It is not clear whether your problem is the dimension reduction or plotting the tessellation: the problems are separate. As suggested in the comments, you can use
library(sos)
???"non-metric"
???"Voronoi"
to find where the functions you need are.
# Sample data: a distance matrix
d <- dist( matrix( rnorm(200), nc=10 ) )
# Dimension reduction, via non-metric multidimensional scaling
library(MASS)
r <- sammon( d )
# Plot the Voronoi tessellation
library(tripack)
x <- r$points
plot( voronoi.mosaic(x[,1], x[,2]) )
points(x, pch=13)
Besides principal component analysis (prcomp
)
and multidimensional scaling (MASS::isoMDS
, MASS:sammon
),
you can also look at
isomap (vegan::isomap
),
local linear embedding (lle::lle
),
maximum variance unfolding
or T-distributed stochastic neighbor embedding (tsne::tsne
):
since some of those (Isomap, LLE, MVU) are based on the "neighbourhood graph",
which is not unlike the 2-dimensional tessellation you seek,
they may be more meaningful for your problem.