I am interested in exploring the idea of generating a predictive model for a target variable in a spatial context. I would then like to correct the residuals of this model using kriging, i.e. in a nutshell you sum up the results from both models and take that as the final prediction:
The classical approach is to fit a GLM and then krige (is that how you turn it into a verb?) its residuals. I would like to fit something like a Random Forest. Which leads me to my question. How do you validate these models? If I wanted to do cross-validation do I separate the data (e.g. 10-folds) and reapeatedly fit the model, krige the corresponding residuals -> predict on test set, etc. Or do I have to separate the data into 3 sets, one to fit the model, see how it predicts on another data set, krige THOSE errors and then test on the final data set, repeat. What is the usual apporach to do this correctly?
Thank you in advanced.
I'm using R by the way.