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I'm not geting too many answers to this message

in the ordnews list, maybe somebody in the ADElist

could be interested?

Dr. Agustin Lobo

Instituto de Ciencias de la Tierra (CSIC)

Lluis Sole Sabaris s/n

08028 Barcelona SPAIN

tel 34 93409 5410

fax 34 93411 0012

alobo@ija.csic.es

http://pangea.ija.csic.es/alobo

---------- Forwarded message ----------

Date: Fri, 8 Jan 1999 18:18:36 +0100 (MET)

From: Agustin Lobo <alobo@ija.csic.es>

To: ordnews@colostate.edu

Subject: canonical correlation:results (fwd)

Hi!

Although I'm also reading the papers

suggested by Warren Sarle on

"maximum redundancy analysis", "principal

components of instrumental variables", or "simultaneous linear

prediction." and plan to

use this approach as well, I'm now using canonical

correlation analysis in the following way. Please

send me your comments and critics.

I have a set of p "predictor variables" in a matrix X,

and a set of q "response variables" in a matrix Y

with p < q

and for n observations (n >>> p).

I run a canonical analysis in Splus (using cancor, actualy

I have to review that function)

which yields two transformation matrices

which I use to obtain the transformed matrices

X.can and Y.can

I verify that the inverse transformation yields

the original X and Y (o.k, kind of paranoic).

I also verify that the inverse transformation

with only the first 3 components of Y.can produces

a reasonable estimation of Y.

My goal is to predict another set of

responses, Y', from another set of observed

variables, X', according to the following path:

1. Calculate X.can and Y.can as explained above.

2. Calculate the linear regresions between X.can

and Y.can

3. Calculate X'.can by applying the transform to X'.

4. Estimate Y'.can from the regression lines calculated in 2.

5. Calculate Y' by applying the inverse transform to Y'.can

Opinions?

The result is not great. I think this is because only the

first X.can and Y.can (Y.can[,1] and X.can[,1]) have a good

linear correlation, and thus the rest of Y'.can

(Y´can[,2:q]) are not well estimated.

My feeling is that I could do much better because

the cancor approach also implies that the eigenvalues

from whence the transforms for X.can and Y.can come

are the same, but how to use that information?. I mean that somehow

I have more info to estimate Y'can than just the linear

regressions from X.can to Y.can.

Dr. Agustin Lobo

Instituto de Ciencias de la Tierra (CSIC)

Lluis Sole Sabaris s/n

08028 Barcelona SPAIN

tel 34 93409 5410

fax 34 93411 0012

alobo@ija.csic.es

http://pangea.ija.csic.es/alobo

**Next message:**Daniel Chessel: "Coefficients RV (Re : O Raymond)"**Previous message:**Renaud Lancelot: "Re: [S] Canonical correlation analysis for prediction?"**Messages sorted by:**[ date ] [ thread ] [ subject ] [ author ]

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