> On Nov 22, 2016, at 1:29 PM, Marine Regis <marine.re...@hotmail.fr> wrote: > > Hello, > >> From capture data, I would like to assess the effect of longitudinal changes >> in proportion of forests on abundance of skunks. To test this, I built this >> GAM where the dependent variable is the number of unique skunks and the >> independent variables are the X coordinates of the centroids of trapping >> sites (called "X" in the GAM) and the proportion of forests within the >> trapping sites (called "prop_forest" in the GAM): > > mod <- gam(nb_unique ~ s(x,prop_forest), offset=log_trap_eff, > family=nb(theta=NULL, link="log"), data=succ_capt_skunk, method = "REML", > select = TRUE) > summary(mod) > > Family: Negative Binomial(13.446) > Link function: log > > Formula: > nb_unique ~ s(x, prop_forest) > > Parametric coefficients: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -2.02095 0.03896 -51.87 <2e-16 *** > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > Approximate significance of smooth terms: > edf Ref.df Chi.sq p-value > s(x,prop_forest) 3.182 29 17.76 0.000102 *** > --- > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > R-sq.(adj) = 0.37 Deviance explained = 49% > -REML = 268.61 Scale est. = 1 n = 58 > > > I built a GAM for the negative binomial family. When I use the function > `predict.gam`, the predictions of capture success from the GAM and the values > of capture success from original data are very different. What is the reason > for differences occur?
You have an offset that is not described. And `gam` suppresses the Intercept. These would seem to be likely sources of confusion. For the best answers either on Rhelp or on CrossValidated.com you should be offering a working example. It's not our responsibility to build these for you. I found that others had included offsets and then had questions about prediction. I haven't reviewed these candidates but perhaps you can find one in this modest listing that comes up from the MarkMail search engine: http://markmail.org/search/?q=list%3Aorg.r-project.r-help+mgcv+gam+offset+predict library(mgcv) x<-seq(0,10,length=100) y<-x^2+rnorm(100) m1<-gam(y~s(x,k=10,bs='cs')) m2<-gam(y~s(x,k=10,bs='cs'), offset= rep(10,100) ) x1<-seq(0,10,0.1) y1<-predict(m1,newdata=list(x=x1)) y2<-predict(m2,newdata=list(x=x1)) plot(x,y,ylim=c(0,100)) lines(x1,y1,lwd=4,col='red') lines(x1,y2,lwd=4,col='blue') -- David. > > **With GAM:** > > modPred <- predict.gam(mod, se.fit=TRUE,type="response") > summary(modPred$fit) > Min. 1st Qu. Median Mean 3rd Qu. Max. > 0.1026 0.1187 0.1333 0.1338 0.1419 0.1795 > > **With original data:** > > summary(succ_capt_skunk$nb_unique) > Min. 1st Qu. Median Mean 3rd Qu. Max. > 17.00 59.00 82.00 81.83 106.80 147.00 > > The question has already been posted on Cross validated > (http://stats.stackexchange.com/questions/247347/gam-with-the-negative-binomial-distribution-why-do-predictions-no-match-with-or) > without success. > > Thanks a lot for your time. > Have a nice day > Marine > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. David Winsemius Alameda, CA, USA ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
