Created
April 11, 2012 01:31
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Q-Q plots
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from __future__ import division | |
import numpy as np | |
import scipy.stats as stats | |
from matplotlib import pyplot as plt | |
def plot(data,cdf=stats.norm.cdf): | |
plt.figure() | |
plt.plot(np.cumsum(1./len(data) * np.ones(data.shape)),cdf(np.sort(data)),'bx') | |
plt.plot((0,1),(0,1),'r-') | |
plt.ylim((0,1)) | |
plt.xlim((0,1)) | |
plt.xlabel('empirical percentiles') | |
plt.ylabel('fit percentiles') |
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function qqplot(data,cdf) | |
if ~exist('cdf','var'), cdf=@normcdf; end | |
figure(); | |
hold on | |
plot(cumsum(1/length(data)*ones(size(data))),cdf(sort(data)),'bx'); | |
plot([0,1],[0,1],'r-'); | |
ylim([0,1]); | |
xlim([0,1]); | |
xlabel('empirical percentiles'); | |
ylabel('fit percentiles'); | |
hold off | |
end |
OKAY technically these are P-P plots
This is good. I don't understand, why SciPy doesn't do QQ plots.
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In Matlab, let's generate some data from an exponential
Now how well does the maximum-likelihood fit exponential distribution fit the data?
What if we had a lot more data?
What if we fit some totally bogus distributions?