# [R] prcomp( and cmdscale( not equivalent?

David Carlson dcarlson at tamu.edu
Mon Apr 29 16:02:35 CEST 2013

```I may not understand completely, but it seems you have a 45x45 distance
matrix of stimuli and you want to use to determine which stimuli are
similar. Wouldn't hierarchical clustering be a more straightforward
approach?

?hclust

-------------------------------------
David L Carlson
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77840-4352

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Bob Wiley
Sent: Friday, April 26, 2013 4:33 PM
To: r-help at r-project.org
Subject: [R] prcomp( and cmdscale( not equivalent?

Hello,

I have a dilemma that I'm hoping the R gurus will be able to help resolve.
For background:
My data is in the form of a (dis)similarity matrix created from taking the
inverse of normalized reaction times. That is, each cell of the matrix
represents how long it took to distinguish two stimuli from one another-- a
square matrix of 45X45 where the diagonal values are all zero (since this
represents two identical stimuli).

I have been using cmdscale with this matrix as the input--  So:

X = cmdscale(mydata,k=44,add=FALSE,eig=TRUE)\$points returns a 45x34 matrix
because only 34 of the eigenvalues > 0

I then run prcomp on the (transposition of) this matrix:
prcomp(t(X),scale.=TRUE)

The goal is to take the original matrix of inverse reaction times and
transform that data such that we have PCs that show how stimuli are grouping
should reflect how similar the stimuli are to one another.

My concern is that I'm not fully understanding the mathematics behind
cmdscale( and prcomp(, and that I may just be losing a lot of information or
introducting noise? Or is my approach theoretically sound... I've read a TON
on this now but I can't see exactly what R is doing with these two
functions.
thank you!

-bob
JHU

Robert (Bob) Wiley

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