## Linear regression, where p>nlibrary(SSLASSO)n=100p=1000X=matrix(rnorm(n*p), n, p)beta=c(1,2,3,rep(0,p-3))Y=X[,1]*beta[1]+X[,2]*beta[2]+X[,3]*beta[3]+rnorm(n)lambda1<-0.1lambda0<-seq(lambda1,100,length=50)theta<-0.5# Separable penalty with fixed thetaresult<-SSLASSO(X, Y,penalty="separable", variance ="fixed",lambda1 = lambda1, lambda0 = lambda0,theta=theta)plot(result)