MATLAB: Quantifying fit of data to distribution

curve fittinghistogram

I have a vector of data and want to see which distribution fits it best. I can fitdist to find the parameters of the closest fit, to a particular distribution. But after that, how do I compare the fit from two different distributions and see which is a better fit?

Best Answer

You can do it visually like this: documentation. Alternatively, try fitmethis on FileExchange. I think it computes the likelihood of the data under each fitted distribution and indicates the one under which the data have the highest likelihood.

Related Solutions

MATLAB: Quantify difference between discrete distributions

Use the Two-sample Kolmogorov-Smirnov test from the Statistics Toolbox.

MATLAB: Evaluate a distribution programmatically

The fitdist (link) function will fit a distribution to data, producing a probability distribution object:

pd = fitdist(x,distname)

You can then calculate the probabilities with it using the Plot Standard Normal Distribution cdf (link) or Plot the pdf of a Standard Normal Distribution (link) functions:

data = 5 + 2*randn(1, 100);                         % Create Data
pd = fitdist(data(:), 'Normal');
x = linspace(-2, 12);
Npdf = pdf(pd, x);
Ncdf = cdf(pd, x);
figure
plot(x, Npdf,   x, Ncdf)
grid

Best Answer

Related Solutions

MATLAB: Quantify difference between discrete distributions

MATLAB: Evaluate a distribution programmatically

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