[ADMB Users] Output from glmm.admb
hskaug at gmail.com
Mon Mar 29 11:52:57 PDT 2010
Output is supposed to be available at a higher level from
Here are some examples:
>fit = glmm.admb(y~Base*trt+Age+Visit,random=~Visit,group="subject",data=epil2,family="nbinom")
>fit2 = glmm.admb(y~Base*trt+Age,random=~Visit,group="subject",data=epil2,family="nbinom")
Analysis of Variance Table
Model 1: y ~ Base * trt + Age
Model 2: y ~ Base * trt + Age + Visit
NoPar LogLik Df -2logQ P.value
1 8 -625.87
2 9 -624.57 1 2.6 0.10686
(For standard deviations of the fixed effects, you maybe have to
to the .std file.)
To get AIC you have to type
> aic = -2*logLik(fit) -2*10
where you have to count the number of parameters yourself.
The complete list of functions (with their own help pages) is:
anova.glmm.admb Anova for glmm.admb objects
epil2 Seizure Counts for Epileptics
glmm.admb Generalized Linear Mixed Models using AD Model
glmmADMB-package What the package does (short line) ~~ package
glmmADMB-package Generalized Linear Mixed Models using AD Model
print.glmm.admb Print glmm.admb objects
ranef.glmm.admb Extract Random Effects
On Mon, Mar 29, 2010 at 1:36 PM, Nina Bhola <nina.bhola at gmail.com> wrote:
> I have been trying to implement the glmm.admb to my data.
> I have a model which works great with my data.
> However, i don't know exactly what some of the output is.
> For example, the parameters together with estimated standard
> deviations in the file nbmm.std. are not clear. How do you know which
> estimate belongs to which parameter?
> How do you extract AIC values?
> The -log-likelihood is in the file nbmm.par. that can be used for a
> likelihood ratio test on the significance of adding the parameter T to
> the model. What do you mean by T?
> The file nbmm.rep contains the predicted mean for each observation in
> the second column of the list. What are these means? The reason i ask
> is that the glmm.admb, should back-transform the estimates to give the
> us means, which should be the ones in the the nbmm.rep output file,
> however, if i exp(estimate) i do not get the same result as what is in
> this file? I get different predicted means which i am inclined to
> believe since i back transformed them myself! So what are these means
> in the output file?
> What does Tau r2/mu r2/(mu*tau) mean?
> Can we generate confidence intervals since there are no p values?
> Hope you can clarify these issues
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