Aggregate selection frequencies over clusters of correlated variables

When several candidate terms measure the same thing - weight and body surface area, say - each one's individual selection frequency understates the cluster's importance, because replicates split between them. This reports how often at least one member of a cluster was selected. The R port of %cluster (bootstrap.clusters.sas in the CORR macro library).

Usage

boot_clusters(x, clusters)

Arguments

x

A boot_selection from boot_select(), or a numeric matrix with one row per replicate and one column per term.

clusters

Named list mapping a cluster name to its member terms.

Value

A data frame with columns cluster, n_any, pct_any, members, sorted by descending n_any.

Details

n_any is not the sum of the members' individual counts: a replicate that selected two members counts once.

The macro also tabulates its ncluster counter - the full distribution of how many members appeared together in a replicate. That is deliberately not reproduced: the per-variable frequencies are already boot_summary()'s job, and "at least one" is the number neither function can derive from the other.

See also

boot_summary() for the per-variable frequencies, and boot_select(), which produces the replicates.

Examples

set.seed(1)
n  <- 300
x1 <- rnorm(n)
df <- data.frame(y = 2 * x1 + rnorm(n), x1 = x1,
                 x2 = rnorm(n), noise = rnorm(n))

fit <- boot_select(df, y ~ x1 + x2 + noise, fit_linear,
                   n_rep = 50, seed = 42)

# x1 and x2 stand in for a correlated pair -- weight and body surface area,
# say. n_any is not the sum of their individual counts: a replicate that
# selected both counts once.
boot_clusters(fit, list(size = c("x1", "x2"), noise = "noise"))
#>   cluster n_any pct_any members
#> 1    size    50     100  x1, x2
#> 2   noise     3       6   noise