Hi, does anybody knows how to code a 4 Factorial-Split Plot design with proc mixed?
Thank´s a lot!
Caroline
There are examples of using PROC MIXED for split plots in both the online SAS help, plus in the book "SAS System for Mixed Models" by Littell, Milliken Stroup and Wolfinger.
You really haven't given us enough details about the design for us to give more explicit instructions.
Thank you very much for your answer Paige!
I already cheked it on SAS help, but there are only examples with 2 factors, but not with 4. In my design, I want to test the effect of the following variables:
Block
Year (3)
Nematodes (2 species)-wholeplot
Treatment (2)-Plot
Cereal Cultivar (3)-Subplot
Here is how I do it with 3 factors, but I do not know how is the code when I want to add another factor (year)
Proc mixed data=one;
class blk nem trt cultivar;
model lgsprCysts100=
nem
trt
trt*cultivar
cultivar
nem*cultivar
trt*cultivar
nem*trt*cultivar/ddfm=Satterth;
random blk blk*nem blk*nem*trt;
Is Year crossed with the other factors?
I guess, because each year a different cereal was planted. I want to compare the dependent variable between 3 years.
If each year a different cereal was planted, then cereal is nested within year, but year could still be crossed with treatment and nematodes. Could you confirm?
These are the types of details that we require if we are going to provide a meaningful and correct answer.
Yes, I guess cereal could be nested in year, and year could also be crossed with treatment and nematodes.
I think maybe I should analize it as a Split-split plot model in order to include the year, which should be include before block and the rest of the factors. What do you think?
The problem is that you have to analyze the design in an appropriate manner. So if Year is crossed with the other factors, then it can't be a split-split plot (I don't think).
So, I think this is the analysis
Year and Nematode and their interaction is the whole plot, treatment is plot, cereal cultivar nested within Year is subplot.
So this is my analysis
proc mixed;
class year blk nem trt cultivar;
model lgsprCysts100=nem|year trt trt*nem trt*year trt*nem*year cultivar(year) cultivar(year)*nem cultivar(year)*trt cultivar(year)*new*trt;
random blk blk*trt blk*trt*nem blk*trt*year blk*trt*year*nem;
run;
Maybe I'm way off. What do you think, ?
Thanks a lot Paige! its look very good. Maybe I said it wrong, but block should be the whole plot, because each block includes the 3 nematodes, the 2 different treatments and the cultivars, so nematode and treatment should be the plot, and cultivar the subplot.
Sorry to be so slow in replying. That model ought to be OK. Now I'm going to worry more about the dependent variable, and whether this should be done in GLIMMIX with an untransformed DV and an appropriate distribution for counts of cysts.
If it were in GLIMMIX, with a negative binomial distribution for overdispersed counts, we could also get an appropriate covariance error structure to model any time dependence over years.
Steve Denham
Thank you very much for your reply Steve! I think this model should be also ok.
Proc mixed data=one;
where nem>1;
class blk nem trt crop date;
model lgsprCysts100=
nem|trt|crop|date/ddfm=Satterth;
random blk blk*nem blk*nem*trt*crop;
Hi Steve!
I have a Split plot design but with 4 factors; nem, trt, crop and date. Until now I have only done split-split designs, but what comes next when you have to add a forth factor. Is this code ok??
Thanks!!
blk
nem
blk*nem
trt
nem*trt
blk*nem*trt
crop
blk*crop
nem*crop
trt*crop
blk*nem*crop
nem*trt*crop
date
blk*date
nem*date
trt*date
crop*date
blk*nem*trt*date
nem*trt*crop*date;
random blk blk*nem blk*nem*trt blk*crop blk*nem*crop blk*date blk*nem*trt*date/test;
run;
Caroline,
I think there may be some missing factors. Let me see if the design is correct:
Fixed effects: nematode type, treatment, crop
Random effect: block
Repeated effect: date
I assume that there is a single measurement per date for the nem*trt*crop*block cell.
If this is the case, it looks like the following might be what you should consider:
proc mixed;
class nem trt crop blk date;
model nem|trt|crop|date;
random intercept nem|trt|crop/subject=blk;
repeated date/subject=nem*trt*crop*blk type=<insert appropriate type here depending on spacing of dates, cs or csh for uneven spacing, ar(1) or arh(1) for regular spacing;
run;
Appropriate options should be added for solutions, etc., as well as LSmean or LSestimate statements as needed.
Steve Denham
Hi Steve,
thank you very much for your help!
ja, there is a single measurement per date, and and there is a regular spacing for date (harvest 2010, harvest 2011 and harvest 2012). The thing is that is not a repeated measures model, because the crop are not the same every year, so the conditions are not the same every year. How would you code this model with proc glm?
Thanks a lot!
Caroline
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