DLPCA {DLPCA}

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)