David, Are you using the normalized residuals from the $lme part of the gam object (i.e. something like residuals(foo$lme,type="normalized"))? Without standardization the raw residuals will look pretty much as bad for the gamm as they did for the gam (actually they might even lookl a little worse).
best, simon On Saturday 15 November 2008 17:19, David M Warner wrote: > Greetings > This is a long email. > > I'm struggling with a data set comprising 2,278 hydroacoustic estimates of > fish biomass density made along line transects in two lakes (lakes > Michigan and Huron, three years in each lake). The data represent > lakewide surveys in each year and each data point represents the estimate > for a horizontal interval 1 km in length. > > I'm interested in comparing biomass density and bathymetric distribution > (bottom depth) in the two lakes and there is graphical evidence of a > non-linear relationship between biomass density and bottom depth. Hence > my interest in GAMs. > > Predictors of primary interest are lake (factor) and bottom depth > (continuous). > > The fish data are autocorrelated at varying ranges, depending on species > and year. I've tested this using correlog (package ncf) > > The bottom depth data are also highly autocorrelated. > > Because of the autocorrelations in data, autocorrelations in GAM residuals > (up to 20 lags in some cases), patterns in residual plots from GAM models, > and very narrow confidence intervals for the predictions, I feel that GAM > results are biased and have attempted to use GAMM. > > Data and procedure examples: > #> fish[1:10, ] > Transect yaoalebiom yaosmeltbiom yaobloaterbiom year depth lake x > y interval > 1 nn_1 12.019655 34.910370110 2.647370 2005 97.07525 2 > 526601.8 4850206 1 > 2 nn_1 12.164686 35.331548810 3.982028 2005 98.37024 2 > 526742.2 4849339 2 > 3 nn_1 11.176009 32.460052230 1.646604 2005 99.98218 2 > 526886.9 4848348 3 > 4 nn_1 0.000000 0.036457091 5.306225 2005 81.44616 2 > 526993.4 4850849 4 > 5 nn_1 40.808118 10.988825410 3.222485 2005 101.45707 2 > 526997.5 4847359 5 > 6 nn_1 6.273421 18.176753520 18.832348 2005 98.69197 2 > 527084.1 4846366 6 > 7 nn_1 6.225799 16.050983390 66.941892 2005 94.14283 2 > 527214.7 4845372 7 > 8 nn_1 7.322910 19.001196850 47.273341 2005 91.21771 2 > 527331.6 4844636 8 > 9 nn_1 0.000000 0.067646462 20.912908 2005 87.76123 2 > 527495.9 4843390 9 > 10 nn_1 0.000000 0.006012106 26.611785 2005 87.59767 2 > 527606.6 4842426 10 > > #GAM example > bloat.gam8 <- gam(log10(yaobloaterbiom+0.00325) ~ lakef +s(depth, > by=lakef), data=fish3) > > #GAMM example: > bloat.gamm1 <- gamm(log10(yaobloaterbiom+0.00325) ~ lakef + s(depth, > by=lakef), correlation=corAR1(form = ~ interval|tranf), data=fish3) > > However, GAMM results from models including a wide variety of correlation > structures (corExp, CorSpher, CorLin, AR1, ARMA) produce autocorrelated > residuals (similar lag range as GAM), patterns in residuals plots, and > confidence intervals for predictions that are only slightly large than for > GAMs. This suggests to me that GAMM is not performing much better than > GAM (or I've not specified models correctly). > > Is my assessment of the GAMM performance reasonable? None of the models > (GAM or GAMM) explain much of the deviance (~20%). > > I'm interested in an information-theoretic approach to selecting the best > model from a set of possible models (AICc, dAICc, AICc weights), but > cannot run some of the GAM models with GAM because they lack a random > term. I'm not sure how to use the GAMM output to compare the models I can > run with this procedure. > > Finally, as a last resort, I've subsampled the original data set so that I > have 1 record per transect per lake per year for a total N=99. > > I get different "best models" from GAM (original data) GAMM (original data > but including correlation structure), and GAM (subsetted data). Selection > of different models leads to fairly different conclusions about the > similarities and differences between the lakes. > > I'm not sure where to go with this as a result. > > Any thoughts/comments would be appreciated. > Dave > > > > > > > David Warner > Research Fishery Biologist > USGS Great Lakes Science Center > 1451 Green Road > Ann Arbor MI 48105 > 734.214.9392 > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html and provide commented, minimal, > self-contained, reproducible code. -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603 www.maths.bath.ac.uk/~sw283 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
