search for: satterth

Hello all! When you are working with a little more complicated models in SAS PROC MIXED, you often use the /ddfm=satterth call to make sure the df decomposition is done the best way possible. Running the same models in lme, without any special calls, results in warning messages about the df handling. Is anybody out there working with something like the /ddfm=satterth? It would be handy, or are there any reasons no...

...s. I have tried several approaches, but cannot seem to duplicate the results presented in Piepho and Ogutu using R's lme function (but I can reproduce the results using SAS proc mixed). In SAS, the model is fit using: proc mixed method=REML nobound; class year site; model y=w site/ddfm=satterth s; random int/sub=year; random int w/sub=site type=un; run; Any help would be greatly appreciated! Reference: Piepho, H-P. and J.O.Ogutu. 2002. A simple mixed model for trend analysis in wildlife populations. Journal of Agricultural, Biological, and Environmental Statistics, 7(3):350-360....

...ance parameters for lot. Is there anyway to make R not estimate this correlation? Thank you. lmer(y~sor+(sor-1|lot)+(1|wafer:lot),wafer) For those familiar with proc mixed the following SAS code fits the model that I want: proc mixed scoring=4; class sor lot wafer site; model y= sor/ddfm=satterth; random lot(sor)/group=sor; random wafer(lot); run; sor lot wafer site y 1 1 1 1 1 2006 2 1 1 1 2 1999 3 1 1 1 3 2007 4 1 1 2 1 1980 5 1 1 2 2 1988 6 1 1 2 3 1982 7 1 1 3 1 2000 8 1 1 3 2 19...

...mulas for the submatrices Lambda,Delta1 and Delta2 which I can't really paste in here. The SAS code dealing with this model is the following: proc mixed data=rnadeg.pnau; title 'CV structure for PNAU'; class probepos probeno end probe pixelid newprobeid; model logPM=end logpgc / ddfm=satterth; random probeno newprobeid / subject=probe type=cs; lsmeans end / diff cl; run; Any ideas are appreciated a lot since I am kind of stuck at this point. Thank you Tobias Guennel

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid, and 16 probenos nested within each of the probeids. I have specified in the SAS Proc Mixed procedure that t...

...onAdditive model: aov(rv ~ A*B + Error(suj+suj/A+suj/B) Additive model: aov(rv ~ A*B + Error(suj) and also easy with SAS MIXED (I missed some obvious lines): NonAdditive model model vr = A B A*B; random suj A*suj B*suj; repeated / type=cs subj=suj; Additive model; model vr = A B A*B /ddfm=satterth; repeated / type=cs subj=suj; Using LME I do not find any problems to fit the additive model with lme(vr~A*B, random=~1|suj, cor=corCompSymm()) but I have found some difficulties fitting the nonadditive model. Can anyone help me? Thanks in advance. Manuel Ato Dpto. Psic.B?sica y Metod...

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid, and 16 probenos nested within each of the probeids. I have specified in the SAS Proc Mixed procedure that t...

Dear list, I ran a mixed effect model using R 1.4.1 and SAS 8.0 on the SIMS data found in the SASmixed package and found that the degrees of freedoms for fixed effects are very different. From R, df = n - v -1 where n is total # of observations, v is the # of levels for the grouping factor. From SAS df = v -1. Am I wrong about this or can somebody explain which is correct and why? Thanks a