Nothing
fstep.sparse <-
function(X,T,lambda,nbit,l2){
if (length(lambda)>1) stop('The user needs to enter a single figure comprised between 0 and 1')
# require('lars')
# require('elasticnet')
# Initialization
K = ncol(T)
p = ncol(X)
d = min(p-1,(K-1))
m = matrix(NA,K,p)
# Compute summary statistics
Xbar = colMeans(X)
n = colSums(T)
for (k in 1:K){ m[k,] = colSums((as.matrix(T[,k]) %*% matrix(1,1,p))* X) / n[k] }
# Matrices Hb and Hw
Hb = as.matrix(sqrt(n) * (m - matrix(1,K,1) %*% Xbar))
Hw = X - t(apply(T,1,'%*%',m))
# Cholesky decomposition of t(Hw) %*% Hw
if (nrow(X)>p) Rw = chol(t(Hw)%*%Hw) else {
gamma = 0.5
Rw = chol(t(Hw)%*%Hw + gamma*diag(p))}
# LASSO & SVD
Binit = eigen(ginv(cov(X))%*%(t(Hb)%*%Hb))$vect[,1:d]
if (is.complex(Binit)) Binit = matrix(Re(Binit),ncol=d,byrow=F)
if (is.null(dim(Binit))) B = matrix(Binit) else B=Binit
res.svd = svd(t(ginv(Rw))%*%t(Hb)%*%Hb%*%B)
A = res.svd$u %*% t(res.svd$v)
for (i in 1:nbit){
for (j in 1:d){
W = rbind(Hb,sqrt(lambda)*Rw)
y = rbind(Hb %*% ginv(Rw) %*% A[,j],matrix(0,p,1))
# res.lasso = lars(W,y,intercept=FALSE)
# B[,j] = coef(res.lasso,mode="fraction",s=lambda)
# if (n<=p) {l2 = 1}
res.enet = enet(W,y,lambda=l2,intercept=FALSE)
B[,j] = predict.enet(res.enet,X,type="coefficients",mode="fraction",s=lambda)$coef
}
normtemp = sqrt(apply(B^2, 2, sum))
normtemp[normtemp == 0] = 1
Beta = t(t(B)/normtemp)
res.svd = svd(t(ginv(Rw))%*%t(Hb)%*%Hb%*%Beta)
A = res.svd$u %*% t(res.svd$v)
Beta = svd(Beta)$u
# Beta = qr.Q(qr(Beta))
}
# return the sparse loadings
as.matrix(Beta)
}
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