bqs_select: Select Ranked Cluster Solutions by Quadratic Score

Description

Select solutions from a bqs object based on specified rank and type of score.

Usage

bqs_select(bqs_sol, rank = 1, type = "smooth", rankby = NA, boot_na_share = 0.25)

Value

A named list of all clustering solutions achieving a type score of rank rank when ranked according to rankby criterion, or NULL if no such solution is available in the bqs_sol object. List names correspond to methods' names, and each named entry contains the corresponding clustering method in bqs_sol$methodlist fit on bqs_sol$data.

Arguments

bqs_sol: An object of class bqs containing the clustering solutions to be selected.
rank: An integer >0 specifying the rank of the solution to select. Default is 1.
type: A character string specifying the type of Quadratic Score. Possible values are "hard", "smooth", "oob_hard", "oob_smooth". Default is "smooth".
rankby: A character string specifying the criteria used to rank solutions in bqs. Possible values are "lq", "mean", "1se", or NA (default). See Details.
boot_na_share: A numeric value between (0, 1). Clustering solutions in bqs_sol with a share of NA bootstrap estimates are excluded from ranking. Default is 0.25.

Details

Even if the bqs_sol object is not pre-ranked, the user may specify a ranking criterion to rank clustering solutions dynamically using the rankby argument; this does not influence the bqs_sol ranking. In these instances, the user can also specify boot_na_share as in bqs_rank to exclude solutions based on the proportion of unsuccessful bootstrap estimations. If rankby=NA, the bqs_sol must be pre-ranked.

Examples

Run this code

# \donttest{
# Load data and set seet
set.seed(123)
data("banknote")
dat <- banknote[-1]

# set up kmeans, see help('mset_kmeans')
KM    <- mset_kmeans(K = 2:5)

# set up Gaussian model-based clustering via gmix()
GMIX  <- mset_gmix(K=2:5, erc=c(1, 50 , 100))

# combine tuned methods
mlist <- mbind(KM, GMIX)

# perform bootstrap
# se 'ncores' to the number of available physical cores
res <- bqs(dat, mlist, B = 20, type = "both", rankby=NA, ncores = 1,
           oob = TRUE, savescores = TRUE, saveparams = FALSE)

# Methods are not ranked; this will raise an error
try(bqs_select(res, rank = 1))

# Rank method dynamically
ranked_res <- bqs_select(res, rank = 2, rankby = "lq", boot_na_share = 0.25)
names(ranked_res)
# }