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#' Calculate the MSE values of the DLPCA method
#'
#' @param V is the right singular matrix
#' @param X is the original data matrix
#' @param n is the sample size
#' @param p is the number of variables
#' @param m is the number of eigenvalues
#' @param K is the number of nodes
#' @param L is the number of subgroups
#' @return MSEpca
#' @export
#'
#' @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)
#' V=DLPCA_result$V
#' MSEpca_result=MSEpca(V=V,X=X,n=n,p=p,m=m,K=K,L=L)
#' MSE_PCA=MSEpca_result$MSEpca
MSEpca <-
function(V=V,X=X,n=n,p=p,m=m,K=K,L=L){
B=V%*%t(V)%*%t(X)
U=NULL;Q=NULL;MSEE=NULL
for(i in 1:n){
for(j in 1:p){
E=t(V[,(m+1):p])%*%B[,i]
U[j]=(V[j,(m+1):p]%*%E)^2
}
Q[i]=sum(U)
}
L=rep(1,1,n);
nk=ceiling(n/K)
for (k in 1:K){
SSE=sum(Q[(((k-1)*nk)+(1:nk))])
MSE=(1/(p*nk))*SSE
MSEE[k]=MSE
}
MSEpca=min(MSEE)
return(list(MSEpca=MSEpca))
}
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