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()
function:
## load data and fit model
library("pscl")
data("bioChemists")
fm <- glm.nb(art ~ ., data = bioChemists)
## compute expected probabilites for y = 0
## via predprob()
pp <- predprob(fm)
pp[,1]
## 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)))
hth,
Z
Thanks in advance !
--
-Don
Don Catanzaro, PhD Landscape Ecologist
[EMAIL PROTECTED] 16144 Sigmond Lane
479-751-3616 Lowell, AR 72745
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