DLPCA: Distributed local PCA

In DLPCA: The Distributed Local PCA Algorithm

Distributed local PCA

Description

Calculate the estimator on the DLPCA method

Usage

DLPCA(X = X, n = n, p = p, m = m, K = K, L = L)

Arguments

X	is the original data matrix
n	is the sample size
p	is the number of variables
m	is the number of eigenvalues
K	is the number of nodes
L	is the number of subgroups

Value

time	is the time cost
V	is the right singular matrix
Vm	is the m-right singular matrix
Smean	is the mean covariance matrix
MMSER	is the mean MSE values of the robust covariance matrix sub-estimators
MMSES	is the mean MSE values of the covariance matrix sub-estimators
MMSEX	is the mean MSE values of the sub-estimators of the matrix X
MSER	is the min MSE values of the robust covariance matrix sub-estimators
MSES	is the min MSE values of the covariance matrix sub-estimators
MSEX	is the min MSE values of the sub-estimators of the matrix X
wMSER	is the location of the min MSE values of the robust covariance matrix sub-estimators
wMSES	is the location of the min MSE values of the covariance matrix sub-estimators
wMSEX	is the location of the min MSE values of the sub-estimators of the matrix X
sigm	is the estimator of the covariance matrix of the matrix X

Examples

data(Application)
X=Application
n=nrow(Application);p=ncol(Application)
m=5;L=4;K=4
DLPCA_result=DLPCA(X=X,n=n,p=p,m=m,K=K,L=L)        

DLPCA documentation built on Aug. 7, 2022, 9:05 a.m.