clc
clear all
close all
n_s = 1000;
mother_random_variable = lognrnd(0.3,0.5,[1,100000]); %data lognormal
S = mother_random_variable(randi(numel(mother_random_variable),1,n_s)) %sample
S_y = [S]'; %selected data
S_mean=mean(S_y); %mean sample
S_var=std(S_y); %variance sammple
test_cdf = [S_y,cdf('Lognormal',S_y,S_var,S_mean)]; %make cdf
kstest(S_y,'CDF',test_cdf) %ktest
plot(sort(S_y),logncdf(sort(S_y)),'r--')
hold on
cdfplot(S_y)
they have same distribution and ITs srange result . I found more strage result when I compare my data set with itself, Its result shows me they don't have same distribution.
clc
clear all
close all
n_s = 1000;
mother_random_variable = lognrnd(0.3,0.5,[1,100000]); %data
S=mother_random_variable; % I named data with S for simpler code
S_y = [S]'; %selected data
S_mean=mean(S_y);
S_var=std(S_y);
test_cdf = [S_y,cdf('Lognormal',S_y,S_var,S_mean)];
kstest(S_y,'CDF',test_cdf)
plot(sort(S_y),logncdf(sort(S_y)),'r--')
hold on
cdfplot(S_y)
DO you have any Idea.
Prashant Kumar answered . 2024-04-26 10:35:21
y = cdf('Lognormal', S_y, S_var, S_mean); % your code, incorrect
y = cdf('Lognormal', S_y, S_mean, S_var); % correct
2) This is just a suggestion but it's a bit cleaner to use the makedist() function rather than entering the parameters manually into cdf().
doc cdf
pd = makedist('Lognormal', 'mu', S_mean, 'sigma', S_var);
y = cdf(pd, S_y); % instead of cdf('Lognormal', S_y, S_mean, S_var)
3) " when I compare my data set with itself, Its result shows me they don't have same distribution." But you aren't comparing your data with itself. You're comparing your data with the results of the cumulative distribution function of your data. The plot below shows the distribution of values from your data (top) and the distribution of values from the CDF. Clearly those distributions differ and the kstest() correctly rejects the null hypothesis.
figure
subplot(2,1,1)
histogram(S_y)
title('mother random variable')
subplot(2,1,2)
histogram(cdf('Lognormal', S_y, S_mean, S_var))
title('CDF distribution')
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