| Type: | Package |
| Title: | An Optimal Subset Selection for Distributed Hypothesis Testing |
| Version: | 0.2.0 |
| Maintainer: | Guangbao Guo <ggb11111111@163.com> |
| Description: | In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>. |
| License: | Apache License (== 2.0) |
| Depends: | R (≥ 3.5.0) |
| Encoding: | UTF-8 |
| Imports: | MASS,stats |
| NeedsCompilation: | no |
| RoxygenNote: | 7.3.1 |
| Packaged: | 2024-07-06 14:45:07 UTC; LJR |
| Author: | Guangbao Guo [aut, cre, cph], Jiarui Li [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2024-07-08 11:00:06 UTC |
An Optimal Subset Selection for Distributed Hypothesis Testing
Description
We introduce an optimal subset selection for distributed hypothesis testing called as PPCDT.
Usage
PPCDT(X,Y,alpha,K)
Arguments
X |
A independent variable |
Y |
The response variable |
alpha |
Significance level |
K |
The number of blocks into which variable X is divided |
Value
Xopt |
optimal subset of selected independent variables |
Yopt |
optimal subset of selected response variables |
Bopt |
Regression coefficients |
Eopt |
The Mean Squared Error of optimal subset |
Aopt |
The Mean Absolute Error of optimal subset |
Author(s)
Guangbao Guo, Jiarui Li
Examples
alpha=0.05
t=5;K=10;n=1000;p=5
X=matrix(rnorm(n*p,0,1),ncol=p)
beta=matrix(runif(p),nrow = p)
esp=matrix(rnorm(n),nrow = n)
Y=X%*%beta+esp
PPCDT(X=X,Y=Y,alpha=alpha,K=K)