I think you should use glmm.admb.
library(glmmADMB)
?glmm.admb
glmm.admb package:glmmADMB R Documentation
Generalized Linear Mixed Models using AD Model Builder
Description:
Fits mixed-effects models to count data using Binomial, Poisson or
negative binomial response distributions. Zero-inflated versions
of Poisson and negative binomial distributions are available.
2006/7/28, Tracy Feldman <[EMAIL PROTECTED]>:
To whom it may concern:
I have a question about how to appropriately conduct an lmer analysis for
negative binomially distributed data. I am using R 2.2.1 on a windows machine.
I am trying to conduct an analysis using lmer (for non-normally distributed
data and both random and fixed effects) for negative binomially distributed
data. To do this, I have been using maximum likelihood, comparing the full
model to reduced models (containing all but one effect, for all effects).
However, for negative binomially distributed data, I need to estimate the
parameter theta. I have been doing this by using a negative binomial glm of
the same model (except that all the effects are fixed), and estimating mu as
the fitted model like so:
model_1 <-glm.nb(y~x1+x2+x3, data = datafilename)
mu_1 <- fitted(model_1)
theta_1 <- theta.ml(y, mu_1, length(data), limit = 10, eps =
.Machine$double.eps^0.25, trace = FALSE)
Then, I conduct the lmer, using the estimated theta:
model_11 <-lmer(y~x1+x2+(1|x3), family = negative.binomial(theta = theta_1, link =
"log"), method = "Laplace")
First, I wondered if this sounds like a reasonable method to accomplish my
goals.
Second, I wondered if the theta I use for reduced models (nested within
model_11) should be estimated using a glm.nb with the same combination of
variables. For example, should a glm.nb with x1 and x3 only be used to
estimate theta for an lmer using x1 and x3?
Third, I wish to test for random effects of one categorical variable with 122
categories (effects of individual). For this variable, the glm.nb (for
estimating theta) does not work--it gives this error message:
Error in get(ctr, mode = "function", envir = parent.frame())(levels(x), :
orthogonal polynomials cannot be represented accurately enough for 122
degrees of freedom
Is there any way that will allow me to accurately estimate theta using this
particular variable (or without it)? Or should I be using a Poisson
distribution (lognormal?) instead, given these difficulties?
If anyone has advice on how to properly conduct this test (or any references
that might tell me in a clear way), I would be very grateful. Also, please let
me know if I should provide additional information to make my question clearer.
Please respond to me directly, as I am not subscribed to this list.
Thank you very much,
Tracy S. Feldman
Postdoctoral Associate, the Noble Foundation, Ardmore, OK.
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--
黄荣贵
Department of Sociology
Fudan University
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and provide commented, minimal, self-contained, reproducible code.
