I'm attempting to code CONTRAST statements in GLIMMIX for a RCBD, factorial, repeated measures design.  There are two experimental treatments ("Treatment_1" and "Treatment_2"), two locations, and three time points (years).  Location and year need to be included as interactive. proc glimmix data=df plots=studentpanel method=rspl; class Year_CAT Location Treatment_1 Treatment_2 Block ID_S_P; model Response_Var = Treatment_1 | Treatment_2 | Location | Year_CAT / ddfm=kr2; random Block(Location); random Year_CAT / residual type=un subject=ID_S_P; /* This works. */ contrast "Overall impact: Treatment 1 (vs. Control)" /* Treatment 1 */ Treatment_1 0 0 0 0 1 1 -2 0; /* (10/14/25 Doesn't work. */ contrast "Site Impact (Location 1): Treatment 1 (vs. Control)" Location 1 0 Treatment_1 0 0 0 0 1 1 -2 0 Location * Treatment_1 0 0 0 0 1 1 -2 0 0 0 0 0 0 0 0 0 / e; run; The second contrast (indicated to not work by the comment above it) appears to be inestimable.  I don't get a warning or error in the log, but no values are produced in the contrast table output (just periods in place of where F/p-values should be).  The coefficients as they appear in the table (procured from passing option "e" to the contrast statement) look as they should.  Edit:  The reason I want an F-value from a contrast statement is because I want a single figure that represents treatment effect magnitude which I can use to compare across different response variables.  The response variables here are various measurement of soil properties, and having a sense of the degree to which different levels of "treatment 1" (e.g., compost or biochar) affects the different measurements of soil properties would be useful to me.    I can't use LSMESTIMATE for this purpose, because the response variables are all on different scales, and so effect magnitudes expressed in absolute terms don't work. Based on the code above, is anything apparent that may be preventing the contrast from working?   Thank you for your time. ... View more

I'm analyzing data from a designed agricultural experiment using linear mixed models.  At one point, I collected data for what I thought would serve a covariate (weed rating) for explaining crop yield, but further analysis revealed overlap between this variable and one of my experimental treatments.  A major hint that these variables might be collinear was that, when both variables were included in the model as predictors for crop yield, the treatment variable was no longer significant (it previously had been consistently significant).  Note that both variables are categorical, and the treatment variable has two levels while the covariate (weed rating) is an ordered variable with 5 levels (1 to 5).    I would like to determine a standard way to demonstrate redundancy between these variables, so that I can justify not including them simultaneously in models.   A chi-squared test (code below) seems to be appropriate.  Is this approach adequate for establishing predictor redundancy?  I'm not using the term "collinearity" here, because I'm under the impression that collinearity applies more to regression and not ANOVA.  I don't think I need to determine VIF here. title "Chi-squared test for predictor redundancy"; proc freq data=df; tables treatment * other_variable / chisq; run; Ultimately, my goal is to establish a causal link between the response (crop yield), the treatment, and weed rating.  I've accomplished part of this by treating the covariate (weed rating) variable as the response variable and modeling that with my experimental treatments.  That confirmed a high degree of correlation (not sure if "correlation" is technically correct to use here, but you know what I mean) between the treatment in question and the weed rating.   Now, I believe that I just need to establish the link between weed rating and yield, and justify not including the redundant treatment in the model.  Thank you for reading. ... View more

I've configured two repeated measures models, one using PROC MIXED and the other using PROX GLIMMIX, as consistently as possible to the best of my understanding.  The experiment is a factorial, RCBD. Trt_Amend_App and Trt_CC are experimental treatments with 8 and 2 levels, respectively. ID_S are sites or locations (2) Block has 4 levels Year_Time is the time component with 7 levels. ID_S_P is the plot ID or subject     proc mixed data=df plots=studentpanel; class Trt_Amend_App Trt_CC Year_Time ID_S Block ID_S_P; model Soil_PAN_LN = Trt_Amend_App | Trt_CC | ID_S | Year_Time; repeated Year_Time / type=un sub=ID_S_P; random Block(ID_S); run; proc glimmix data=df plots=studentpanel method=rspl; class Trt_Amend_App Trt_CC Year_Time ID_S Block ID_S_P; model Soil_PAN_LN = Trt_Amend_App | Trt_CC | ID_S | Year_Time; random Year_Time / residual type=un subject=ID_S_P; random Block(ID_S); /*nloptions maxiter=500; /* Set this to avoid "did not yield a valid objective function error"*/ /*nloptions technique=nrridg;*/ run; The within-subject correlations are complex, and unstructured yields the best (lowest) information criteria results (AIC, BIC).  The MIXED model doesn't execute when using compound symmetry, giving the "WARNING: Stopped because of infinite likelihood" error.    I'm curious as to why I cannot get the GLIMMIX model to execute, and why I get the error "The initial estimates did not yield a valid objective function." every time that I attempt to run it.  To address this I've attempted increasing the iterations (via INITITER and MAXITER, the latter of which is shown commented out).  I've also tried changing the optimization technique to NRRIDG, which I've read is the default for PROC MIXED but not PROC GLIMMIX.  None of that has worked, and I get the valid objective function error each time.  I considered passing PARMS based on results from MIXED, but that seems unpractical given the amount of covariance parameters with unstructured correlation (28).  Why might GLIMMIX be failing where MIXED works? Also, it occurred to me that within-subject measurements may be so irregular that attempting to model within-subject correlation may be pointless.  How might one verify whether there is any reason to use repeated measures period?  When executing the MIXED model with ar(1) structure, the within-subject correlation is less than the estimate for residual, but not very low (see picture).  I could use some practical advice in this regard.   Thanks for reading! ... View more