function to generate coefficient matrix with hierarchical sparsity

function to generate coefficient matrix with hierarchical sparsity

genHierSparseBeta(
  ncats,
  nvars,
  hier.sparsity.param = 0.5,
  avg.hier.zeros = NULL,
  effect.size.max = 0.5,
  misspecification.prop = 0
)

Arguments

ncats	number of categories to stratify on
nvars	number of variables
hier.sparsity.param	parameter between 0 and 1 which determines how much hierarchical sparsity there is. To achieve a desired total level of sparsity among the variables with hierarchical sparsity, this parameter can be estimated using the function 'estimate.hier.sparsity.param'
avg.hier.zeros	desired percent of zero variables among the variables with hierarchical zero patterns. If this is specified, it will override the given hier.sparsity.param value and estimate it. This takes a while
effect.size.max	maximum magnitude of the true effect sizes
misspecification.prop	proportion of variables with hierarchical missingness misspecified

Examples

set.seed(123)

# estimate hier.sparsity.param for 0.15 total proportion of nonzero variables
# among vars with hierarchical zero patterns
# NOT RUN: Takes a long time
# hsp <- estimate.hier.sparsity.param(ncats = 3, nvars = 25, avg.hier.zeros = 0.15, nsims = 100)
# 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)  ))
#> [1] 0.14565

sparseBeta <- genHierSparseBeta(ncats = 3, nvars = 25, hier.sparsity.param = hsp)