Date: Mon, 26 Sep 2011 12:38:51 +0200
From: Swen Meyer<s.meyer at lmu.de>
To: R-sig-Geo at r-project.org
Subject: [R-sig-Geo] Leave one out cross validation in a Generalized
linear Model?
Message-ID:<4E80563B.8050705 at lmu.de>
Content-Type: text/plain; charset=ISO-8859-15; format=flowed
Dear All,
I got the following problem. I use the GLM below to do Regression Kriging:
#MLR backward Co-Variables(P1 -P10)
fit1<- lm((CLAY)~ PC1 + PC2 +PC3 +PC4 +PC5 +PC6 +PC7 +PC8+PC9 +PC10
, boden.ov[sel,] )
#GLM
m.glm<- glm(formula(step), boden.ov[sel,], family=gaussian)
summary(m.glm)
p.glm<- predict(m.glm, newdata=boden.grid, type="link", se.fit=TRUE)
om.glm<- as(boden.grid["band1"], "SpatialPointsDataFrame")
om.glm$var1.pred<- p.glm$fit
om.glm$var1.var<- p.glm$se.fit
om.glm$svar<- p.glm$se.fit^2/(m.glm$null.deviance/m.glm$df.null)
gridded(om.glm)<- TRUE
fullgrid(om.glm)<- TRUE
##Regression Kriging with GLM
vario.res<- autofitVariogram((CLAY)~om.glm, boden.ov[sel,], model =
c("Sph"))
plot(variogram((CLAY)~om.glm, boden.ov[sel,]), vario.res$var_model)
om.rk<- krige((CLAY)~om.glm,boden.ov, boden.grid, vario.res$var_model)
om.rk$om.pred<- (om.rk$var1.pred)
#Leave one out Cross Validation Regression Kriging
rk.cv<- krige.cv((CLAY)~om.glm,boden.ov, vario.res$var_model,
verbose=TRUE)
rk.RMSE<-
sqrt(mean((rk.cv$var1.pred-rk.cv$observed)^2)/length(rk.cv$var1.pred))
rk.RMSE
I would like to calculate a leave one out cross validation like I did it
for the Regrssion Kriging, but only for upper GLM-part. Is there a way
of package to calculate the CV only for the GLM part?
kordinary_m.glm.cv<- krige.cv(m.glm , loc = boden.ov)
Does anyone have an idea?
Thank you in advance,
Swen