if (FALSE) {
library(MASS)
data(Boston)
#pretend the Boston data was an experiment setting
#first pull out the covariates
X = Boston[, 1 : 13]
#begin the greedy design search
ged = initGreedyExperimentalDesignObject(X,
max_designs = 1000, num_cores = 2, objective = "abs_sum_diff")
#wait
res = resultsGreedySearch(ged, max_vectors = 2)
design = res$ending_indicTs[, 1] #ordered already by best-->worst
design
#what is the balance on this vector?
res$obj_vals[1]
#compute balance explicitly in R to double check
compute_objective_val(X, design) #same as above
#how far have we come?
ged
#we can cut it here
stopSearch(ged)
}
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