Based on my reading of the MIXED documentation, it does not look like you can obtain externally studentized conditional residuals. The INFLUENCE option produces internally and externally studentized marginal residuals.    The MIXED Procedure | Details | Residuals and Influence Diagnostics      the output dataset "resid" includes the internally conditional studentized residual which is dissimilar from internally conditional studentized residual returned by output dataset "studres" from Influence. I expected to see the same result. Why difference in the result?     Because they are different kinds of residuals. The output dataset "studres" from INFLUENCE contains internally studentized marginal (not conditional) residuals; those will match the internally studentized marginal residuals in the output dataset from OUTPM= , but are of course different than internally studentized conditional residuals from OUTP= .   From a practical point of view, if I couldn't obtain externally studentized conditional residuals, I would use internally studentized conditional residuals.    ... View more

Speculating here....   To me, screening is exploratory. If the objective of this study is to select a smaller number of treatments for future study, I could see doing no adjustment at all. (I'm sure someone somewhere would disagree.)   Or you could explicitly balance the costs of Type I and Type II errors; for example, see Setting an Optimal α That Minimizes Errors in Null Hypothesis Significance Tests; but see LOPSIDED REASONING ON LOPSIDED TESTS AND MULTIPLE COMPARISONS for an opposing opinion.    Of the usual Type I error control methods, I might consider false discovery rate rather than something like Tukey.   I doubt that there is a definitive answer to your question, although there may be something like a consensus in your particular discipline (which might not be the same consensus in another discipline).     ... View more

This is my vision of your experimental design.   You want to assess whether there are differences in mean response among 4 specifically-chosen methods on 3 specifically-chosen farms. Both method and farm are fixed effects factors. The experimental unit for the farm factor is a house (although clearly levels of farm cannot be randomly assigned to houses); house is a random effects factor. Repeated measures on a house (of which there are 4) are randomly assigned to method; these repeated measures units comprise a random effects factor. Essentially, this is a two-way factorial in a split-plot design.   So, given all this, I suggest this model:   /* Fit split-plot model */ proc glimmix data= duckstudyMethodEffect2 plots=(studentpanel boxplot(fixed student)); class house farm method; model wing = farm method farm*method; random intercept / subject=house(farm); lsmeans farm method / diff adjust=simulate(seed=12345); lsmeans farm*method / plot=meanplot(sliceby=method join cl); run; BUT!!!   I do not know what each method measures, and looking at the data you provided, I can see that the scale of measurement is very different for each method (for example, DE is always an integer and the other methods are not). I suspect you are trying to compare apples and oranges here, which is an activity of little value. The measurements for the different methods must have a common scale; otherwise they cannot be sensibly compared.   You have additional issues with these data. You do not have data for all methods in all houses. You have a lot of zero values. You have values that appear unusual relative to the other values (i.e., potential outliers). And you have the minimal level of replication (n=2 houses) so your ability to assess distributional assumptions is very limited.     These analyses will be challenging. But the first challenge is to determine whether the measurement scales for the different methods are actually comparable.   I hope this helps.   ... View more

So I would say that house is a random effects factor, and that method is a fixed effects factor.   One more question about farm: are you interested in just these 3 farms, where the level of farm determines the mean of the response rather than the variance? If so, then house is the experimental unit for the fixed effect of farm.  ... View more

Were the two houses at each farm assigned to some sort of treatment (for example, light cycle)? In other words, what do the two houses represent in your study? Does the level of house (1 or 2, 3 or 4, 5 or 6) affect the mean of the response or the variance of the response. Are you specifically interested in these 6 houses, or are they meant to represent "houses" in general?   Does the level of farm (1 or 2 or 3) affect the mean of the response or the variance of the response? Are you specifically interested in these 3 farms, or are they meant to represent "farms" in general?   I recommend this paper if you are unfamiliar with figuring out what factors are random and what factors are fixed Use and Misuse of Mixed Model Analysis of Variance in Ecological Studies It's biologically friendly 🙂   Were there groups of birds within each house, where each group of birds was assigned to a different method? If so, were the methods assigned at the same time, or in sequence? How many birds were in each group? Were different birds assigned to different groups?       ... View more

I'm sure that your model is not correctly specified, but you have not provided enough information for anyone to suggest an alternative.   What are the roles in your study for Farm, House, and Method? Is each factor a fixed or a random effect? Are any factors nested within any other factors? What is the repeated measure factor, and what design unit is it measured on multiple times? In other words, describe your experimental design.   ... View more

Speculating here...   Because GENMOD is able to plot the simulated paths (as illustrated in Example 46.8 Model Assessment of Multiple Regression Using Aggregates of Residuals), the data for these simulated paths exist somewhere in the guts of the analysis. However, I didn't see anything in ODS Table Names that would write these data to a dataset for subsequent processing. I'd ask SAS Technical Support.   ... View more

Your experimental design involved subjects assigned to three treatment groups, not subjects assigned to two treatment groups. The experimental design determines the statistical model. Post-hoc redefinition of experimental treatments is hardly ever (even never?) a good idea.   In my opinion, the appropriate model specifies three treatment groups with a contrast to compare the mean of groups 2 and 3 to the mean of group 1. ... View more