I would like to request help with the following:
I am trying to use a Generalized Additive Model (gam) to examine the density distribution of fish as a function of latitude and longitude as continuous variables, and year as a categorical variable. The model is written as:
 
gam.out   <-  gam(Density ~ s(Lat) + s(Lon) + as.factor(Year))
 
The fitted model prediction of the link function is gam.out$linear.predictors. Presumably, gam.out$linear.predictors must be derived from some combination of the original predictor variables (Lat, Lon, Year), their corresponding coefficients and the intercept (gam.out$coefficients), and the smooth outputs gam.out$smooth and/or gam.out$sp.
 
By comparison, for a glm model:
 
glm.out   <-  glm(Density ~ Lat + Lon + as.factor(Year))
 
this is simply:
 
glm.out$linear.predictors  =  glm.out$coefficients(“intercept”)  +  glm.out$coefficients (“year”)  +  glm.out$coefficients(“lat”) x Lat  +  glm.out$coefficients(“lon”) x Lon
 
My problem is that I cannot figure out how to get the equivalent from the gam model. I would like to know how to ‘decompose’ gam.out$linear.predictors into its components so that I can evaluate the effects of the different predictor variables separately.
 
I would appreciate any comments that can help me with this.
 
Thank you,
 
Andreas Winter
Blacksburg, VA


      
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