Hi all,
I am having difficulty coming up with a good way to describe the GLIMMIX model statements in the methods section of my research articles.
This is what I have now, but I have been asked if there is a better way to explain the analysis. The journal is an applied weed science journal, so in-depth explanations are not encouraged.
"All rush skeletonweed count data were converted from whole-plot counts to plants m-2 and analyzed using PROC GLIMMIX in SAS® Statistical Software (SAS Institute 2019) with the Laplace method of maximum likelihood estimation. The random statement for the full-model, including both treatment and location, was “intercept / subject=block(location),” and “intercept / subject=block” for analysis of treatment within each location (Stroup 2013). Differences between lsmeans (PDIFF) were used to compare treatments (α=0.05)."
Any thoughts?
The thing to remember is that the following two statements are equivalent as far as MM reporting:
random intercept/subject=block;
random block;
It is just that the first statement results in faster calculations, and is required if method=quad
But it still boils down to "Block was fit as a random effect."
And random intercept/subject=block(location) is "Location within block was fit as a random effect."
So boiling this all down, would this make sense?: "Treatments were randomly assigned within blocks, and multiple locations within each block were measured. Treatment was fit as a fixed effect, while block and location within block were fit as random effects."
You could probably look through the ecology or psychology literature to find examples using the phrase 'random intercept', especially where R packages were used to analyze the results. Those might be enough to get you started.
SteveDenham
There are other reasons to use GLIMMIX aside from data that are non-Gaussian. There is a wider variety of optimizers (tech=) and methods (MIXED restricted to REML, ML and MIVQUE0. GLIMMIX has a functional and flexible COVTEST statement.
And there are reasons to use MIXED rather than GLIMMIX, primarily related to the Kronecker product variance/covariance structure for doubly repeated measures.
In this case, where there is interest in reporting the random effects, the COVTEST statement may be of particular interest.
SteveDenham
The thing to remember is that the following two statements are equivalent as far as MM reporting:
random intercept/subject=block;
random block;
It is just that the first statement results in faster calculations, and is required if method=quad
But it still boils down to "Block was fit as a random effect."
And random intercept/subject=block(location) is "Location within block was fit as a random effect."
So boiling this all down, would this make sense?: "Treatments were randomly assigned within blocks, and multiple locations within each block were measured. Treatment was fit as a fixed effect, while block and location within block were fit as random effects."
You could probably look through the ecology or psychology literature to find examples using the phrase 'random intercept', especially where R packages were used to analyze the results. Those might be enough to get you started.
SteveDenham
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