Test(data_H1)
A Robust Test Statistic for Independence in High-Dimensional Datasets
RDnp_Test(X, alpha = 0.75)RDnp_Test(X, alpha = 0.75)
X |
the data. It must be matrix. |
alpha |
numeric parameter. It gives the rate of uncontaminated observations. Allowed values are between 0.5 and 1 and the default is 0.75. |
RDnp_Test function tests the complete independence in high-dimensional
data sets without being affected by outliers.
a list with 2 elements:
TestValue |
The value of test statistic |
pval |
The p value |
robust |
Logical. Indicates whether the results are based on robust
statistic. Here, it returns |
Hasan BULUT <[email protected]>
Bulut, H (Unpublished). A Robust Test Statistic for Independence in High Dimensional Data
# Under H0 library(MASS) data_H0<-mvrnorm(n = 20,mu = rep(0,30),Sigma = diag(30)) RDnp_Test(data_H0) # Under H1 library(MASS) data_H1<-mvrnorm(n = 20,mu = rep(0,30),Sigma = (diag(30)+1)) RDnp_Test(data_H1)# Under H0 library(MASS) data_H0<-mvrnorm(n = 20,mu = rep(0,30),Sigma = diag(30)) RDnp_Test(data_H0) # Under H1 library(MASS) data_H1<-mvrnorm(n = 20,mu = rep(0,30),Sigma = (diag(30)+1)) RDnp_Test(data_H1)