Date: Sun, 8 Apr 2007 21:37:23 0700
ReplyTo: David L Cassell <davidlcassell@MSN.COM>
Sender: "SAS(r) Discussion" <SASL@LISTSERV.UGA.EDU>
From: David L Cassell <davidlcassell@MSN.COM>
Subject: Re: Help with NLMIXED or GLIMMIX for multilevel GLMM
InReplyTo: <200704052110.l35J2EK3000491@mailgw.cc.uga.edu>
ContentType: text/plain; format=flowed
qxu@UCDAVIS.EDU wrote:
>
>Dear all,
>
>I have a time allocation multinomial response data set in Anthropology. It
>was a two year study on twenty families: from A to T; each family had
>unequal number of individuals, for example, family A had individual A1, A
>2, A3, and family B had B1, B2. Each individual was observed by
>anthropologists every six days at a random time during that day. Their
>activities were recorded. For example, there are three activity codes: A
>doing agriculture work, LLeisure, MMarketing (there were more categories
>in the original data, up to 26). The response will look like:
>A1: A A A M L ... (over two years)
>A2: A M M L M ...
>.
>.
>.
>
>Now I want to fit a generalized linear mixed model for this response
>against fixed effects, such as gender, age, with random effect for family
>and person. (since each person has repeated observations over 2 years and
>individuals were clustered in families.)
>I attampted to fit the model by GLIMMIX using multinomial distribution and
>glogit link, but convergence could not be reached. I could not figure
>outhow to fit more than one level random effects in NLMIXED either.
>Any suggestion is highly appreciated.
[1] How were the families selected? Isn't this really a survey sample?
So isn't this something other than a multilevel GLMM? It looks to me
(from way over here) like a simple cluster sample with lots of data over
time. So it may be that you should be using PROC SURVEYLOGISTIC
instead.
[2] If I'm right, then the random effects you perceive are simply
survey sampling structure, and you have no random effects to handle
in your model.
[3] Before I can get much further, I really need to know what your
hypotheses are, and how you are testing them. It may be a royal
pain to analyze as a survey sample model, or it may be a breeze. It
depends on your hypothese and your data usage.
HTH,
David

David L. Cassell
mathematical statistician
Design Pathways
3115 NW Norwood Pl.
Corvallis OR 97330
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