Jeff Evans-5 wrote:
>
>
> lme4 does have a leg up on GLIMMIX in other areas, though.
> The latest SAS release (9.2) is now able to compute the Laplace
> approximation of the likelihood, but it will only fit an overdispersion
> parameter using pseudo-likelihoods which can't be used for model
>
You can fit this kind of model (and negative binomial) and more
difficult mixed models with AD Model Builder's random effects module
which is now freely available at
http://admb-project.org/
--
David A. Fournier
P.O. Box 2040,
Sidney, B.C. V8l 3S3
Canada
Phone/FAX 250-655-3364
http://ott
com] On Behalf Of Douglas
Bates
Sent: Thursday, February 26, 2009 3:50 PM
To: Jeff Evans
Cc: r-help@r-project.org
Subject: Re: [R] generalized linear mixed models with a beta distribution
On Thu, Feb 26, 2009 at 12:04 PM, Jeff Evans wrote:
> Has there been any follow up to this question? I have
On Thu, Feb 26, 2009 at 12:04 PM, Jeff Evans wrote:
> Has there been any follow up to this question? I have found myself wondering
> the same thing: How then does SAS fit a beta distributed GLMM? It also fits
> the negative binomial distribution.
When SAS decides to open-source their code we'll b
Has there been any follow up to this question? I have found myself wondering
the same thing: How then does SAS fit a beta distributed GLMM? It also fits
the negative binomial distribution.
Both of these would be useful in glmer/lmer if they aren't 'illegal' as
Brian suggested. Especially as SAS i
See this post:
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/7144.html
Cheers,
Simon.
On Mon, 2008-03-17 at 17:04 +1100, Steve Candy wrote:
>
> Craig A Faulhaber wrote:
>
>
>
> >I am interested in using a generalized linear mixed model with data
>
> > that best fits a beta distribution
Craig A Faulhaber wrote:
>I am interested in using a generalized linear mixed model with data
> that best fits a beta distribution (i.e., the data is bounded between
> 0 and 1 but is not binomial).
..
>For clarification, here's what I'm trying to model:
>I have a beta-distributed resp
Thanks for the tips and clarifications. I'm a newbie and don't always
have the terminology down correctly. My understanding is that one
should be able to use generalized linear mixed models to model response
variables that take any of the exponential family of distributions. The
beta distrib
glmmPQL can fit the same GLM families as glm() can -- it does not list
_any_ .
Howver, the beta distribution does not give a GLM family and hence your
subject line is strictly about a non-existent concept. I'm presuming that
you want to model the logit of the mean of a beta by a random effects
Greetings,
I am interested in using a generalized linear mixed model with data that
best fits a beta distribution (i.e., the data is bounded between 0 and 1
but is not binomial). I noticed that the beta distribution is not
listed as an option in the "family objects" for glmmPQL or lmer. I
f
10 matches
Mail list logo
