Dear Jacob,
May be you can use cluster sampling or adaptive cluster sampling
(Design-based estimation) to get a density estimate.
Best,
Manuel Spínola
Capelle, Jacob wrote:
Dear all,
I have a kind of a theoretical question from which I hope it might interest you and hopefully can help me
Jacob- You can use a Chi-squared goodness of fit - chisq.test() for discrete
distributions like the negative binomial and a Kolmogorov-Smirnoff test-
ks.test() for continuous distributions. They will both produce a p-value
which tests the null hypothesis that your data come from the given
Dear Jacob,
Erika was right, you just have to perform a goodness of fit test. Bit
it is easier
to inspect your residual deviance.
It follows a Chi-sqared distribution, where the expected value should
be close to
the degrees of freedom if the fit is good. To get a P value for an
object of class
