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Date:         Tue, 13 Jul 2010 08:58:03 -0400
Reply-To:     peterflomconsulting@mindspring.com
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Peter Flom <peterflomconsulting@MINDSPRING.COM>
Subject:      PROC MIXED - why would AR(1) model be exactly like CS model?
Content-Type: text/plain; charset="us-ascii"

Hello

I am working with a longitudinal data set. Six waves of data on 37 people. With such data, I usually try CS and AR(1) and sometimes other structures. This time, I get identical results from the two structures (same parameter estimates, same fit indexes etc) and the estimate for the covariance for AR(1) is 0 (although it is not 0 for CS.

PID = ID number

ITT is a class variable for treated vs. control

Anxdep is the dependent variable, a measure of anxiety and depression

Wave is the wave number (1 to 6).

Code for CS:

<<<<<<<<<<

title 'ITT analysis';

title2 'Factor 1: AnxietyDepression';

title3 'Random intercepts only';

title4 'CS covariances';

ods html;

ods graphics on;

proc mixed data = schooler.long plots=studentpanel (marginal);

class pid itt;

model anxdep = itt wave itt*wave/solution residual;

random pid/type = cs subject = pid;

run;

ods graphics off;

ods html close;

>>>>>>>>>>>>>

Code for AR(1)

<<<<<<<<<

title4 'AR(1) covariances';

ods html;

ods graphics on;

proc mixed data = schooler.long plots=studentpanel (marginal);

class pid itt;

model anxdep = itt wave itt*wave/solution residual;

random pid/type = ar(1) subject = pid;

run;

ods graphics off;

ods html close;

>>>>>>>>>>>

What am I missing here?

Thanks

Peter

Peter Flom PhD.

Peter Flom Consulting LLC

5 Penn Plaza, Ste 2342

NY NY 10001

www.statisticalanalysisconsulting.com

www.IAmLearningDisabled.com


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