[R-sig-eco] MuMIn package, Inquiry on summary output _ Conditional or Full average models?
Daniel Gruner
dsgruner at umd.edu
Sun Feb 14 17:07:16 CET 2016
Dear Laura,
Grueber et al. (2011) discusses the distinctions and the rationale for
making this choice (p 705-706), citing Burnham & Anderson (2002) and
Nakagawa and Freckleton (2011).
Burnham KP, and DR Anderson (2002). Model Selection and Multimodel
Inference: a Practical Information-Theoretic Approach. 2nd edition.
Springer, New York.
Grueber CE, S Nakagawa, RJ Laws, and IG Jamieson (2011). Multimodel
inference in ecology and evolution: challenges and solutions. Journal of
Evolutionary Biology 24:699-711.
Nakagawa S, and RP Freckleton (2011). Model averaging, missing data and
multiple imputation: a case study for behavioural ecology. Behavioral
Ecology and Sociobiology 65:103-116.
On 2/14/2016 10:02 AM, Laura Riggi wrote:
> Dear all,
>
> I have a question regarding the output for model averaging in R with MuMin package. In the summary for model averaging two models of coefficient calculations come out: the "full average" and the "conditional (or subset) average" model (example of output below).
>
> As explained on the MuMin package pdf:
> "The 'subset' (or 'conditional') average only averages over the models where the parameter appears. An alternative, the 'full' average assumes that a variable is included in every model, but in some models the corresponding coefficient (and its respective variance) is set to zero. Unlike the 'subset average', it does not have a tendency of biasing the value away from zero. The 'full' average is a type of shrinkage estimator and for variables with a weak relationship to the response they are smaller than 'subset' estimators."
>
> However, I cannot find information online concerning the theory behind these different outputs. I am not sure what is the point of having a "conditional" model as it seems to go against the idea of doing a model averaging analysis.
> Do you know of articles / books that discuss this? When should we use one or the other?
> Any advice would be appreciated.
>
>> summary(model.avg(dd, subset = delta < 2))
> Call:
> model.avg.model.selection(object = dd, subset = delta < 2)
>
> Component model call:
> lme.formula(fixed = log(Parasitoi_S1.S2 + 1) ~ <8 unique rhs>, data = data, random = ~1 | Field.x/Site.x, method
> = ML, na.action = na.fail)
>
> Component models:
> df logLik AICc delta weight
> 1345 8 -161.74 340.52 0.00 0.22
> 345 7 -162.97 340.74 0.22 0.19
> 12345 9 -161.26 341.82 1.31 0.11
> 13456 9 -161.36 342.03 1.51 0.10
> 2345 8 -162.53 342.10 1.58 0.10
> 3456 8 -162.54 342.11 1.60 0.10
> 35 6 -164.76 342.12 1.60 0.10
> 145 7 -163.84 342.47 1.96 0.08
>
> Term codes:
> L OSR2012_X500 OSR2013_X500 Weed.cover Wood_X500 Weed.cover:Wood_X500
> 1 2 3 4 5 6
>
> Model-averaged coefficients:
> (full average)
> Estimate Std. Error Adjusted SE z value Pr(>|z|)
> (Intercept) 2.5693356 0.5295081 0.5337822 4.813 1.5e-06 ***
> L -0.0005893 0.0007720 0.0007756 0.760 0.447
> OSR2013_X500 -3.7932641 2.1558940 2.3307509 1.627 0.104
> Weed.cover 0.1331237 0.0915813 0.0922877 1.442 0.149
> Wood_X500 -5.5516524 3.2502461 3.5300659 1.573 0.116
> OSR2012_X500 0.4326628 1.3007077 1.3966718 0.310 0.757
> Weed.cover:Wood_X500 0.1922066 0.6215019 0.6255589 0.307 0.759
>
> (conditional average)
> Estimate Std. Error Adjusted SE z value Pr(>|z|)
> (Intercept) 2.5693356 0.5295081 0.5337822 4.813 1.5e-06 ***
> L -0.0011489 0.0007205 0.0007279 1.578 0.1145
> OSR2013_X500 -4.1301091 1.9155698 2.1268744 1.942 0.0522 .
> Weed.cover 0.1474601 0.0847131 0.0855581 1.724 0.0848 .
> Wood_X500 -5.5516524 3.2502461 3.5300659 1.573 0.1158
> OSR2012_X500 2.0508163 2.1681256 2.4346913 0.842 0.3996
> Weed.cover:Wood_X500 0.9639767 1.0923693 1.1039224 0.873 0.3825
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Relative variable importance:
> Wood_X500 OSR2013_X500 Weed.cover L OSR2012_X500 Weed.cover:Wood_X500
> Importance: 1.00 0.92 0.90 0.51 0.21 0.20
> N containing models: 8 7 7 4 2 2
>
> Thank you for your help.
> Kind Regards,
> Laura
>
>
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>
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--
Daniel S. Gruner, Associate Professor
Department of Entomology
4112 Plant Sciences Bldg
University of Maryland
College Park, MD 20742 U.S.A.
(o) 301-405-3957 (f) 301-314-9290
dsgruner at umd.edu
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