# R/fstep.sparse.R In FisherEM: The Fisher-EM algorithm

#### Defines functions fstep.sparse

```fstep.sparse <-
function(X,T,lambda,nbit,l2){

if (length(lambda)>1) { cat('\n','The user needs to enter a single figure comprised between 0 and 1','\n')
break}
# 	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))
}