R 对分位数回归的系数应用正/负约束
在包R 对分位数回归的系数应用正/负约束,r,constraints,quantile,quantreg,R,Constraints,Quantile,Quantreg,在包quantreg中,可以执行惩罚分位数回归。选择被认为具有统计意义的变量是“容易的”。然而,当我考虑对系数应用一个约束时:即一些系数严格为正/负(否则它们将为零),我就是不知道它是如何实现的!到目前为止,我掌握的代码如下: 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
quantreg
中,可以执行惩罚分位数回归。选择被认为具有统计意义的变量是“容易的”。然而,当我考虑对系数应用一个约束时:即一些系数严格为正/负(否则它们将为零),我就是不知道它是如何实现的!到目前为止,我掌握的代码如下:
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
}
}
quant添加
j=2
(1:23中的k){
如果(II[k]){
if(k0){
等式$coeff[j]=0}
j=j+1}
}
}
打印(eq$coeff)
就在做出预测之前,解决了这个问题
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)