Displaying 20 results from an estimated 21 matches for "ddfm".
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dfm
2004 Feb 23
0
Is there a /ddfm=satterth for R?
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 r...
2011 Jan 27
4
HLM Model
Hi
I am trying to convert SAS codes to R, but some of the result are quite
different from SAS.
When I ran proc mixed, I have an option ddfm=bw followed by the model. How
can I show this method in R?(I am thinking that this maybe the reason that I
can't get the similar results)
below is my SAS codes:
proc mixed data=test covtest empirical;
class pair grade team school;
model score = trt pair grade school/ solution covb ddfm=bw ;...
2017 Sep 29
5
Converting SAS Code
...elete;
run; quit;
Title'2016 Asilomar Stress Relief champagin yield';
proc mixed method=reml data=yield;
class rep Management Foliar_Fungicide Chemical_Treatment;
model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment
Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr;
random rep rep*Management rep*Management*Foliar_Fungicide;
lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff;
ods output diffs=ppp lsmeans=means;
ods listing exclude diffs lsmeans;
run; quit;
%include'C:\Users\harmon12\Desktop\pdmix800.sas';
%pdmix800(ppp,means,alpha...
2011 Dec 27
1
Longitudinal data
...model the time
using regression. Only compare the groups differ in terms of
milk production. There are many missing observations.
Because the data are correlated I used the SAS program:
proc mixed data=univar method=reml;
class RACA GRUPO APELIDO Dias;
model Prod = GRUPO / solution DDFM=BW;
repeated Dias / type=arh(1) subject=APELIDO r rcorr;
lsmeans GRUPO / pdiff adjust=tukey;
run ;
But, I want use R. What would be the equivalent in R?
Thank you.
--------------------------------------
Silvano Cesar da Costa
Departamento de Estat?stica
Universidade Estadual de Londr...
2005 Apr 24
1
random interactions in lme
...re are multiple
terms or random interactions, the documentation available just doesn't
hold up.
Here's an example: a split block (strip plot) design evaluated in SAS
with PROC MIXED (an excerpt of the model and random statements):
model DryMatter = Compacting|Variety / outp = residuals ddfm =
satterthwaite;
random Rep Rep*Compacting Rep*Variety;
Now the fixed part of that model is easy enough in lme:
"DryMatter~Compacting*Variety"
But I can't find anything that adequately explains how to simply add
the random terms to the model, ie "rep + rep:compacting + rep:va...
2009 Jun 23
1
nested cross-sectional design using lmer or nlme
...iate analysis of such studies (Analysis of
data from group-randomized trials with repeat observations on the same
groups, Stats in Med. 17, 1581-1600). They offer three examples of
SAS code - one of which is as follow:
proc mixed;
class cond unit timecat;
model y=cond timecat cond*timecat/ddfm=res;
random int timecat/subject=unit(cond);
run;
cond is 0/1 corresponding to control/intervention
timecat is 0/1 corresponding to baseline/follow-up
unit is 1 to 39 and identifies the communities.
and y is a continuous score
I've read the random statement as cond nested within unit and cro...
2003 Apr 02
2
lme parameterization question
...'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)...
2017 Sep 29
4
Converting SAS Code
...champagin yield';
>>
>> proc mixed method=reml data=yield;
>>
>> class rep Management Foliar_Fungicide Chemical_Treatment;
>>
>> model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment
>> Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr;
>>
>> random rep rep*Management rep*Management*Foliar_Fungicide;
>>
>> lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff;
>>
>> ods output diffs=ppp lsmeans=means;
>>
>> ods listing exclude diffs lsmeans;
>>
>> run; quit;...
2017 Sep 29
0
Converting SAS Code
...6 Asilomar Stress Relief champagin yield';
>
> proc mixed method=reml data=yield;
>
> class rep Management Foliar_Fungicide Chemical_Treatment;
>
> model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment
> Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr;
>
> random rep rep*Management rep*Management*Foliar_Fungicide;
>
> lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff;
>
> ods output diffs=ppp lsmeans=means;
>
> ods listing exclude diffs lsmeans;
>
> run; quit;
>
> %include'C:\Users\ha...
2005 Sep 29
1
lmer random effect model matrix question
...ovariance 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...
2006 Jun 19
2
Nested variance-covariance matrix in Multilevel model
...formulas 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
2017 Aug 10
4
PROC MIXED RANDOM equivalence in R nlme
...NPUT study $ vehicle $ thc rv t5 t9 ar ol ox su bz;
/* CREATE NEW VARIABLES */
ln_thc = log (thc);
new = study||vehicle;
/* PERFORM ANALYSIS */
PROC MIXED DATA=emiss MAXITER=1000 CONVH=1E-8 METHOD=REML NOCLPRINT
NOITPRINT;
CLASS new;
MODEL ln_thc = rv t5 t9 ar ol ox su bz
/S DDFM=RES;
RANDOM int rv t5 t9 ar ol ox su bz
/SUB=new;
RUN;
------------------------------------------------------------------
The R code I've devised for the PROC MIXED statement is shown below:
------------------------------------------------------------------
FitTHC <...
2006 Jun 30
1
lme and SAS Proc mixed
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 procedu...
2011 Apr 16
3
lme4 problem: model defining and effect estimation ------ question from new bird to R community from SAS community
...umerical expression has 20 elements: only the first used
2: In sire:dam : numerical expression has 20 elements: only the first used
**********************how can I estimate the BLUP effects?*************
#equavalent code in SAS
proc mixed data=genetic_evaluation;
class sire dam;
model adg= / ddfm=kr;
random sire dam(sire);
estimate 'sire 1 BLUP "broad" '
intercept 1 | sire 1 0;
estimate 'sire 1 BLUP "narrow" '
intercept 2 | sire 2 0
dam(sire) 1 1 0 0 0 0 0 0 0 0 / divisor=2;
estimate 'sire 1 BLUP wi...
2017 Aug 11
0
PROC MIXED RANDOM equivalence in R nlme
...;
> /* CREATE NEW VARIABLES */
> ln_thc = log (thc);
> new = study||vehicle;
>
> /* PERFORM ANALYSIS */
> PROC MIXED DATA=emiss MAXITER=1000 CONVH=1E-8 METHOD=REML NOCLPRINT
> NOITPRINT;
> CLASS new;
>
> MODEL ln_thc = rv t5 t9 ar ol ox su bz
> /S DDFM=RES;
>
> RANDOM int rv t5 t9 ar ol ox su bz
> /SUB=new;
> RUN;
> ------------------------------------------------------------------
>
> The R code I've devised for the PROC MIXED statement is shown below:
>
> ------------------------------------...
2017 Sep 29
0
Converting SAS Code
...t;>
>>> proc mixed method=reml data=yield;
>>>
>>> class rep Management Foliar_Fungicide Chemical_Treatment;
>>>
>>> model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment
>>> Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr;
>>>
>>> random rep rep*Management rep*Management*Foliar_Fungicide;
>>>
>>> lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff;
>>>
>>> ods output diffs=ppp lsmeans=means;
>>>
>>> ods listing exclude diffs lsmean...
2003 Oct 04
2
mixed effects with nlme
...V:
NonAdditive 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?sic...
2006 Jun 30
0
SAS Proc Mixed and lme
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 procedu...
2017 Sep 29
0
Converting SAS Code
...#39;2016 Asilomar Stress Relief champagin yield';
proc mixed method=reml data=yield;
class rep Management Foliar_Fungicide Chemical_Treatment;
model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment
Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr;
random rep rep*Management rep*Management*Foliar_Fungicide;
lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff;
ods output diffs=ppp lsmeans=means;
ods listing exclude diffs lsmeans;
run; quit;
%include'C:\Users\harmon12\Deskt...
2002 Mar 31
1
lme degrees of freedoms: SAS and R
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