Defining the "random" term in function "negbin" of AOD package
Dear Caroline
How could I adjust this to function with the "negbin" function? Specifically, what would I use for the required "random" term?
The random argument is used to specify either a global parameter "phi" (random = ~ 1) or specific parameters "phi" for the levels of a given group factor (random = ~ group) (see ?negbin for what represents "phi" in negbin: Var[y] = ? + phi * ?^2)
In your example, it shoud be: negbin(formula = ..., random = ~ 1, data = ...)
Note that if your model has too many parameters, negbin may fail to reach the MLE.
You also can try the package 'gamlss' on CRAN. For your example, you can use the function gamlss as follows:
fm <- gamlss(formula = ..., family = NBI, sigma.formula = ~ 1, data = ...)
summary(fm)
Regards
ML
--------------------------------------------------
Matthieu Lesnoff
International Livestock Research Institute (ILRI)
PO BOX 30709, Nairobi, 00100 GPO, Kenya
Tel: Off: (+254) 20 422 3000 (ext. 4801)
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Email: m.lesnoff at cgiar.org
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-----Original Message-----
From: r-help-bounces at r-project.org
[mailto:r-help-bounces at r-project.org] On Behalf Of Caroline Paulsen
Sent: 13 December 2007 00:25
To: r-help at r-project.org
Subject: [R] Defining the "random" term in function "negbin"
of AOD package
I have tried glm.nb in the MASS package, but many models (I
have 250 models with different combinations of predictors for
fish counts data) either fail to converge or even diverge.
I'm attempting to use the negbin function in the AOD package,
but am unsure what to use for the "random" term, which is
supposed to provide a right hand formula for the
overdispersion parameter. I'm not even sure what this
statement means. Any advice you have would be greatly appreciated.
negbin(formula, random, data, phi.ini = NULL, warnings = FALSE,
na.action = na.omit, fixpar = list(),
hessian = TRUE, control = list(maxit = 2000), ...)
My largest model using glm.nb looks like this:
negBin.glm1 <- glm.nb(Count ~ offset(log(Tow.Area)) + Basin +
Bathy + Hypoxia + Period + Depth + Basin*Depth + Bathy*Depth
+ Hypoxia*Depth +
Period*Depth + Basin*Period + Bathy*Period +
Hypoxia*Period + Hypoxia:Period:Depth + Bathy:Period:Depth +
Basin:Period:Depth,
control=glm.control(maxit=1000), method="glm.fit",
data=Combined.Counts.df)
Caroline E. Paulsen
Masters Candidate
School of Aquatic and Fishery Sciences
University of Washington
phone: 206.852.9539
email: cpaulsen at u.washington.edu
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