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?
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