I believe this is more a question for SO (stats.stackexchange.com).
There are many possible goodness of fit statistics that can easily be
calculated in R, but I think the fundamental question is: To what end?
First, there are probably several parametric distributions that give
(essentially) equally good fits; and second, you may want none of
them, preferring some sort of nonparametric fit. Again, the sort of
thing that is probably better at SO -- or even better, with a local
statistician.
Cheers,
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
H. Gilbert Welch
On Mon, Feb 10, 2014 at 12:25 AM, Alaios <alaios at yahoo.com>
wrote:> Hi all,
> I have a large number of measurements from which I select a large number of
unique vectors. For each vectors I would like to test which distribution might
be a candidate for fitting.
> It is impossible to look on each vector separately but I can inside a for
loop test different models and based on their goodness of fit to make offline
decisions (I will be saving goodness of fits results on a text file).
>
> Do you know given a vector how I can get the goodness of fit for the
"basic" distributions : "norm", "lnorm",
"pois", "exp", "gamma", "nbinom",
> "geom", "beta", "unif" and "logis"
>
> Is it possible to try many of those (or at least some of the above) and try
to get these results?
>
> Regards
> Alex
> [[alternative HTML version deleted]]
>
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