TuckerFactors: Choice of the number of Tucker 3 factors for FPDC

In FPDclustering: PD-Clustering and Factor PD-Clustering

Choice of the number of Tucker 3 factors for FPDC

Description

An empirical way of choosing the number of factors for FPDC. The function returns a graph and a table representing the explained variability varying the number of factors.

Usage

TuckerFactors(data = NULL, nc = 2)

Arguments

data	A matrix or data frame such that rows correspond to observations and columns correspond to variables.
nc	A numerical parameter giving the number of clusters

Value

A table containing the explained variability varying the number of factors for units (column) and for variables (row) and the corresponding plot

Author(s)

Cristina Tortora

References

Kiers H, Kinderen A. A fast method for choosing the numbers of components in Tucker3 analysis.British Journal of Mathematical and Statistical Psychology, 56(1), 119-125, 2003.

Kroonenberg P. Applied Multiway Data Analysis. Ebooks Corporation, Hoboken, New Jersey, 2008.

Tortora C., Gettler Summa M., and Palumbo F.. Factor pd-clustering. In Lausen et al., editor, Algorithms from and for Nature and Life, Studies in Classification, Data Analysis, and Knowledge Organization DOI 10.1007/978-3-319-00035-011, 115-123, 2013.

See Also

T3

Examples

## Not run: 
# Asymmetric data set example (with shape=3).
data('asymmetric3')
xp=TuckerFactors(asymmetric3[,-1], nc = 4)


## End(Not run)

## Not run: 
# Asymmetric data set example (with shape=20).
data('asymmetric20')
xp=TuckerFactors(asymmetric20[,-1], nc = 4)

## End(Not run)

FPDclustering documentation built on Aug. 31, 2022, 5:09 p.m.