Given this general example:
set.seed(1)
data(iris)
iris.rf <- randomForest(Species ~ ., iris, proximity=TRUE, keep.forest=TRUE)
#varImpPlot(iris.rf)
#varUsed(iris.rf)
MDSplot(iris.rf, iris$Species)
I’ve been reading the documentation about random forest (at best of my -
poor - knowledge) but I’m in trouble with the correct interpretation of
the MDS plot and I hope someone can give me some clues
What is intended for “the scaling coordinates of the proximity matrix”?
I think to understand that the objective is here to present the distance
among species in a parsimonious and visual way (of lower dimensionality)
Is therefore a parallelism to what are intended the principal components
in a classical PCA?
Are the scaling coordinates DIM 1 and DIM2 the eigenvectors of the
proximity matrix?
If that is correct, how would you find the eigenvalues for that
eigenvectors? And what are the eigenvalues repreenting?
What are saying these two dimensions in the plot about the different
iris species? Their relative distance in terms of proximity within the
space DIM1 and DIM2?
How to choose for the k parameter (number of dimensions for the scaling
coordinates)?
And finally how would you explain the plot in simple terms?
Thank you for any feedback
Best regards
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