[R] cross validation/GAM/package Daim

[Kim Vanselow]

Dear r-helpers,
I estimated a generalized additive model (GAM) using Hastie's package GAM.

Example:
gam1 <- gam(vegetation ~ s(slope), family = binomial, data=aufnahmen_0708, trace=TRUE)
pred <- predict(gam1, type = "response")

vegetation is a categorial, slope a numerical variable.

Now I want to assess the accurancy of the model using k-fold cross validation.

I found the package Daim with function Daim for estimation of prediction error based on cross-validation (CV) or various bootstrap techniques.

But I am not able to run it properly. I tried the following 3 versions:

1.
accurancy <- Daim (vegetation ~ s(slope), model=gam1, data=aufnahmen_0708, labpos="alpine mats") --> error: could not find function "model"

2.
accurancy <- Daim (vegetation ~ s(slope), model=gam, data=aufnahmen_0708, labpos="alpine mats") --> error in model(formula, train, test) : `family' not recognized

3. accurancy <- Daim (vegetation ~ s(slope), model=gam(family=binomial), data=aufnahmen_0708, labpos="alpine mats") --> error in environment(formula) : Element 1 is empty; Der Teil der Argumentliste '.Internal' der berechnet wurde war:  (fun)

Can anybody help me? Any advice is greatly appreciated!

Thanks
Kim




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