I have a 3-way factorial with a 3-way interaction between YEAR, LOCATION, and TREATMENT. If I use SliceDiff to compare simple effect TREATMENT lsmeans at each level of YEAR at each LOCATION, does that preclude me from comparing certain simple effect TREATMENT lsmeans between certain YEARs? In other words, the interaction graphically indicates that treatments differ at each year, but  also between years. I am not trying to look at every simple effect combination and risk a Type I error, just comparisons between the ones that help explain the interaction. The old GLM approach as I recall would have been to only analyze one factor at each level of the other factor, but this limits telling the whole story.  Can I specify LSMEANS YEAR|LOCATION|TREATMENT / SLICEDIFF = LOCATION*YEAR and also do a second run with SLICEDIFF=LOCATION*TREATMENT and not violate any of the rules? ... View more

We have done long-term weed control trials over three to six years using density counts each year to measure efficacy of the treatments. Each year a new group of stems from underground rhizomes emerge in each plot. I am not sure that the counts each year are a true repeated measure because we are not measuring (counting) the same stems. If it were length of growth of the same stem over time, then I could see that as a repeated measure, but we are counting a new crop of stems each year. Does anyone have any thoughts on this?    ... View more

I am working with a dataset from an agricultural weed management study. The data are counts per sq. meter. There are 2 study sites, 6 treatments, 4 replications per treatment, and 2 subsamples per plot. Plot is the experimental unit and I am considering study site as a random effect. This is a 6-year trial but I have already determined that I need to evaluate each year separately because of an interaction with year sequence and treatment. Using a negative binomial distribution doesn't always work because of zeros resulting from 100% effectiveness of the treatments.    This is the model that seems to work the best and produce dimensions that seem realistic.   ods graphics on; proc glimmix data=ltf method=laplace plots=studentpanel; by seq; class site seq trt sub rep; tctm2 = sqrt(ctm2); model tctm2 = trt / dist=normal link=identity; random intercept / subject=rep(sub); lsmeans trt / ilink diff lines plot=meanplot; run; quit; ods graphics off;   It produces this structure. (site=field site, seq=year, trt=treatment, sub=subplot, rep=replication)   Class Level Information Class Levels Values Site 2 ED ST Seq 1 6 Trt 6 1 2 3 4 5 6 sub 2 1 2 Rep 4 1 2 3 4 Number of Observations Read 96 Number of Observations Used 96 Dimensions G-side Cov. Parameters 1 R-side Cov. Parameters 1 Columns in X 7 Columns in Z per Subject 1 Subjects (Blocks in V) 8 Max Obs per Subject 12   If I try to add "site" as a random effect, the model structure seems all wrong, as follows.   ods graphics on; proc glimmix data=ltf method=laplace plots=studentpanel; by seq; class site seq trt sub rep; tctm2 = sqrt(ctm2); model tctm2 = trt / dist=normal link=identity; random intercept / subject=site; random intercept / subject=rep(sub); lsmeans trt / ilink diff lines plot=meanplot; run; quit; ods graphics off;   Class Level Information Class Levels Values Site 2 ED ST Seq 1 6 Trt 6 1 2 3 4 5 6 sub 2 1 2 Rep 4 1 2 3 4 Number of Observations Read 96 Number of Observations Used 96 Dimensions G-side Cov. Parameters 2 R-side Cov. Parameters 1 Columns in X 7 Columns in Z 10 Subjects (Blocks in V) 1 Max Obs per Subject 96   The number of subjects is wrong, and the number of obs per subject is the total number of obs.   Any thoughts on this analysis? Thanks! ... View more

I recently started a discussion about difficulty installing SAS 9.4 on a Windows 11 platform. There were a couple comments but none with a solution (even though one was accepted as a solution by accident, somehow).    After communicating with SAS technical support directly, it was discovered that Windows 11 requires SAS 9.4 to be at least version M7, and the latest version is actually M8. The version I was trying to install was an old M4 version and that is apparently why the install failed.   Moral of the story, if you are using Windows 11, use the proper version of 9.4.      ... View more

I am not sure where to go with this, but I recently purchased a new desktop PC running Windows 11 for work. The PC is connected to a university VPN.  When I tried to install 9.4 on this PC, it had issues with some of the 64-bit components and would not complete the installation or even load. This was the error message I got after the last attempt at installing and running the program.    Error: Configuration file "C:\Program Files\SASHome\SASFoundation\9.4\nls\en\sasv9.cfg"" is unreadable.   Our university IT person told me they have had this problem before and they have had to a complete wipe of the hard drive and reinstall all software!    Really, this is where we are at???    Sincerely frustrated! ... View more

I have a question regarding polynomial regression interpretation. In the quadratic model below, all coefficients are significant. But, in the cubic model, the linear and squared coefficients are not significant but the cubic coefficient is significant. The cubic model is a slightly better fit than the quadratic, but not sure if the cubic model should be used given the significance issues. I would greatly appreciate any thoughts on this matter.  Thanks!   FYI: day2 = day*day; day3=day*day*day.   *Quadratic model*; ods graphics on; proc reg data=no2019; by year; model sps = day day2 / lackfit; output out=b student=sresid stdp=stderr p=yhat r=yresid; run; ods graphics off;   The REG Procedure Model: MODEL1 Dependent Variable: SPS YEAR=17 Number of Observations Read 450 Number of Observations Used 450   Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model 2 1375.32618 687.66309 192.34 <.0001 Error 447 1598.17313 3.57533     Lack of Fit 2 17.24169 8.62084 2.43 0.0895 Pure Error 445 1580.93144 3.55265     Corrected Total 449 2973.49931         Root MSE 1.89085 R-Square 0.4625 Dependent Mean 4.85756 Adj R-Sq 0.4601 Coeff Var 38.92605       Parameter Estimates Variable DF Parameter Estimate Standard Error t Value Pr > |t| Intercept 1 7.56219 0.20706 36.52 <.0001 DAY 1 -0.27141 0.04139 -6.56 <.0001 day2 1 0.00320 0.00149 2.15 0.0321         The REG Procedure Model: MODEL1 Dependent Variable: SPS YEAR=17         *cubic model*; ods graphics on; proc reg data=no2019; by year; model sps = day day2 day3 / lackfit; output out=b student=sresid stdp=stderr p=yhat r=yresid; run; ods graphics off;     The REG Procedure Model: MODEL1 Dependent Variable: SPS YEAR=17 Number of Observations Read 450 Number of Observations Used 450   Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model 3 1391.51323 463.83774 130.77 <.0001 Error 446 1581.98608 3.54705     Lack of Fit 1 1.05463 1.05463 0.30 0.5861 Pure Error 445 1580.93144 3.55265     Corrected Total 449 2973.49931         Root MSE 1.88336 R-Square 0.4680 Dependent Mean 4.85756 Adj R-Sq 0.4644 Coeff Var 38.77181       Parameter Estimates Variable DF Parameter Estimate Standard Error t Value Pr > |t| Intercept 1 7.22585 0.25946 27.85 <.0001 DAY 1 -0.07735 0.09976 -0.78 0.4385 day2 1 -0.01591 0.00907 -1.75 0.0800 day3 1 0.00047628 0.00022295 2.14 0.0332       The REG Procedure Model: MODEL1 Dependent Variable: SPS YEAR=17     ... View more