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December 8th, 2016, 12:23 PM  #1 
Newbie Joined: Dec 2016 From: España Posts: 2 Thanks: 0  Transforming (Normalizing) probability data.
I need some help finding a function to transform my data. I have raster files with the probability to find a certain plant species as unsigned 8 bit integers, that means: Value 0 is 0% chance to find the species, Value 255 means 100% to find the species. So far everything is right (If I want to have percentages I just have to divide). The problem comes when the cutoff (the optimun value, that represent 50% chance) is 170 (not 127). I am looking for a function to normalize the data since for each species the cutoff value is different (ranging from 40 to 230). To make my question clear, I need a function that obeys the following: 0 < f(0) 50 < f(cutoff) 100 < f(255) Every other value should be transformed accordingly to the cuttoff. Thank you for trying to help! 
December 8th, 2016, 02:17 PM  #2 
Newbie Joined: Dec 2016 From: España Posts: 2 Thanks: 0 
I think I have done it (R code): normalize < function (value, cutoff){ if (value == 0) { return(0) }else if (value == 255) { return (1) }else if (value == cutoff) { return (0.5) }else if (value < cutoff) { return (value*0.5/cutoff) }else if (value > cutoff) { return (1(255value)*0.5/(255cutoff)) } } If someone have a more elegant answer that be great too, thanks! 

Tags 
data, normalizing, probability, transforming 
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