[R] interpretation of MDS plot in random forest

[mbressan at arpa.veneto.it]

sorry, in fact it was a trivial question!

by just peeping into the function I've worked out this simple solution:

MDSplot(iris.rf, iris$Species)
legend("topleft", legend=levels(iris$Species), fill=brewer.pal(3, "Set1"))

thank you

> thanks andy
>
> it's a real honour form me to get a reply by you;
> I'm still a bit faraway from a proper grasp of the purpose of the plot...
>
> may I ask you for a more technical (trivial) issue?
> is it possible to add a legend in the MDS plot?
> my problem is to link the color points in the chart to the factor that was
> used as response to train rf, how to?
>
> best
>
> max
>
>> Yes, that's part of the intention anyway.  One can also use them to do
>> clustering.
>>
>> Best,
>> Andy
>>
>> -----Original Message-----
>> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
>> On Behalf Of Massimo Bressan
>> Sent: Monday, December 02, 2013 6:34 AM
>> To: r-help at r-project.org
>> Subject: [R] interpretation of MDS plot in random forest
>>
>> 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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>
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