Applying a positive/negative constraint to the coefficients of a quantile regression

Question

In the package quantreg one can perform a penalized quantile regression. Selecting the variables that were deemed statistically significant is "easy". However, when I considered applying a restrain to the coefficients: i.e some to be strictly positive/negative (otherwise they will be zero), I just couldn't figure out how it was done! The code i have so far is this:

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
  }
}

I fear that this problem is very obvious. I had considered using ifelse(eq$coef<0,0,eq$coef) but given that a few variables are restrained either positive or negative that wasn't the ideal solution. Any ideas?

EDIT: Something I forgot to include, is that each iteration selects a (maybe) different variable(s) than the previous iteration!

Answer 1

Adding

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)

right before the predictions are made, solves the issue.