How to save the results of a nested loop in R

Question

I have a dataset of 15 variables (1 under examination, and 14 its regressors) all numeric. What I do is that i run an algorithm that is a recursive forecasting technique. This algorithm cuts the data in an in-sample and an out-sample. Here I want to figure out how to store the results produced for each value of a and t, which are parameters of the cv.hqreg function (hqreg package).

• Note: That for each value of t and a we get 1 value (the one in the code as predicedQ. For each of those t and a we run the cv.hqreg 648 times. And then again 648 times for the next value of t and a. Thus the ending result will be a matrix/dataset of 648 rows and 231 columns.

For each cv.hqreg I get 100 fitted models from which I select the one with the smallest error via this LMQ$fit$beta[,which(LMQ$lambda.min==LMQ$lambda)] command line.

   dataR<-TRAINSET
    fittedvaluesQRidge<-NULL
        for(i in 1:(nrow(TESTSET)-1)){ #adding a new row and repeat
          for(a in seq(0,1,0.1)){ #for each penalty of selection
            for(t in seq(0,1,0.05)){ #for each quantile
      print(i)                         #to see it works/or where stops
      dataR<-rbind(dataR,TESTSET[i,])  #update dataset
      LMQ<-cv.hqreg(as.matrix(dataR[,-15]),dataR$LHS,method = "quantile",tau=t,alpha = a)  #FIT THE Lasso Quantile-MODEL 
      predictdQR<-LMQ$fit$beta[1,which(LMQ$lambda.min==LMQ$lambda)]+LMQ$fit$beta[2,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,1]+LMQ$fit$beta[3,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,2]+LMQ$fit$beta[4,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,3]+LMQ$fit$beta[5,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,4]+LMQ$fit$beta[6,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,5]+LMQ$fit$beta[7,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,6]+LMQ$fit$beta[8,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,7]+LMQ$fit$beta[9,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,8]+LMQ$fit$beta[10,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,9]+LMQ$fit$beta[11,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,10]+LMQ$fit$beta[12,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,11]+LMQ$fit$beta[13,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,12]+LMQ$fit$beta[14,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,13]+LMQ$fit$beta[15,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,14] #find the forecasts
      fittedvaluesQRidge<-c(fittedvaluesQRidge,predictdQR) #then put them in a vector 
        }
      }
    }

The commands I have used to get the predicted value are quite extensive using each one variable at a time. However I have tried to use matrix algebra (matrix of the covariates %*% data with no results but an error: non-numeric argument to binary operator. It works, in an ugly yes way, but if there is a shorter way I would like all the assistance.

Answer 1

Consider sapply() with expand.grid() as sapply can take multiple input lists or vectors, similar to the nested for loops but returns a matrix. And with expand.grid which cross joins two lists in a data.frame object, you can capture every combination between a and t:

at_combns <- expand.grid(a=seq(0,1,0.05), t=seq(0,1,0.1))

matpredictdQR <- sapply(seq(nrow(at_combns)), function(j, i){
  # UPDATE dataset
  dataR <- rbind(TRAINSET, TESTSET[1:i,])

  # FIT THE Lasso Quantile-MODEL 
  LMQ <- cv.hqreg(as.matrix(dataR[,-15]),dataR$LHS,method = "quantile",
                  tau=at_combns$t[j], alpha=at_combns$a[j]) 

  predictdQR <-LMQ$fit$beta[1,which(LMQ$lambda.min==LMQ$lambda)]+
               LMQ$fit$beta[2,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,1]+
               LMQ$fit$beta[3,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,2]+
               LMQ$fit$beta[4,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,3]+
               LMQ$fit$beta[5,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,4]+
               LMQ$fit$beta[6,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,5]+
               LMQ$fit$beta[7,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,6]+
               LMQ$fit$beta[8,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,7]+
               LMQ$fit$beta[9,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,8]+
               LMQ$fit$beta[10,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,9]+
               LMQ$fit$beta[11,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,10]+
               LMQ$fit$beta[12,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,11]+
               LMQ$fit$beta[13,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,12]+
               LMQ$fit$beta[14,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,13]+
               LMQ$fit$beta[15,which(LMQ$lambda.min==LMQ$lambda)]*TESTSET[i+1,14] 

  return(predictdQR)

}, seq(nrow(TESTSET)-1))