NestedCDpca: NestedCDpca

In luisrei/new-cdpca: Clustering and Disjoint Principal Component Analysis

Description

NestedCDpca performs (sequentially) two clustering and disjoint principal components analysis on the given numeric data matrix and returns a list of results.

Usage

NestedCDpca(data, method = "CDpca", class, fixAtt, nnloads = 0, Q, P,
  K, ent = 1, tol, maxit, r, stand)

Arguments

data	data frame (numeric).
method	method to apply. CDpca or RCDpca.
class	vector (numeric) or 0, if classes of objects are unknown.
fixAtt	vector (numeric) or 0, for a selection of attributes.
nnloads	1 or 0, for nonnegative loadings
Q	integer, number of clusters of variables.
P	integer, number of clusters of objects.
K	vector (numeric), representing number of sub-clusters.
ent	integer, fuzzifier parameter (default 1).
tol	real number, small positive tolerance.
maxit	integer, maximum of iterations.
r	number of runs of the cdpca algoritm for the final solution.
stand	integer, 1 to standardize data before applying relaxed FKM (0 otherwise).

Value

firstCDpca: CDpca object. secondCDpca: CDpca objects of second run of CDpca.

Examples

example = NestedCDpca(data, method, class, fixAtt, nnloads, Q, P, K, ent, tol, maxit, r)

luisrei/new-cdpca documentation built on May 17, 2019, 7:45 a.m.