Hello,
I am a novice in Generalized Additive Models (GAMs) and I would need some
advice on these models. 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
Should I include the simple effects of independent variables "X" and
"prop_forest" into the GAM when the interaction is significant? I ask this
question because the longitude and latitude are often included as an
interaction term in a GAM (i.e., s(X,Y)) without the simple effects (however, I
tested for the simple effects and they were not significant in my case).
Is it correct to include the interaction between X and proportion of forests
when my objective is to test longitudinal changes in proportion of forests?
Thanks a lot for your time.
Have a nice day.
Marine
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