r - 对分位数回归的系数应用正/负约束

Question

在包中quantreg可以执行惩罚分位数回归。选择被认为具有统计显着性的变量是“容易的”。然而，当我考虑对系数应用限制时:即某些系数严格为正/负(否则它们将为零)，我只是不知道它是如何完成的!到目前为止我的代码是这样的:

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
  }
}

恐怕这个问题是非常明显的。我曾考虑过使用 ifelse(eq$coef<0,0,eq$coef)但考虑到一些变量受到积极或消极的限制，这不是理想的解决方案。有什么想法吗？

编辑:我忘记包括的一点是，每次迭代都会选择一个(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。