R/checkbest.R

In robflreg: Robust Functional Linear Regression

checkbest <-
function(y,x,approximate, rhoOpt, resrw, bestscales, besttauscales, c1,c2,b1, bestbetas, Mscale, scalerw, rr, rworst, worsti, betarw){
  if (!approximate) {
    scaletest1 <- mean(rhoOpt(resrw / bestscales[worsti],c1)) < b1
    scaletest2 <- sum(rhoOpt(resrw / bestscales[worsti],c2)) < sum(rhoOpt(rworst/bestscales[worsti],c2))
    if (scaletest1 || scaletest2) {
      snew <- Mscale(resrw, b1, c1, scalerw)
      computeTAU<- TRUE
    }
    else {computeTAU<- FALSE}
  }
  else { # or just compute approximations (and don't bother with the conditions)
    snew <- scalerw
    computeTAU<- TRUE
    if (rr>0) {
      for (kstep in 1:rr) {
        snew <- sqrt( snew^2 * mean( rhoOpt(resrw/snew,c1) ) / b1 )
      }
    }
  }
  if(computeTAU) {
    taunew <- snew * sqrt(mean(rhoOpt(resrw/snew,c2)))
    if (taunew < besttauscales[worsti]) {
      # if candidate has indeed better tau than the worst of the tt best until now, keep it. 
      besttauscales[worsti] <- taunew
      bestscales[worsti] <- snew
      bestbetas[,worsti] <- betarw
      worsti <- which.max(besttauscales)
      rworst <- y - x %*% bestbetas[,worsti]
    }
  }
  results<- list(bestscales=bestscales, besttauscales=besttauscales, bestbetas=bestbetas, rworst=rworst, worsti=worsti)
  return(results)
}