On Thu, 10 Nov 2016, danilo.car...@uniparthenope.it wrote:
Thank you for your hints, now the goodness of fit test provides me good
results, but surprisingly for me the three-component model turns out to be
worse than the two-component one (indeed, I focused on the three-component
mixture because the two-component one exhibits a low p-value).
In addition, I have noticed that for some data the function fails to find
good starting values, as you have mentioned in your previuous answer. The
problem is that the driver FLXMRnegbin() allows to specify only the theta
parameter (and only one value, even in the event of mixtures of two or more
components).
I have read the description of flexmix() function too, but it seems that it
does not allow to set starting values for the parameters of the model. Am I
right? Or is there a way to do it?
No, I don't think so. You could look at the FLXMRnegbin() driver code and
tweak this directly to use better starting values etc. But I think that
the main issue is that the "theta" parameter often diverges to infinity
(i.e., a Poisson distribution) if theta is too large in (at least) one of
the components.
Given that the negbin distribution is a continuous mixture of Poisson
distributions, I'm not sure whether approaching such data with a finite
mixture of such continuous mixtures.
What to do with this situation, certainly depends on the data you have and
the questions you have about it. And I concur with Bert's advice of
contacting a local statistics expert for discussion of such issues.
hth,
Z
Achim Zeileis <achim.zeil...@uibk.ac.at> ha scritto:
On Tue, 8 Nov 2016, danilo.car...@uniparthenope.it wrote:
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),
No, a log link is employed, i.e., exp(1.2746536) and exp(1.788578) are the
means.
while the "theta" coefficients are the size parameters (size1 and size2).
Yes.
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?
Hard to say without a reproducible example. Using parameter values similar
to the ones you cite above, the following seems to do a reasonable job:
## packages
library("countreg")
library("flexmix")
## artificial data from two NB distributions:
## 1/3 is NB(mu = 3.5, theta = 0.2) and
## 2/3 is NB(mu = 6.0, theta = 5.0)
set.seed(1)
y <- c(rnbinom(200, mu = 3.5, size = 0.2), rnbinom(400, mu = 6, size = 5))
## fit 2-component mixture model
set.seed(1)
fm <- flexmix(y ~ 1, k = 2, model = FLXMRnegbin())
## inspect estimated parameters -> look acceptable
parameters(fm)
exp(parameters(fm)[1,])
My experience was that finding good starting values may be a problem for
flexmix(). So maybe setting these in some better way would be beneficial.
-------------------------------------------------------------
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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