For the sake of asking here assume I have 5 models that attempt to predict a pair of future values like tomorrow's high & low. The predictions are placed in a matrix with the low prediction in column 1 and the high prediction in column 2. _IF_ there is an intersection of all 5 predictions then I can test for that using the simple equation in Range1. However if there is a prediction that's a complete outlier as is Pred2[4,] then the simple equation fails and says the high is less than the low.
Are there any R packages that automatically handle simple intersection problems like this? My real goals include things like probability distributions across the ranges, but for now I'm just looking for what's out there? Thanks, Mark Pred1 = matrix(data=NA, nrow=5, ncol=2) Pred1[1,] = c(100,110) Pred1[2,] = c(88,125) Pred1[3,] = c(92,120) Pred1[4,] = c(25,105) Pred1[5,] = c(91,121) Range1 = c(max(Pred1[,1]), min(Pred1[,2])) Range1 Pred2 = matrix(data=NA, nrow=5, ncol=2) Pred2[1,] = c(100,110) Pred2[2,] = c(88,125) Pred2[3,] = c(92,120) Pred2[4,] = c(25,70) #outlier - the high is too low Pred2[5,] = c(91,121) Range2 = c(max(Pred2[,1]), min(Pred2[,2])) Range2 _______________________________________________ R-SIG-Finance@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.