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
--
Jetzt kostenlos herunterladen: Internet Explorer 8 und Mozilla Firefox 3.5 -
sicherer, schneller und einfacher! http://portal.gmx.net/de/go/atbrowser
