Hi,
-- I read in distributions.jl documentation that it is possible to fit a distribution to a given set of samples using : d = fit(D, sample) I have a set of (X,Y) values and I would like to determine if the distribution of these values is Gaussian and to have a goodness of fit or Pvalue to accept or reject the hypothesis that the distribution is Gaussian. 1- First of all, in the equation d = fit(Normal, sample), is is unclear for me how sample should be organised : can I concatenate the X and Y vectors like that : sample = [X Y] ? 2- params(d) does not give goodness-of-fit, how it is possible to have this information ? Many thanks for your comments ! You received this message because you are subscribed to the Google Groups "julia-stats" group. To unsubscribe from this group and stop receiving emails from it, send an email to [hidden email]. For more options, visit https://groups.google.com/d/optout. |
The `D` in the code snippet could be replaced with `MultivariateNormal`. `sample` should have a row for `X` and a row for `Y`. This could be arranged using `sample = [X'; Y']`.
-- The resulting fit is a 2D Gaussian, with a mean for X and Y and the correlations. If this is not the desired fit, perhaps an Ordinary Least Squares would do the job. Hope this helps. On Wednesday, April 13, 2016 at 4:12:30 PM UTC+3, Fred wrote:
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Thank you very much Dan ! I tried with a big array and I had an error :
So I created a simple example :
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