On Tue, 2 Mar 2010, casperyc wrote:
[code]library(MASS)
x=c(rep(0,8096),
rep(1,1629),
rep(2,233),
rep(3,38),
rep(4,4)
)
x.bar=round(mean(x),4)
x.var=round(var(x),4)
p.hat=round(x.bar/x.var,4)
alpha.hat=round(x.bar*p.hat/(1-p.hat),4)
fitdistr(x, "Negative Binomial")
fitdistr(x, "Poisson")[/code]
1- fitdistr(x, "Negative Binomial")
the parameters got here, is it for negative binomial type 2?
Yes.
how can i ask it to use the methods of moments to calculat the
parameters?
You can't with fitdist()
(p.hat and alpha.hat which i derived from methods of moments seem to give
a different value)
It is a different parametrization of NB2 that you are using. Your
alpha.hat corresponds to "size" and p.hat corresponds to "prob" in
?dnbinom. However, fitdistr() uses the "mu" parametrization, i.e.
R> alpha.hat * (1 - p.hat) / p.hat
[1] 0.222497
which roughly matches the ML estimate for mu from fitdistr().
2-how can i fit it and than test the goodness of it?
You could use goodfit() from "vcd":
gf_pois <- goodfit(x, type = "poisson")
plot(gf_pois)
summary(gf_pois)
gf_nbin <- goodfit(x, type = "nbinom")
plot(gf_nbin)
summary(gf_nbin)
which shows that the fit from the NB is satisfactory while the fit from
the Poisson is not.
hth,
Z
3-then compare with the Poisson model?
Thanks
===============================================
by negative binomial type two i mean
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