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Date:         Sun, 4 Oct 2009 15:19:21 -0700
Reply-To:     j1een <jess.nagel@YAHOO.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         j1een <jess.nagel@YAHOO.COM>
Subject:      Re: Help with RBCD and SAS code
Comments: To:
Content-Type: text/plain; charset=ISO-8859-1

On Sep 28, 11:12 am, Warren.Schlec...@TPWD.STATE.TX.US (Warren Schlechte) wrote: > Although no expert, I will add my understanding as well. > > With a fixed effect, you are assuming the inference will only apply to > the set of objects sampled. With a random effect, you are assuming that > the set of objects investigated are a sample from a larger population. > Thus, you have a sample, and need to account for the fact that your > sample has variance, and that a sample can only tell you so much about > the larger population. Because of the narrower inference with the fixed > effect idea, you might see a treatment effect that is significant, but > when you broaden the inference space, your variance increases, meaning > your treatment effect may no longer be strong enough to be significant. > > Proc GLM assumes a narrow inference space (Fixed Effects); Proc MIXED > assumes a broad inference space, but narrower inference spaces can be > defined by reducing the number of random effects in the model. > > Warren Schlechte > > -----Original Message----- > From: Steve Denham [mailto:steve...@YAHOO.COM] > Sent: Monday, September 28, 2009 9:07 AM > Subject: Re: Help with RBCD and SAS code > > I would guess that GLM is giving significant results in the type 3 > table, but what do you find in the random section--particularly after > changing to: > > PROC glm data=dataset; > class block treatment; > model samplevar = block treatment block*treatment; > random block block*treatment/test; > run; > > You need the /test option on the random statment to test against the > "proper" mean square. > > By the way, the GLM documentation says this about random effects: > > Note:PROC GLM uses only the information pertaining to expected mean > squares when you specify the TEST option in the RANDOM statement and, > even then, only in the extra tests produced by the RANDOM statement. > Other features in the GLM procedure-including the results of the LSMEANS > and ESTIMATE statements-assume that all effects are fixed, so that all > tests and estimability checks for these statements are based on a > fixed-effects model, even when you use a RANDOM statement. Therefore, > you should use the MIXED procedure to compute tests involving these > features that take the random effects into account; see the section PROC > GLM versus PROC MIXED for Random-Effects Analysis and Chapter 56, The > MIXED Procedure, for more information. > > Steve Denham > Associate Director, Biostatistics > MPI Research, Inc. > > ----- Original Message ---- > From: j1een <> > To: SA...@LISTSERV.UGA.EDU > Sent: Saturday, September 26, 2009 7:50:09 PM > Subject: Help with RBCD and SAS code > > Hey all. > > I have a randomized complete block design for which I am trying to > decide on the correct Proc statement. In a nutshell, I have a block > (3 levels), treatment (4 levels), plot (3 reps within each treatment), > rep (2 within each plot) and samplevar. It's been a while since I've > used SAS. I assume that I can use either GLM or MIXED but when I > tried both, I received different output (i.e., glm outputs highly > significant treatment effect, mixed says no effect). In my design, > treatment is the only fixed variable. Here's the code: > > PROC glm data=dataset; > class block treatment; > model samplevar = block treatment block*treatment; > random block block*treatment; > run; > > OR.... > > PROC MIXED data=dataset; > class block treatment; > model samplevar = treatment; > random block block*treatment; > run; > > Anyone have any idea what I've done wrong? Again, my SAS knowledge is > rusty so I'd appreciate any help! Thanks!

Thanks everyone!

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