[ADMB Users] help with glmm.admb negative binomial multilevel model
mbrooks at ufl.edu
Thu Sep 22 19:16:18 PDT 2011
oops, I meant glmmadmb without the dot.
On 22 Sep 2011, at 10:11 PM, Mollie Brooks wrote:
> Hi Matt,
> Type in ?glmmadmb and if you're using the latest version, there should be this
> random a formula specifying the random effects. A single random effect may be specified as ~x|g, but parentheses must be used if more than one random effect is specified (e.g.,~(x1|g1)+(x2|g2)).
> If you don't see that, then you need to download the newer version before you can do multiple random effects.
> To get the latest version, follow directions on this web page
> The website says the new version has a "new formula format, similar to that of the lme4 package, where random and fixed effects are specified as part of a single formula (random can also be specified separately, as in lme)"
> So, if you want a random intercept for each student and class, you would do
>> output <- glmm.admb(Y~Treat+ (1|class)+ (1|student) ,family="nbinom", data=mydata)
> or if you think the different classes and students will be affected differently by the treatment levels, you may want
>> output <- glmm.admb(Y~Treat+ (Treat|class)+ (Treat|student) ,family="nbinom", data=mydata)
> but this requires a larger dataset because it fits more parameters (random intercept and random slope).
> Mollie Brooks
> Ph.D. Candidate
> NSF IGERT Fellow
> Biology Department
> University of Florida
> mbrooks at ufl.edu
> On 22 Sep 2011, at 9:40 PM, Matthew Kraft wrote:
>> I am struggling to understand the syntax for fitting a negative binomial multilevel model using glmm.admb. I have conducted a cluster-randomized trial with longitudinal data where students are nested in classes and observations over time are nested within students. My outcomes have a negative binomial distribution.
>> I would like to fit a model where I model Y = Treat with nested random effects for classes and students.
>> my question:
>> 1) in the random statement how do I specific multiple nested random effects? Is is random= ~class: student
>> 2) I don't understand the "group" argument. The documentation say that this is a string naming the main nesting variable. Would that be students or classes in my case? When it says it must be a factor what does that mean?
>> My general thought is to specify the model as
>> output <- glmm.admb(Y~Treat, random=~class: student , group="???",family="nbinom", data=mydata)
>> Any specific help you can provide about coding would be greatly appreciated.
>> Matthew Kraft
>> Doctoral Candidate in Quantitative Policy Analysis
>> Harvard Graduate School of Education
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>> Users at admb-project.org
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