cluster.fmmstil.parallel.divisive: Automatic model based clustering via fmmstil in parallel...

In henrylobster/mstil: 'Multivariate Skew t Distribution with Independent Logistic Skewing Function (MSTIL)'

Description

Automatic model based clustering via fmmstil in parallel using divisive hierarchical method 1.

Usage

cluster.fmmstil.parallel.divisive(
  x,
  ncore = 1,
  criteria = c("ICL", "BIC", "AIC"),
  init.cluster.method,
  init.param.method,
  show.progress = TRUE,
  control = list()
)

Arguments

x	matrix of quantiles of size n x k. Each row is taken as a quantile.
ncore	a positive integer, represents the number of cpu threads to be used in parallel. By default 1.
criteria	Either 'ICL', 'BIC', or 'AIC'. Represents the type of information criteria used for model selection. By default 'ICL'.
init.cluster.method	a function of x, K that seperates x into K initial clusters.
init.param.method	a function of x, returns initial parameters.
show.progress	a logical value. If TRUE, progress of the algorithm will be printed in console. By default TRUE.
control	list of control variables, it accepts all control arguments used in fit.fmmstil.r and fit.fmmsil.

Value

a list with components:

res	a list containing details of the best fitted distribution.
record	a list of lists containing details all fitted fmmstil.r.

Examples

# Not run:
# data(RiverFlow)
# cluster.fmmstil.parallel.divisive(as.matrix(log(RiverFlow)))

henrylobster/mstil documentation built on Sept. 25, 2020, 3:48 p.m.