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 个评论
dpb
2018-8-10
What is longhorn?
>> which longhorn
'longhorn' not found.
>>
What do you expect to "change"?
Yaser Khojah
2018-8-10
Jeff Miller
2018-8-11
编辑：Jeff Miller
2018-8-11
What do you mean "nothing changed"? The three columns of LogG have different means and variances, corresponding to what you requested.
Are you unhappy that the frequency distributions are not very skewed? This happens because of the parameters you selected: with some parameters, the lognormal hardly looks skewed at all.
Yaser Khojah
2018-8-13
编辑：Yaser Khojah
2018-8-13
Jeff Miller
2018-8-14
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);
Yaser Khojah
2018-8-14
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