On Thu, 2 Jun 2011, geojs wrote:
Thanks for the quick reply,
I understand that the predict(zip1A, type = "response") command is computing
the fitted_means and these are different than the probabilities
predict(zip1A, type = "prob").
Yes. One evaluates the probability density function, and the other one the
expectation from this density.
Although, according to Martin (2005), the highest probabilities do not
simply lead to the true count estimates: "to get the true estimate of
relative mean abundance from the ZIP one must multiply the estimated
relative mean number of individuals at a site by the probability that
the relative mean number of individuals at a site is generated through a
Poisson distribution."
I haven't checked that paper but I suspect that this is a verbal
description of the fitted mean of a zero-inflated Poisson distribution.
See Equation 8 in the JSS paper that introduces hurdle/zeroinfl.
I initially thought that the predicted mean and the observed count could
be compared to estimate the fit of the model,
They can be. But then - not surprisingly - you only assess the fit of the
mean. (E.g., you do not assess the ability to predict the number of zeros,
and you do not assess potential overdispersion etc.)
Z
but now I am not sure what to think with Martin (2005) statement.
Thank you for your help,
JM
Martin, T.G. et al. (2005) Zero tolerance ecology: improving ecological
inference by modelling the source of zero observations, Ecology Letters,
Volume 8, Issue 11, pages 1235?1246.
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