Date:         Thu, 26 Jan 2006 13:16:50 +0100
Reply-To:     "adel F." <adel_tangi@YAHOO.FR>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         "adel F." <adel_tangi@YAHOO.FR>
Subject:      Re: Another PROC MIXED question - marginal and conditional
              residuals
Comments: To: David L Cassell <davidlcassell@MSN.COM>
In-Reply-To:  <BAY103-F4C559E5F3FD4307335EBAB0120@phx.gbl>
Content-Type: text/plain; charset=iso-8859-1

  Thanks to JW,PF and DC for their comments.

  When I have done the frequency table for the IV's as suggested by DC (see below), I find few values for the combinations, I found frequencies like 2, 3 and many of one.

  I think, that I need to reduce the number of IV's and  I must have more than 5 number for each combination in order to have a raisonable accurate model.

  Any this case which goodness of fit can I use in order to see the model fit?

  I have another querry related to logistic regression:

  It is important to declare binary and categorical IV as class either in a binary logistic or ordored logistic?
  I have noticed that in NESUG18 (Peter Flom) the categorcial variables are not declared as class.

  Any comments please.
  Thanks for your help
  Adel

David L Cassell <davidlcassell@MSN.COM> a écrit :
  flom@NDRI.ORG wrote:
>With ODS graphics in PROC MIXED, SAS can produce studentized, marginal, and
>raw residuals, each can be conditional or marginal.
>
>How do these relate to the assumptions of the model?
>
>y = Xbeta + Zgamma + epsilon
>
>E(gamma) = E(epsilon) = 0
>V(gamma) = G
>V(epsilon) = R
>
>I understand that studentization and Pearsonization (if that's the word)
>are ways to standardize the raw numbers;
>my question is more about the conditional vs. the marginal. I see that
>(on p. 2764 in the SAS STAT manuals)
>r marginal _i = Y_i -x'_i*betahat
>r conditional_i = r_mi - z'_i*gammahat
>
>this seems to me to suggest that the marginal residuals are somehow about
>G, and the conditional residuals about R.....but I am not at all sure.....

I see that Dale has already given his usual impressive answer. Let me just
toss
some trafe in.

Think about marginal vs. conditional in the same way you think about
'marginal'
when using PROC FREQ. It's *analogous* to an average of the possible
conditional
means - but it is not an unbiased estimator, because of the z'_i*gammahat
part. So 'conditional' goes with 'conditioning on the subject'.

You can convince yourself that the two words actually make sense, if you try
hard
enough. :-)


"I dare say you haven't had much practice," said the queen. "When I was your
age, I always did it for half an hour a day. Why, sometimes I've believed as
many
as six impossible things before breakfast." - Lewis Carroll

David
--
David L. Cassell
mathematical statistician
Design Pathways
3115 NW Norwood Pl.
Corvallis OR 97330

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