WeightedPCoA: Weighted Principal Coordinates Analysis

In villardon/MultBiplotR: Multivariate Analysis Using Biplots in R

Description

Weighted Principal Coordinates Analysis

Usage

  WeightedPCoA(Proximities, 
  weigths = matrix(1,dim(Proximities$Proximities)[1],1), 
  dimension = 2, tolerance=0.0001)

Arguments

Proximities	A matrix containing the proximities among a set of objetcs
weigths	Weigths
dimension	Dimension of the solution
tolerance	Tolerance for the eigenvalues

Details

Weighted Principal Coordinates Analysis

Value

data(spiders) dist=BinaryProximities(spiders) pco=WeightedPCoA(dist) An object of class Principal.Coordinates

Author(s)

Jose Luis Vicente-Villardon

References

Gower, J. C. (2006) Similarity dissimilarity and Distance, measures of. Encyclopedia of Statistical Sciences. 2nd. ed. Volume 12. Wiley

Gower, J.C. (1966). Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53: 325-338.

J.R. Demey, J.L. Vicente-Villardon, M.P. Galindo, A.Y. Zambrano, Identifying molecular markers associated with classifications of genotypes by external logistic biplot, Bioinformatics 24 (2008) 2832.

Cuadras, C. M., Fortiana, J. Metric scaling graphical representation of Categorical Data. Proceedings of Statistics Day, The Center for Multivariate Analysis, Pennsylvania State University, Part 2, pp.1-27, 1995.

See Also

BinaryProximities

Examples

data(spiders)
dist=BinaryProximities(spiders)
pco=WeightedPCoA(dist)

villardon/MultBiplotR documentation built on June 5, 2021, 8:55 a.m.