Creating Random Log Normal Distribution
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I'm a bit confused with converting a normal distribution to a log normal and then creating random numbers. I'm not sure if what I'm doing is right or not?
For example, I have the following:
row = 1000;
G = [14000000 3600000 10000000];
Mean = G;
Variance = Mean .* 0.5;
mu = log(Mean.^2./sqrt(Variance+Mean.^2));
sigma = sqrt(log(1+Variance./Mean.^2));
GIP = zeros(row,length(G));
for i = 1:length(G)
R = longhorn(mu(i),sigma(i),[row,1]);
GIP(:,i) = R;
end
LogG = GIP;
The things with this nothing changed. However, if I converted G to G = G/10^6. Then, it will work but I have to convert LogG later to LogG = LogG * 10^6. I do not know why this happens. Does that because the number is too big? please help.
6 Kommentare
Jeff Miller
am 14 Aug. 2018
If you use (1), you will get lognormal random numbers. If you use (2), you will get normal random numbers. Another way to get lognormal random numbers is to use:
R = exp(normrnd(mu(i),sigma(i),row,1));
You should check these to make sure you are getting what you think you are getting:
meanR = mean(R)
stdR = std(R)
figure; histogram(R);
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