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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