About Dumbo

Dumbo · ‎04-03-2014

Hi Steve, 1. I actually believe that there is a location effect, since location 2 has a higher fertility and regular irrigation whereas location 1 does not. Therefore I tried to test the year effect on each location. Here is my SAS code: proc mixed; class year block treatment cultivar timing; model M1_Ca= year| treatment| cultivar| timing/ddfm=kr; random intercept treatment/subject=block; repeated year timing/subject=block(treatment*cultivar) type=un@cs; lsmeans year| treatment| cultivar| timing/diff; run; I think the random factor is block and block*treatment. I am not sure if it's correct to use UN@CS covariance structure. Since the repeated factor "timing" was not equally spaced, shold I use sp(pow)? 2. I ran the above code, and found year effect is not significant. So I think I can then treat the year as a random effect and ran the test again. Here is my SAS code with year being a random effect: Proc mixed; class year block treatment cultivar timing; model Ca=treatment| cultivar| timing/ddfm=kr; random year block year*block year*treatment block*treatment year*treatment(block); repeated timing/subject=year*block*treatment*cultivar type=sp(pow); run; Am I correct with the random effects and the covariance structure?

Dumbo · ‎04-03-2014

Hi Steve, I ran the code, but got two message: NOTE: Estimated G matrix is not positive definite. NOTE: Asymptotic variance matrix of covariance parameter estimates has been found to be singular and a generalized inverse was used. Covariance parameters with zero variance do not contribute to degrees of freedom computed by DDFM=KENWARDROGER. Can I trust the results despite of these notes? Thank you！

Dumbo · ‎04-02-2014

Thank you Steve! I am not familiar with the Kronecker product method, but I will try to figure out. The random factors are block, block(location) and treatment*block(location), am I correct ? Rui

Dumbo · ‎04-01-2014

I have the following problem: A study was conducted in 2 locations (1, 2) for 2 years (2012, 2013). In each location, we have 4 blocks . Within each block, we have split plot design with repeated measures. Main plot is gypsum treatments, sub plot is different peanut cultivar. Soil samples were taken from each sub plot at planting, mid bloom and harvest. The response is soil Ca content. Objectives: 1. Whether there is difference between locations or years? If not, then I can pool my data through locations and years. 2. Whether there is difference between treatments? 3. Whether there is difference between sampling times? Here is my SAS code, I don't know if it's correct. If I want to test the year effect, I have treat year as a repeated factor. But I've already had repeated measures within each block (timing), can I use two repeated statements at the same time? proc mixed; class year location block treatment cultivar timing; model Ca=year| location| treatment| cultivar| timing/ddfm=kr; random block block(location) treatment*block(location); repeated timing/subject=block(treatment*cultivar) type=csh; repeated year/subject=location(block*treatment*cultivar) type=csh; lsmeans year| location| treatment| cultivar| timing/diff; run; Thanks!

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Re: Split plot design repeated in multiple locations and multiple year...

Re: Split plot design repeated in multiple locations and multiple year...

Re: Split plot design repeated in multiple locations and multiple year...

Split plot design repeated in multiple locations and multiple years

Re: Split plot design repeated in multiple locations and multiple year...

Re: Split plot design repeated in multiple locations and multiple year...

Re: Split plot design repeated in multiple locations and multiple year...

Split plot design repeated in multiple locations and multiple years