From: Peter Dalgaard <p.dalgaard_at_biostat.ku.dk>

Date: Fri 25 Feb 2005 - 11:48:33 EST

Date: Fri 25 Feb 2005 - 11:48:33 EST

Peter Dalgaard <p.dalgaard@biostat.ku.dk> writes:

*> Bela Bauer <bela_b@gmx.net> writes:
**>
**> > Hi,
**> >
**> > I'm still not quite there with my H-F (G-G) correction code. I have it
**> > working for the main effects, but I just can't figure out how to do it
**> > for the effect interactions. The thing I really don't know (and can't
**> > find anything about) is how to calculate the covariance matrix for the
**> > interaction between the two (or even n) main factors.
**> > I've looked through some books here and I've tried everything that came
**> > to my mind, but I can't seem to be able to figure out an algorithm that
**> > does it for me.
**> >
**> > Could anyone give me a hint about how I could do this?
**> > (I'll append my code at the end, in case that helps in any way...)
**>
**> I have given it to you before: My plan is to drop the explicit formula
**> involving on/off diagonal elements of S and go directly at Box (1954),
**> theorems 3.1 and 6.1, involving eigenvalues of TST', where T is the
**> relevant residual operator. In the case where one of the factors have
*

> only two levels, I believe you just take differences and use the usual

> formula, but more than two levels is tricky.

[moved to r-devel since this is getting technical]

Now I am getting confused: I can reproduce the G-G epsilon in all the cases I have tried but the H-F epsilon eludes me. Consider this SAS code

proc glm;

model bmc1-bmc7= / nouni; repeated visit 7/printe;

This ends up with

Greenhouse-Geisser Epsilon 0.6047 Huynh-Feldt Epsilon 0.7466

This makes OK sense since there are 22 observations

> (22*6*0.6047 -2)/(6*(21-6*.6047))

[1] 0.7466162

However, consider the following small change:

proc glm;

class grp; model bmc1-bmc7= grp / nouni; repeated visit 7/printe;

Now I get

Greenhouse-Geisser Epsilon 0.6677 Huynh-Feldt Epsilon 0.8976

Since we have one less DF for the covariance matrix, I would expect that the H-F epsilon would be

> (21*6*0.6677)/(6*(20-6*.6677))

[1] 0.876696

The discrepancy gets worse as more covariates are added. If bmc1 is moved to the rhs, I get

Greenhouse-Geisser Epsilon 0.6917 Huynh-Feldt Epsilon 0.9533

> (20*5*0.6917-2)/(5*(19-5*.6917))

[1] 0.8643953

Does anyone have a clue as to what is going on here? Is mighty SAS simply doing the wrong thing? The G-G epsilon depends only on the eigenvalues of the observed covariance matrix, so surely the H-F correction should depend only on the dimension and the DF for the empirical covariance matrix?

-- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard@biostat.ku.dk) FAX: (+45) 35327907 ______________________________________________ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-develReceived on Fri Feb 25 11:08:56 2005

*
This archive was generated by hypermail 2.1.8
: Fri 18 Mar 2005 - 09:02:58 EST
*