Well part of the issue is that the negative binomial estimates are for means and they can differ a fair bit from the raw counts, but I'm also guessing that part of the issue is that the offset may not be accounted for with the predict.gam() function.
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 Tue, Nov 22, 2016 at 2:29 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? > > **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. > > [[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.
