Related to the previous question, I tried the binomial example in the landmap help but it does not estimate the model error.
eberg$Parabraunerde <- ifelse(eberg$TAXGRSC=="Parabraunerde", 1, 0)
X <- eberg_grid[c("PRMGEO6","DEMSRT6","TWISRT6","TIRAST6")]
mB <- train.spLearner(eberg["Parabraunerde"], covariates=X,
family=binomial(), cov.model = "nugget", parallel=FALSE)
eberg.Parabraunerde <- predict(mB)
eberg.Parabraunerde$pred
Object of class "SpatialPixelsDataFrame" (package sp):
Grid parameters:
cellcentre.offset cellsize cells.dim
x 3570050 100 100
y 5708050 100 100
Variables measured:
prob.0 prob.1 response error.0 error.1
1 0.8300358 0.16996416 0 NA NA
2 0.8251524 0.17484764 0 NA NA
3 0.8216094 0.17839064 0 NA NA
4 0.9327373 0.06726271 0 NA NA
5 0.9327373 0.06726271 0 NA NA
6 0.9327373 0.06726271 0 NA NA
...
Related to the previous question, I tried the binomial example in the landmap help but it does not estimate the model error.
eberg$Parabraunerde <- ifelse(eberg$TAXGRSC=="Parabraunerde", 1, 0)
X <- eberg_grid[c("PRMGEO6","DEMSRT6","TWISRT6","TIRAST6")]
mB <- train.spLearner(eberg["Parabraunerde"], covariates=X,
family=binomial(), cov.model = "nugget", parallel=FALSE)
eberg.Parabraunerde <- predict(mB)
Grid parameters:
cellcentre.offset cellsize cells.dim
x 3570050 100 100
y 5708050 100 100
Variables measured:
prob.0 prob.1 response error.0 error.1
1 0.8300358 0.16996416 0 NA NA
2 0.8251524 0.17484764 0 NA NA
3 0.8216094 0.17839064 0 NA NA
4 0.9327373 0.06726271 0 NA NA
5 0.9327373 0.06726271 0 NA NA
6 0.9327373 0.06726271 0 NA NA
...