in.da | R Documentation |
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
in.da(springs, similarity) rv.da(springs, similarity)
springs |
A matrix of 2D dimensional anchor coordinates, as returned by |
similarity |
A similarity matrix measuring the correlation between Dimensional Anchors |
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.
A measure of the efficiency of the Radviz projection of the similarity matrix onto a set of springs
Yann Abraham
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)
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