in.da: 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
A matrix of 2D dimensional anchor coordinates, as returned by make.S
similarity
A similarity matrix measuring the correlation between Dimensional Anchors
Value
A measure of the efficiency of the Radviz projection of the similarity matrix
onto a set of springs
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.
# NOT RUN {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)
# }