, Congfan Zhang [aut]| Title: | Distributed Online Covariance Matrix Tests |
|---|---|
| Description: | Distributed Online Covariance Matrix Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform covariance matrix tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, 'Docovt' ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for covariance matrix estimation and hypothesis testing. The philosophy of 'Docovt' is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>. |
| Authors: | Guangbao Guo [aut, cre]
, Congfan Zhang [aut] |
| Maintainer: | Guangbao Guo <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1 |
| Built: | 2025-02-28 05:27:34 UTC |
| Source: | https://github.com/cran/Docovt |
Given data, it performs 1-sample test for Covariance where the null hypothesis is
where is the covariance of data model and is a
hypothesized covariance based on a procedure proposed by Cai and Ma (2013).
cm13(X,Sigma0, alpha)cm13(X,Sigma0, alpha)
X |
an |
Sigma0 |
a |
alpha |
level of significance. |
a named list containing:
a test statistic value.
rejection criterion to be compared against test statistic.
a logical; TRUE to reject null hypothesis, FALSE otherwise.
## generate data from multivariate normal with trivial covariance. p = 5;n=10 X=data = matrix(rnorm(n*p), ncol=p) alpha=0.05 Sigma0=diag(ncol(X)) cm13(X,Sigma0, alpha)## generate data from multivariate normal with trivial covariance. p = 5;n=10 X=data = matrix(rnorm(n*p), ncol=p) alpha=0.05 Sigma0=diag(ncol(X)) cm13(X,Sigma0, alpha)
Given two sets of data, it performs 2-sample test for equality of covariance matrices where the null hypothesis is
where and represent true (unknown) covariance
for each dataset based on a procedure proposed by Cai and Ma (2013).
If statistic threshold, it rejects null hypothesis.
cmtwo(X, Y, alpha)cmtwo(X, Y, alpha)
X |
an |
Y |
an |
alpha |
level of significance. |
a named list containing
a test statistic value.
rejection criterion to be compared against test statistic.
a logical; TRUE to reject null hypothesis, FALSE otherwise.
## generate 2 datasets from multivariate normal with identical covariance. p= 5; n1 = 100; n2 = 150; alpha=0.05 X=data1 = matrix(rnorm(n1*p), ncol=p) Y=data2 = matrix(rnorm(n2*p), ncol=p) # run test cmtwo(X, Y, alpha)## generate 2 datasets from multivariate normal with identical covariance. p= 5; n1 = 100; n2 = 150; alpha=0.05 X=data1 = matrix(rnorm(n1*p), ncol=p) Y=data2 = matrix(rnorm(n2*p), ncol=p) # run test cmtwo(X, Y, alpha)
Given data, it performs 1-sample test for Covariance where the null hypothesis is
where is the covariance of data model and is a
hypothesized covariance based on a procedure proposed by Srivastava, Yanagihara, and Kubokawa (2014).
syk(data, Sigma0, alpha)syk(data, Sigma0, alpha)
data |
an |
Sigma0 |
a |
alpha |
level of significance. |
a named list containing
a test statistic value.
rejection criterion to be compared against test statistic.
a logical; TRUE to reject null hypothesis, FALSE otherwise.
## generate data from multivariate normal with trivial covariance. p = 5;n=10 data = matrix(rnorm(n*p), ncol=p) alpha=0.05 Sigma0=diag(ncol(data)) ## run the test syk(data, Sigma0, alpha)## generate data from multivariate normal with trivial covariance. p = 5;n=10 data = matrix(rnorm(n*p), ncol=p) alpha=0.05 Sigma0=diag(ncol(data)) ## run the test syk(data, Sigma0, alpha)