About palolix

palolix · ‎08-16-2024

Thank you so much for your feedback Jiltao. So I will then use genmod as Ksharp suggested since I only have fixed variables. You are right, the var in the excel file is continious so I will use lognormal for that one, but most of my dependent variables are values from 0-3 or 1-5 with half values sometimes like 1.5. In that case I should then choose multinomial distribution right? Thank you! Caroline

palolix · ‎08-16-2024

Thank you very much for your reply. Im thinking that maybe I could treat variety as subject? If not, I guess it woud be best to use genmod if my data has only fixed effects, and especially if some of my dependent variables can only take values that go on a scale from 1 to 5. That way I could use a beta or binomial distribution, right? Thanks! Caroline

palolix · ‎08-15-2024

Thank you so much for your reply! Strictly speaking, the first 5 fruits for variety BL516 at week 1 are not the same fruits I used to do the measures at week 3, and so forth, but since the variety is the same for week1, 3, and 6, what if I treat variety as the subject? Thanks Caroline

palolix · ‎08-15-2024

Hi Jiltao, Thank you very much for your reply. Since I dont have blocks, that was my concern, not having the intercept/subject line in my model. I dont know what my subject should be and if I have one. I attached an excel sheet with the data that shows the independent var: harvest date, variety, and weeks in storage, and the dep var storage firm avg. To give you some context, the fruits (different varieties) were harvested at two different dates (day 1 and day 55), and stored in a coldroom during 1, 3, and 6 weeks. After that, they were assesed for firmness (firm avg). So I want to use harvest date as repeated measures, to see if the harvest date has an effect on the fruit firmness. I also want to know the effect of the variety and weeks in storage on the firmness. Thank you very much! Caroline

palolix · ‎08-14-2024

Dear SAS Community, I have some data that I want to anlalyze as repeated measures. Since I dont have blocks I didnt include the random intercept/subject= line in my model so I just want to make sure that the model Im using is correct. I would greatly appreciate your input. Harvest is the date the fruits were harvested (2 dates), and what I want to use as repeated measures. Variety is the fruit variety (3 different varieties), and weeks are the number of weeks the fruits were storaged in a coldroom before the measurements like FirmAvg were assesed (after 1 week, 3 weeks, and 6 weeks). proc glimmix data=one; class harvest variety weeks; model FirmAvg=harvest|variety|weeks/dist=lognormal ddfm=km; random harvest/ residual type=cs; run; I would greatly appreciate your feedback. Thank you very much Caroline

palolix · ‎02-01-2024

Thank you StatsMan for your reply. I attached the SAS code I am using. I have four replications (blocks) per treatment. I have also attached a file of the map. Thank you very much for your help!

palolix · ‎01-30-2024

Thank you very much for your feedback StatsMan, The example was very helpful. In my case I have a latin square split block design with depth as the strip factor. I tried using proc mixed but I didn't get any F values. Any idea of what it is wrong? proc mixed; class row col trt depth; model lgRing= row col trt col*trt row*col*trt depth row*depth col*depth trt*depth col*trt*depth row*col*trt*depth/ddfm=satterthwaite; run; Thanks a lot!

palolix · ‎01-29-2024

Dear SAS Community, I have a Latin Square Design with two factors (4 treatments and 5 depths). I am not sure if my code is correct, so I would greatly appreciate if I could get some feedback. Thank you! proc glm; class row col trt depth; model lgRing=row col trt depth trt*depth; lsmeans trt*depth / adjust=tukey lines; contrast '1 v 2 when depth=1' trt 1 -1 0 0; contrast '1 v 3 when depth=1' trt 1 0 -1 0; contrast '1 v 4 when depth=1' trt 1 0 0 -1; contrast '2 v 3 when depth=1' trt 0 1 -1 0; contrast '2 v 4 when depth=1' trt 0 1 0 -1; contrast '3 v 4 when depth=1' trt 0 0 1 -1; run;

palolix · ‎07-14-2023

Thank you very much for the clarification StatsMan!

palolix · ‎07-14-2023

Hi StatsMan, Thank you very much for the explanation. I will definitely try the V option. I'm still a bit confused because I thought that the second random statement with the residual option was the Glimmix way of dealing with repeated measures. Thank you Caroline

palolix · ‎07-13-2023

Thank you Jiltao, You were all right, with 4-way interactions the model was too overwhelmed and that was probably the reason of the warnings (also SAS was taking way too long to deliver results). If I instead analyze it by date, the model is simpler, and I get clean results without any warning. The last question: Is the random statement correct for a RCBD that includes Depth as a strip plot factor? Proc glimmix data=one; class blk Trt Rate Depth; model RL=Trt|Rate|Depth/dist=lognormal ddfm=kr ; random intercept Trt*Rate Depth Trt*Rate*Depth/subject=blk; weight RL_wt; run; Thanks a lot! Caroline

palolix · ‎07-12-2023

Thank you againJiltao! Thats very good to know about the G- and the R-side random effects (I changed that already in the model). I don't want Exp as another factor included in the model, that's why I'm trying to do a weighted analysis, so I can combine the data of the two experiments (which are replications). If I include date in the R-side random statement, should I then remove it from the G-side random statement? Thank you! Caroline

palolix · ‎07-12-2023

Thank you Jiltao, It seems like I will not be able to combine both experiments and will have to analyze them separately. I completely agree that the random statements are too convoluted, but I'm afraid that if I start removing interactions it will no longer fit the original model with the strip factors and so on. When I work with proc glm, I pool error terms if possible, to simplify models, but I don't know how to do something analogous in glimmix. Thank you! Caroline

palolix · ‎07-12-2023

Thank you Jiltao! Ok, I changed that in the model to get new residuals (no warning this time). Now, for the final weighted analysis, should I also eliminate the last interaction (Trt*Rate*Depth*Date) in the random statement, but keeping the repeated measures random statement? Then it should look like this: Proc glimmix data=one; class blk Trt Rate Depth Date; model RL=Trt|Rate|Depth|Date/dist=lognormal ddfm=kr; random intercept Trt*Rate Depth Trt*Rate*Depth Date Trt*Date Depth*Date /subject=blk; random Date/residual subject=blk(Trt*Rate Depth Trt*Rate*Depth Date Trt*Date Depth*Date) type=cs; weight RL_wt; run; Thank you Caroline

palolix · ‎07-12-2023

Thank you Jiltao for your quick reply! Isn't the second random statement necessary to indicate SAS to treat date as repeated measures? Even if I remove it, I still get the same warning and no results. Thank you, Caroline

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