对分位数回归的系数应用正/负约束

Question

在包quantreg 中可以执行惩罚分位数回归。选择被认为具有统计学意义的变量是“容易的”。但是，当我考虑对系数施加限制时：即一些严格为正/负（否则它们将为零），我只是无法弄清楚它是如何完成的！我到目前为止的代码是这样的：

quant<-c(0.4,0.5,0.6)

for (t in 400:600){     #the first 400 rows are the trainset, the remaining the test set. In each iteration
  x=X[1:399,]           #we increase the trainset by 1row and use it to predict for the next.
  y=Y[1:399]
  for (i in 1:quant) {
    eq=rqss(y~x,method="lasso",tau=quant[i],lambda=lambdas) #find the significant variable though a Lasso quantile.
    s=summary(eq)
    findsigPV=s$coef[2:28,4] #select the stat. significant coefficient/variable
    selectedPV=findsigPV<=0.05
    if (sum(selectedPV)==0){
      SelectedPV=rank(findsigPV)==1
    }
    newx=as.matrix(subset(X[1:t,],select=which(selectedPV))) #new matrix with the selected variable
    eq=rq(y~newx[1:(t-1),],tau=quant[i])  #applies the new q. regression with the selected coeff from the lasso
    pr[t-400+1,i]=c(1,newx[t,])%*%eq$coef #saves the forecast
  }
}

我担心这个问题很明显。我曾考虑使用ifelse(eq$coef<0,0,eq$coef)，但考虑到一些变量被限制为正或负，这不是理想的解决方案。有什么想法吗？

编辑：我忘了包括，每次迭代选择一个（可能）与前一次迭代不同的变量！

Answer 1

添加

j=2
    for (k in 1:23){

      if (II[k]){ 
        if (k <=12){  #positive constraint to the first 12 variables lets say
          if (eq$coeff[j] <0){
            eq$coeff[j] =0}
          j=j+1}
        if (k > 12){ #negative constraint to the remaining ones
          if (eq$coeff[j] >0){
            eq$coeff[j] =0}
          j=j+1}  
      }
    }
    print(eq$coeff)

在做出预测之前，解决问题。