Partition data according to the maximum a' posteriori (MAP)
tree contained in a
Output is a list of
data.frames populated with the inputs
X contained in each region of the partition of the MAP tree
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# # 2-d Exponential data # (This example is based on random data. # It might be fun to run it a few times) # # get the data exp2d.data <- exp2d.rand() X <- exp2d.data$X; Z <- exp2d.data$Z Xcand <- exp2d.data$XX # fit treed GP LLM model to data w/o prediction # basically just to get MAP tree (and plot it) out <- btgpllm(X=X, Z=Z, pred.n=FALSE, BTE=c(2000,3000,2)) tgp.trees(out) # find a treed sequential D-Optimal design # with 10 more points Xcand.parts <- partition(Xcand, out)
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