So I have a non-standard (non-normal, non-Gaussian, non-exponential, etc...) distribution, so I created a distribution empirically:
Now I want to find out how well another set of data points fits the distribution of the first. It does not match exactly, so I want to get a value for how well it fits or not. However, I can't even get data from the PDF to fit itself...
So consider this: I create the PDF from MyData, and then generate 1,000,000 points randomly from this PDF, and compare it to the PDF itself:
test1 = RandomVariate[skdData, 1000000]; KolmogorovSmirnovTest[test1, skdData] DistributionFitTest[test1, skdData]
This returns anything from 0.1 to 0.9, which blows my mind, especially for the second result. So... Obviously (to me) I'm doing something wrong, because this is clearly a nearly perfect fit:
Can anyone help me get the correct information from these fits? Chi-squared or P-Values are what I'm really after here... or whatever you think is a better estimator of fit.
My guess is that these functions are looking for normal distributions and are failing to use the one I give it, OR I have no idea what I'm doing...