Zoraida <zmorales <at> ingellicom.com> writes:
>
> I need to estimate the parameters for negative binomial distribution (pdf)
> using maximun likelihood, I also need to estimate the parameter for the
> Poisson by ML, which can be done by hand, but later I need to conduct a
> likelihood ratio test between these two distributions and I don't know how
> to start! I'm not an expert programmer in R. Please help
It sounds like you might need some local help. If you're trying
to fit the parameters to a single data set (i.e. no predictor variables,
just a set of values), then you probably want fitdistr() from the MASS
package:
modified from ?fitdistr:
library(MASS)
set.seed(123)
x4 <- rnegbin(500, mu = 5, theta = 4)
ff <- fitdistr(x4, "Negative Binomial")
ff2 <- fitdistr(x4, "Poisson")
ff
size mu
4.2159071 4.9447685
(0.5043658) (0.1466082)
ff2
lambda
4.94400000
(0.09943842)
logLik(ff)
'log Lik.' -1250.121 (df=2)
logLik(ff2)
'log Lik.' -1350.088 (df=1)
You can use the pchisq() function to compute the p-value
for the likelihood ratio test (hint: use lower.tail=FALSE
to compute the upper tail area ...)
If you want to fit and compare negative binomial or Poisson models
with covariates, use glm and MASS::glm.nb, or mle2 from
the bbmle packages ...
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