[R-sig-ME] glmmadmb random effects help

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Mon Nov 10 09:10:35 CET 2014

Dear Greg,

You need to use (1|fTrans/fSite). This is site nested in transect. Note that the random effect of transect should have 11 observation and not 110 as in your current summary.

Month will be crossed with site (and transect).

You could calculate the AIC by hand to check if the AIC in the summary is correct.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op inbo.be
To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
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Van: r-sig-mixed-models-bounces op r-project.org [r-sig-mixed-models-bounces op r-project.org] namens Hacker, Greg (CDPH-CID-DCDC) [Greg.Hacker op cdph.ca.gov]
Verzonden: vrijdag 7 november 2014 19:01
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] glmmadmb random effects help

Hello all,

I am hoping to get some expert guidance on an analysis I am attempting.  At the very least I hope you can tell me if I'm on the right track.  If I'm not, maybe you can nudge me in the right direction.

I have data that consists of repeated counts of ticks that were collected at 110 different sites around a portion of a reservoir. These 110 sites were grouped into 11 transects (I'm thinking nesting here).  The counts were collected monthly (although some months were missed) over 3 years (years = Oct-Sept to account for tick biology).  Along with this I have several continuous and categorical ecological variables (e.g., canopy coverage, dominant overstory/understory veg, average temp, relative humidity, aspect, etc...).  I'm hoping to create a candidate set of models that I can then use AIC (or something related) to determine the best fit.  In the end I'm hoping to have a model that can reasonably predict abundance of ticks based on a subset of environmental variables.

>From what I've read I have too few seasons to use as a random effect.  I believe the month, site, and transect variables should all be considered random effects, with site nested in transect.  However, site/transect is also crossed with month and season(fixed effect).  Below I have provided the output (minus the coefs for fixef) from the summary of my global model (at least the most complex model I could use without getting errors).

glmmadmb(formula = Tot_I_pac ~ SWave.max.temp + ave.rh + SWave.max.temp:ave.rh +
    Perc_Canopy + Dom.Overstory + Perc_Canopy * Dom.Overstory +
    Dom.Midstory + Dom.Understory + Aspect + Soil.Type + fSeason +
    (1 | Month) + (1 | fSite/fTrans), data = Ticks, family = "nbinom")

AIC: 7589

Number of observations: total=3190, Month=11, fSite=110, fSite:fTrans=110
Random effect variance(s):
            Variance StdDev
(Intercept)     18.8  4.336
            Variance StdDev
(Intercept)   0.1429  0.378
            Variance StdDev
(Intercept)   0.1061 0.3257

Negative binomial dispersion parameter: 1.4 (std. err.: 0.096)

Log-likelihood: -3762

Does everything seem reasonable here? Does the way I wrote the random effects portion of the model relate to the description of the data?  Does this model take into account that month and the site/transect combination are crossed with season?  Finally, is the AIC score provided in the summary what should be used to rank the models or should it be ignored and calculated by hand (does the AICtab function utilize this score)?

Forgive me, I am only a wildlife biologist who knows only enough to get myself into trouble!  Thank you in advance for any advice folks can provide!



Greg Hacker

Public Health Foundation Enterprises
California Department of Public Health
Vector-Borne Disease Section
8633 Bond Rd.,
Elk Grove, CA 95624

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