Here are comments from two of my colleagues at NC State in response to
Rickie Domangue's message about proc mixed (see below).

Jackie Dietz


>From Joy Smith:
-------------------------------
There are some more examples for proc mixed on the SAS web site.

Here is a link to the statistical samples: 
http://ftp.sas.com/techsup/download/stat/

There also some in the Proc Mixed section of the SAS OnLineDoc.
OnlineDoc is
available on the SAS web site at:
http://v8doc.sas.com/sod_register.html
-------------------------------


>From Dave Dickey:
-------------------------------
I believe the reason you do not see time*block in a random statement 
is that this would imply an additive random component that changes 
each time a new (block,time) level is encountered.  If you treat 
this as a repeated measures type of situation, then a term like
time(block*A) would seem to be sufficient to model this kind of
effect.  

The random statement sets up some matrices of (often) dummy variables
and you could think of the repeated statement in a similar vein. You 
essentially want to avoid specifying two or more effects whose columns 
span the same space (your dependency question). 

In fact the effect of the repeated statement can often be reproduced by 
a careful invocation of a corresponding random statement.  In the
program 
below I generate and analyze data from a split plot with repeated
measures on the whole plots. Notice that I have used the RANDOM 
statement rather than the repeated statement to get my repeated 
measures analysis on the whole plots.  My model is 
  Y(i,j,k,t) = mu + Block(i)+ A(j) + D(i,j) + w(i,j,,t)
             + B(k) + AB(j,k) + e(i,j,k,t)
As it stands, the whole plot error is D(i,j)+w(i,j,t) 
and I have let the w(i,j,t) have an autoregressive order 1 structure. 
You could also have a model without the D, or of course fit both 
and test D (I would recommend not using the default Wald tests - 
do a likelihood ratio calculation)
   If you follow this program more or less, you should not have 
any dependency problems.  
D. A. Dickey
NCSU

===============PROGRAM ===================================
data a; et=normal(1827655)/sqrt(1-.5*.5);  
  do block = 1 to 4;  BEFF=8*normal(9876655); 
     do A = 1 to 2;
          EA = 2*normal(1827655);
          do time=1 to 5; 
          ET = .5*et + normal(1827655); 
          Ypart = BEFF + 2*A +EA + ET; 
        do B = 1 to 3; 
          Y=Ypart - 3*B +A*B + normal(1827655); 
        output; end;end; end; end; 
proc glm; class a b block; 
model Y = block a block*a b a*b; 
proc mixed; class block a b time; 
  model Y = a|b; 
random block  a*block; 
random time(a*block)/type=ar(1); 
repeated time/subject=a*b*block
    /*type=ar(1)*/; 
   * This would fit an AR(1) structure within the split plots ; 
run; 
-------------------------------

> Rickie Domangue wrote:
> 
> > Can anyone help or provide resources for
> > help with the following mixed model analysis questions
> > in SAS proc mixed?
> >
> > 1.  Understanding when specifications for the random
> >     and repeated statements for repeated measures designs
> >     result in confounding of certain variance components.
> >     SAS System for Mixed Models provides a couple of examples.
> >     Are there other places that I could look?  In particular,
> >     I'm looking for the situation when the design is a split
> >     plot with whole plots blocked and repeated measures taken
> >     on the whole plots.
> >
> > 2.  In a split plot design, with levels of factor A assigned
> >     to whole plots, with whole plots blocked, and repeated
> >     measures over time on the whole plots, including or not
> >     including the block x time interaction in the
> >     random statement. In most examples of split plot design analysis
> >     in proc mixed that I see, this term is omitted.
> >
> > Thanks for any help.
> >
> > Rickie Domangue
> > Dept. of Math & Stats
> > James Madison University
> >
> > email:  [EMAIL PROTECTED]
> > phone:  540-896-4232
> >
> > --
> > Domangue, Rickie James
> > [EMAIL PROTECTED]

-- 
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  E. Jacquelin Dietz                       (919) 515-1929  (phone) 
  Department of Statistics, Box 8203       (919) 515-1169  (FAX) 
  North Carolina State University      
  Raleigh, NC  27695-8203   USA            [EMAIL PROTECTED] 

  Street address for FedEx:  
  Room 210E Patterson Hall, 2501 Founders Drive
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