You might want to reconsider whether it make any sense to model average the individual regression coefficients. See Cade (2015. Model averaging and muddled multimodel inferences. Ecology 96: 2370-2382).
Brian Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: ca...@usgs.gov <brian_c...@usgs.gov> tel: 970 226-9326 On Sun, Feb 14, 2016 at 9:07 AM, Daniel Gruner <dsgru...@umd.edu> wrote: > 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 >> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> R-sig-ecology mailing list >> R-sig-ecology@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> . >> >> > -- > > 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 > dsgru...@umd.edu > > http://grunerlab.umd.edu > https://twitter.com/GrunerDaniel > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology