Hello all,

I need to correctly(!) interpret and graphically present a negative
binomial mixed model generated with glmmADMB.   Is the coefplot2 package
working as of Jan, 2013?   Is there an alternative if not?   Is there an
"extraction" package to provide confidence intervals around the
exponentiated coefficients?   I ran an mcmc approximation to get confidence
intervals, but I'm not sure how to present them graphically.  Is there
ggplot code that might work?

The study system: What are the effects of season and forest stand types on
snowshoe hare pellet densities.  Do snowshoe hares move more seasonally in
some forest stands than in others?  Year is a random (nuisance) variable.
 Each stand type (treatment) has unequal sample sizes that vary by year.
The count data are constrained at zero, residuals are overdispersed.

R ver 2.15.2, package glmmADMB_0.7.3 in Windows 7

> install.packages("coefplot2",repos="http://www.math.mcmaster.ca/bolker/R")
Warning: unable to access index for repository
http://www.math.mcmaster.ca/bolker/R/bin/windows/contrib/2.15
   package ‘coefplot2’ is available as a source package but not as a binary
Warning message:
package ‘coefplot2’ is not available (for R version 2.15.2)

My Data Structure:
> str(ps3)
'data.frame':   452 obs. of  7 variables:
 $ year       : Factor w/ 8 levels "2005","2006",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ season   : Factor w/ 2 levels "Smr","Wtr": 2 2 2 2 2 2 2 2 2 2 ...
 $ stand      : Factor w/ 39 levels "AF1","AF2","AF3",..: 1 2 3 4 5 6 7 8 9
10 ...
 $ stndtyp    : Factor w/ 3 levels "MT","RG","SEL": 3 3 3 3 3 3 2 2 2 2 ...
 $ pellets     : int  86 435 135 34 138 424 984 991 613 1408 ...
 $ days       : num  228 227 225 227 223 ...
 $ ln.days    : num  5.43 5.42 5.42 5.42 5.41 ...

My model:
> nb2 <- glmmadmb(pellets ~ season*stndtyp+offset(ln.days)+(1|year),
data=ps3,family="nbinom")
> summary(nb2)

Call:
glmmadmb(formula = pellets ~ season * stndtyp + offset(ln.days)+(1 | year),
data = ps3, family = "nbinom")
Coefficients:
                                 Estimate Std. Error z value Pr(>|z|)
(Intercept)                     -2.686      0.226  -11.89   <2e-16
***         ## is stand type mature (MT), summer
seasonWtr                     0.560      0.176    3.19   0.0014 **
stndtypRG                      2.308      0.149   15.44   <2e-16 ***
stndtypSEL                    1.136      0.159    7.13    1e-12 ***
seasonWtr:stndtypRG     0.221      0.194    1.14   0.2545
seasonWtr:stndtypSEL   -0.197      0.207   -0.95   0.3408
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Number of observations: total=452, year=8
Random effect variance(s):
Group=year
            Variance StdDev
(Intercept)   0.2586 0.5085
Negative binomial dispersion parameter: 2.4605 (std. err.: 0.17016)

Log-likelihood: -2348.52

> Anova(nb2)   #  Wald chi-square tests are provided for fixed effects in
generalized linear mixed-effects models
Analysis of Deviance Table (Type II tests)
Response: pellets
                        Df    Chisq Pr(>Chisq)
season              1    10.1872   0.001414 **
stndtyp              2  272.7451  < 2.2e-16 ***
season:stndtyp   2   2.3522   0.308476
Residuals      444
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Thank you everyone.

Sheryn Olson, Graduate Research Assistant, The University of Maine

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