I am trying to recreate a model that would mimic results a peer obtained using SAS, but in R. The goal of this model would be to determine if there is any variation among parental sets and lines (essentially is there a significant p-value for the variables “set” and “line”). The problems I am having are:
1) The usage of nested data “Line%in%Set” 2) Having to have both fixed and random variables Fixed=Set Random=Line and Block 3) Results I have obtained usually contain significant Blocking effects. Code I have used thus far without being able to replicate the data includes: Fm<-lmer(Score~(1|Line%in%Set)+Set+(1|Block)) (I figured out how to get a p-value, but it didn't yield the same results as those obtained in SAS) Model<-lm(Score~Line%in%Set+Set+Block) Overall, I realize that I might be completely off in the way I am attempting to create this model and any suggestions will be extremely helpful! Also, if it would be more helpful I can add a dput() of my data. -- View this message in context: http://r.789695.n4.nabble.com/Creating-a-model-with-fixed-and-random-variables-tp4672905.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
