in.da {Radviz}

Optimization functions for Dimensional Anchors in Radviz

Description

Visual efficiency of Radviz plots depends heavily on the correct arrangement of Dimensional Anchors. These functions implement the optimization strategies described in Di Caro et al 2012

Usage

in.da(springs, similarity)
rv.da(springs, similarity)

Arguments

springs

A matrix of 2D dimensional anchor coordinates, as returned by make.S

similarity

A similarity matrix measuring the correlation between Dimensional Anchors

Details

Following the recommendation of Di Caro *et al.* we used a cosine function to calculate the similarity between Dimensional Anchors (see cosine for details). The in.da function implements the independent similarity measure, where the value increases as the Radviz projection improves. The rv.da function implements the radviz-dependent similarity measure, where the value decreases as the Radviz projection improves.

Value

A measure of the efficiency of the Radviz projection of the similarity matrix onto a set of springs

Author(s)

Yann Abraham

Examples

data(iris)
das <- c('Sepal.Length','Sepal.Width','Petal.Length','Petal.Width')
S <- make.S(das)
mat <- iris[,das]
sim.mat <- cosine(mat)
in.da(S,sim.mat)
rv.da(S,sim.mat)