[R] Negative Binomial Predictions
Achim.Zeileis at wu-wien.ac.at
Wed Oct 1 18:55:15 CEST 2008
On Wed, 1 Oct 2008, Donald Catanzaro, PhD wrote:
> Good Day All,
> I have a negative binomial model which I have developed using the MASS
> library. I now would like to develop some predictions from it.
> Running the predict.glm (stats library) using type="response" gives me a
> non-integer value which was rather puzzling. I would like to confirm that
> this is actually the mean predicted value of the probability mass function as
> opposed to the most likely value. I am afraid that reading the help file for
> predict.glm either does not state this or I don't understand what it states
> (which of course could always be the case)
It gives the conditional mean, i.e., the inverse link applied to the
linear predictor x'b.
> Given that this is a negative binomial model, the mean is often times to the
> right of the most likely value, so I'd like to ask how one would go about
> predicting the most likely value.
You can compute the expected probabilities for each count using the
predprob() method in package "pscl", or by hand using the dnbinom()
## load data and fit model
fm <- glm.nb(art ~ ., data = bioChemists)
## compute expected probabilites for y = 0
## via predprob()
pp <- predprob(fm)
## by hand
dnbinom(0, mu = fitted(fm), size = fm$theta)
## compute the count with the highest probability for each observation
apply(pp, 1, which.max) - 1
## or the median
apply(pp, 1, function(x) max(which(cumsum(x) < 0.5)))
> Thanks in advance !
> Don Catanzaro, PhD Landscape Ecologist
> dgcatanzaro at gmail.com 16144 Sigmond Lane
> 479-751-3616 Lowell, AR 72745
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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