I'm trying to simulate the loss of the highest values in a dataset. My end goal is to create a new array that matches the mean of observed data — i.e. I need to calculate means for modeled values in cells 1-2, 1-3, 1-4… 1-n until I find the range of modeled values with a mean that matches my observed data. Right now, I'm trying to work with a for loop that will terminate once a mean threshold is met — when that threshold is met, I would like to create a new array (rh2 in my code) that incorporates the cells used to calculate the modeled mean that best matches my observed mean. My code seems giving me output of a modeled mean that matches my observed mean, but not the output of the array that I need. Thanks!
meanr2 = mean(ruber2);ko = fitdist(ruber1,'kernel');ruber = ko.random(500,1);sort_ruber = sort(ruber);for k=1:500 mur = mean(sort_ruber(1:k)); if mur <= meanr2 rh2 = ruber(1:k); elseif mur > meanr2 return endend
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