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.
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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