# NOT RUN {
set.seed(123)
# estimate hier.sparsity.param for 0.15 total proportion of nonzero variables
# among vars with hierarchical zero patterns
# }
# NOT RUN {
hsp <- estimate.hier.sparsity.param(ncats = 3, nvars = 25, avg.hier.zeros = 0.15, nsims = 100)
# }
# NOT RUN {
# the above results in the following value
hsp <- 0.6341772
# check that this does indeed achieve the desired level of sparsity
mean(replicate(100, mean(genHierSparseBeta(ncats = 3,
nvars = 25, hier.sparsity.param = hsp) != 0) ))
sparseBeta <- genHierSparseBeta(ncats = 3, nvars = 25, hier.sparsity.param = hsp)
# }
# NOT RUN {
hsp2 <- estimate.hier.sparsity.param(ncats = 2, nvars = 100,
avg.hier.zeros = 0.30, nsims = 50) # 0.5778425
hsp3 <- estimate.hier.sparsity.param(ncats = 3, nvars = 100,
avg.hier.zeros = 0.30, nsims = 50) # 0.4336312
hsp4 <- estimate.hier.sparsity.param(ncats = 4, nvars = 100,
avg.hier.zeros = 0.30, nsims = 50) # 0.2670061
hsp5 <- estimate.hier.sparsity.param(ncats = 5, nvars = 100,
avg.hier.zeros = 0.30, nsims = 50) # 0.146682
# }
# NOT RUN {
# 0.07551241 for hsp6
# }
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