PCoA: Principal Coordinates Analysis

In NominalLogisticBiplot: Biplot representations of categorical data

Description

This function calculates principal coordinates analysis using a distante matrix among a set of objets.

Usage

PCoA(dis, r = 2)

Arguments

dis	Distance matrix between a set ob objects.
r	Number of dimensions for the solution.

Value

An object with has some components:

EigenValues	Eigenvalues of the inner products matrix
Inertia	Variance (Inertia) accounted for each dimension
RowCoordinates	Coordinates for the rows in the reduced space
RowQualities	Qualities of representation of the objects. Squared cosines between the points (vectors) in the full space and the points in the reduced space. Values near 1 indicate good quality

Author(s)

Jose Luis Vicente-Villardon,Julio Cesar Hernandez Sanchez

Maintainer: Jose Luis Vicente-Villardon <[e-mail]>

References

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

See Also

NominalDistances

Examples

  
  data(HairColor)
  dis = NominalDistances(data.matrix(HairColor))
  PCoA(dis,2)
  

Example output

Loading required package: mirt
Loading required package: stats4
Loading required package: lattice
Loading required package: gmodels
Loading required package: MASS
$EigenValues
[1] 7.487101e-01 5.492392e-01 3.847255e-01 3.170345e-01 1.439372e-01
[6] 2.778197e-02 5.615604e-18

$Inertia
[1] 3.448007e+01 2.529391e+01 1.771762e+01 1.460028e+01 6.628689e+00
[6] 1.279433e+00 2.586133e-16

$RowCoordinates
            [,1]        [,2]
[1,] -0.35071674 -0.25772261
[2,]  0.31933756  0.24846901
[3,]  0.09130413  0.20561075
[4,]  0.41624778 -0.10782157
[5,] -0.18199117  0.40888205
[6,] -0.51160453 -0.05340979
[7,]  0.21742298 -0.44400785

$RowQualities
             [,1]         [,2]
[1,] 1.642855e-01 8.871383e-02
[2,] 1.856686e-01 1.124043e-01
[3,] 2.166855e-02 1.098856e-01
[4,] 5.465089e-01 3.666948e-02
[5,] 2.301058e-01 1.161510e+00
[6,] 9.421188e+00 1.026783e-01
[7,] 8.418107e+15 3.510628e+16

NominalLogisticBiplot documentation built on May 2, 2019, 6:03 a.m.