I tried the function flexmix() with the driver FLXMRnegbin() with two
components first, in order to compare its results with those provided
by my function mixnbinom(). In particular, I ran the following code:
fm0 <- flexmix(y ~ 1, data = data.frame(y), k = 2, model = FLXMRnegbin())
where "y" is my vector of counts. The previous function provided me
the following parameters:
Comp.1 Comp.2
coef.(Intercept) 1.2746536 1.788578
theta 0.1418201 5.028766
with priors 0.342874 and 0.657126, respectively. I assume that the
coefficients "Intercept" represent the two means of the model (mu1 and
mu2), while the "theta" coefficients are the size parameters (size1
and size2).
Unfortunately, unlike my function mixnbinom(), the model computed with
flexmix() did not provide a good fit to my data (p-value ~0).
Is there something wrong in the process above?
Achim Zeileis <achim.zeil...@uibk.ac.at> ha scritto:
On Mon, 7 Nov 2016, danilo.car...@uniparthenope.it wrote:
I need a function for R software which computes a mixture of Negative
Binomial distributions with at least three components.
The package "countreg" on R-Forge provides a driver FLXMRnegbin()
that can be combined with the "flexmix" package (i.e., functions
flexmix() and stepFlexmix()). The manual page provides some worked
illustrations in example("FLXMRnegbin", package = "countreg").
Note that the driver is mainly designed for negative binomial
_regression_ models. But if you just regress on a constant (y ~ 1)
you can also get negative binomial mixture distributions without
covariates.
-------------------------------------------------------------
Danilo CaritÃ
PhD Candidate
University of Naples "Parthenope"
Dipartimento di Studi Aziendali e Quantitativi
via G. Parisi, 13, 80132 Napoli - Italy
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