I believe this is a known problem and there is a hotfix that fixes the issue.   @SAS_Rob Can you post a link to the appropriate hotfix so that this thread contains the information needed to resolve this issue? ... View more

Product suggestions can be made at SAS Product Suggestions - SAS Support Communities ... View more

> I do not think that the reason why I am conducting such matrix operations is a matter worth disclosing in detail.   OK, that's fine. I was just trying to be helpful. Good luck on your project.  ... View more

Someone else has already discussed how to store/load matrices with a common prefix, but I think a more important issue is WHY you would want to do such a thing? It makes it difficult to write code to analyze the various covariance matrices.    Base SAS programmers experience this same difficulty if they try to write datasets that are named DS1-DSn. The better paradigm is to have one dataset that contains an indicator variable and use BY-group processing to analyze the groups in the data.   For example, if you have matrices cov1-cov5, and you want to analyze them (maybe convert them to correlation matrices or compute eigenvectors), it will be difficult to write code that analyzes cov1-cov5, but it is easier to write code that extracts the various covariance matrices and analyze them in a loop.   Please let us know about your application so we can suggest a better way to structure your analysis. ... View more

I believe the correct date for this is Thursday 14NOV2024. ... View more

I assumed he was talking about the basic SODA. Yes, you are correct that professors who register a class can apply to get EM. For a complete list of what products are available in SODA, see https://www.sas.com/en_us/software/on-demand-for-academics/features-list.html  ... View more

You might be thinking of the old menu in the SAS Windowing System (DMS)  Solutions->Analysis->Interactive Data Analysis which launched SAS/INSIGHT software. That software was discontinued many years ago.    The SAS On-Demand for Academics software is essentially a version of the SAS Studio GUI. You can use tasks to create standard analyses. In the left pane, select  Tasks and Folders->Tasks, and you will see standard analyses and graphics such as frequency analysis, correlation analysis, regression analysis, and so on. However, be aware that these produce STATIC analyses, unlike the dynamically linked analyses in SAS/INSIGHT.   ... View more

I think you are asking where to find an estimate for the sigma^2 term in the formula among the PROC GLMMIX output.   If you add the SOLUTION option to the MODEL statement, you should get a ParameterEstimates table. The last row has an estimate of the scale parameter. I believe that the estimate is for the sigma^2 term in the formulas.   I don't use PROC GLIMMIX very often, so perhaps a GLIMMIX expert can confirm? ... View more

I think you need to use the JUSTIFY=LEFT option for the STEP function instead of JUSTIFY=CENTER. That will make the step function to increase when it gets to the location of the state that awards the electoral votes. ... View more

Nice application of the little-used WATERFALL statement in PROC SGPLOT. This chart type is also called a "cascade chart." It is useful for visualizing cumulative sums. For readers who have never used a cascade plot, see Create a cascade chart in SAS - The DO Loop ... View more

Thanks for posting. Your code is beautifully written and commented. I hope others will find it useful.   One small comment. I noticed the following lines in the LPD_CORRELATED_DEFAULT.sas file: inverse_cdf = j(1,&SIMULATIONS-1,.); do i = 1 to ncol(inverse_cdf); call symputx("j", i); inverse_cdf[i] = quantile('normal',num(symget("j"))/&SIMULATIONS); end; This code uses SYMPUTX to create a macro variable "j", then immediately gets the value, converts it to a numeric value, and uses it.  A more straightforward method is to use  inverse_cdf[i] = quantile('normal',i/&SIMULATIONS); directly in the loop.   And if you want to improve the efficiency of your SAS IML programs, you can try to replace loops with vector operations. For example, you can eliminate the loop altogether and instead use  i = 1:(&SIMULATIONS-1); inverse_cdf = quantile('normal',i/&SIMULATIONS); Best wishes! ... View more

If you are getting 2 estimates, it means that you are using 3 knots for the splines. The procedure probably outputs a "SplineKnots" table, which will show the 3 values of the continuous variable at which the knots are placed. For example, see this example:   proc glmselect data=sashelp.cars; effect CONUTs = spline(Weight / details naturalcubic basis=tpf(noint) knotmethod=equal(3)); /* <== NOTE: 3 equally spaced knots */ model mpg_city = CONUTs / selection=none; /* fit model by using spline effects */ ods select SplineKnots ParameterEstimates; quit; The three spline components are the Intercept, CONUTs_1, and CONUTS_2, The p-values indicate whether each component in the model is a significant predictor of the response variable. For more about the spline components (and how to visualize them), see https://blogs.sas.com/content/iml/2019/10/16/visualize-regression-splines.html    ... View more

There are others who have more experience with crop science than I do, but it looks like you could fit these data by using a mixed model to account for the correlations between time points. I think I would treat WEEK as continuous, not categorical. I don't understand what you are trying to do with the LSMEAN statement, so I'll let someone else advice you on that.   data Waterford; infile datalines expandtabs truncover; input frt$ ph Week; datalines; WOL 15.20 1 WOH 20.30 1 MEL 17.78 1 MEH 20.30 1 CbioL 22.86 1 CbioH 22.80 1 PCL 25.40 1 PCH 20.32 1 FFL 20.30 1 FFH 20.30 1 VIA 17.78 1 VIM 17.78 1 UCL2 17.78 1 UCL3 17.78 1 UCL5 17.78 1 UCL6 17.78 1 Control 15.20 1 WOL 17.78 2 WOH 21.59 2 MEL 19.05 2 MEH 21.59 2 CbioL 24.13 2 CbioH 24.13 2 PCL 26.67 2 PCH 22.86 2 FFL 20.32 2 FFH 19.05 2 VIA 20.32 2 VIM 19.05 2 UCL2 24.13 2 UCL3 21.59 2 UCL5 19.05 2 UCL6 16.51 2 Control 22.86 2 WOL 20.32 3 WOH 31.75 3 MEL 30.48 3 MEH 22.86 3 CbioL 25.40 3 CbioH 24.89 3 PCL 36.83 3 PCH 27.94 3 FFL 26.67 3 FFH 21.59 3 VIA 33.02 3 VIM 29.21 3 UCL2 25.40 3 UCL3 27.94 3 UCL5 19.81 3 UCL6 26.67 3 Control 26.67 3 WOL 29.21 4 WOH 44.45 4 MEL 43.18 4 MEH 33.02 4 CbioL 33.02 4 CbioH 26.67 4 PCL 50.80 4 PCH 50.80 4 FFL 36.83 4 FFH 30.48 4 VIA 49.53 4 VIM 40.64 4 UCL2 25.40 4 UCL3 38.10 4 UCL5 0.00 4 UCL6 0.00 4 Control 43.18 4 WOL 38.10 5 WOH 55.88 5 MEL 52.07 5 MEH 33.78 5 CbioL 38.10 5 CbioH 30.48 5 PCL 59.69 5 PCH 55.88 5 FFL 43.18 5 FFH 38.10 5 VIA 58.42 5 VIM 55.88 5 UCL2 26.67 5 UCL3 46.99 5 UCL5 0.00 5 UCL6 0.00 5 Control 50.80 5 WOL 45.72 6 WOH 60.96 6 MEL 60.96 6 MEH 45.72 6 CbioL 48.26 6 CbioH 43.18 6 PCL 68.58 6 PCH 68.58 6 FFL 55.88 6 FFH 50.80 6 VIA 71.12 6 VIM 73.66 6 UCL2 30.48 6 UCL3 60.96 6 UCL5 0.00 6 UCL6 0.00 6 Control 66.04 6 WOL 55.88 7 WOH 73.66 7 MEL 68.58 7 MEH 53.34 7 CbioL 53.34 7 CbioH 48.26 7 PCL 76.20 7 PCH 73.66 7 FFL 66.04 7 FFH 58.42 7 VIA 78.74 7 VIM 78.74 7 UCL2 33.02 7 UCL3 76.20 7 UCL5 0.00 7 UCL6 0.00 7 Control 78.74 7 WOL 63.50 8 WOH 81.28 8 MEL 76.20 8 MEH 60.96 8 CbioL 60.96 8 CbioH 55.88 8 PCL 81.28 8 PCH 83.82 8 FFL 78.74 8 FFH 63.50 8 VIA 91.44 8 VIM 88.90 8 UCL2 35.56 8 UCL3 81.28 8 UCL5 0.00 8 UCL6 0.00 8 Control 86.36 8 WOL 73.66 9 WOH 88.90 9 MEL 81.28 9 MEH 68.58 9 CbioL 71.12 9 CbioH 63.50 9 PCL 86.36 9 PCH 86.36 9 FFL 81.28 9 FFH 71.12 9 VIA 101.60 9 VIM 91.44 9 UCL2 35.56 9 UCL3 86.36 9 UCL5 40.64 9 UCL6 35.56 9 Control 88.90 9 WOL 81.28 10 WOH 93.98 10 MEL 86.36 10 MEH 71.12 10 CbioL 76.20 10 CbioH 66.04 10 PCL 88.90 10 PCH 93.98 10 FFL 86.36 10 FFH 76.20 10 VIA 101.60 10 VIM 106.68 10 UCL2 35.56 10 UCL3 99.06 10 UCL5 60.96 10 UCL6 50.80 10 Control 96.52 10 WOL 91.44 11 WOH 106.68 11 MEL 91.44 11 MEH 76.20 11 CbioL 93.98 11 CbioH 83.82 11 PCL 91.44 11 PCH 101.60 11 FFL 96.52 11 FFH 88.90 11 VIA 106.68 11 VIM 114.30 11 UCL2 45.72 11 UCL3 116.84 11 UCL5 86.36 11 UCL6 63.50 11 Control 104.14 11 WOL 111.76 12 WOH 114.30 12 MEL 111.76 12 MEH 114.30 12 CbioL 114.30 12 CbioH 101.60 12 PCL 106.68 12 PCH 111.76 12 FFL 104.14 12 FFH 99.06 12 VIA 111.76 12 VIM 104.14 12 UCL2 63.50 12 UCL3 129.54 12 UCL5 93.98 12 UCL6 91.44 12 Control 114.30 12 WOL 119.38 13 WOH 121.92 13 MEL 116.84 13 MEH 116.84 13 CbioL 111.76 13 CbioH 106.68 13 PCL 106.68 13 PCH 116.84 13 FFL 111.76 13 FFH 106.68 13 VIA 121.92 13 VIM 119.38 13 UCL2 93.98 13 UCL3 119.38 13 UCL5 106.68 13 UCL6 106.68 13 Control 121.92 13 WOL 124.46 14 WOH 134.62 14 MEL 119.38 14 MEH 121.92 14 CbioL 116.84 14 CbioH 109.22 14 PCL 119.38 14 PCH 132.08 14 FFL 139.70 14 FFH 137.16 14 VIA 124.46 14 VIM 137.16 14 UCL2 96.52 14 UCL3 127.00 14 UCL5 121.92 14 UCL6 111.76 14 Control 132.08 14 WOL 139.70 15 WOH 149.86 15 MEL 121.92 15 MEH 127.00 15 CbioL 119.38 15 CbioH 111.76 15 PCL 142.24 15 PCH 139.70 15 FFL 142.24 15 FFH 142.24 15 VIA 132.08 15 VIM 142.24 15 UCL2 99.06 15 UCL3 134.62 15 UCL5 147.32 15 UCL6 127.00 15 Control 137.16 15 WOL 142.24 16 WOH 154.94 16 MEL 129.54 16 MEH 129.54 16 CbioL 129.54 16 CbioH 114.30 16 PCL 147.32 16 PCH 142.24 16 FFL 147.32 16 FFH 144.78 16 VIA 134.62 16 VIM 144.78 16 UCL2 104.14 16 UCL3 144.78 16 UCL5 152.40 16 UCL6 132.08 16 Control 139.70 16 WOL 157.48 17 WOH 172.72 17 MEL 147.32 17 MEH 132.08 17 CbioL 154.94 17 CbioH 114.30 17 PCL 157.48 17 PCH 160.02 17 FFL 149.86 17 FFH 147.32 17 VIA 172.72 17 VIM 175.26 17 UCL2 106.68 17 UCL3 149.86 17 UCL5 160.02 17 UCL6 152.40 17 Control 160.02 17 WOL 160.02 18 WOH 175.26 18 MEL 152.40 18 MEH 134.62 18 CbioL 157.48 18 CbioH 127.00 18 PCL 162.56 18 PCH 165.10 18 FFL 154.94 18 FFH 152.40 18 VIA 172.72 18 VIM 177.80 18 UCL2 114.30 18 UCL3 154.94 18 UCL5 165.10 18 UCL6 157.48 18 Control 162.56 18 WOL 165.10 19 WOH 180.34 19 MEL 154.94 19 MEH 137.16 19 CbioL 162.56 19 CbioH 127.00 19 PCL 167.64 19 PCH 167.64 19 FFL 160.02 19 FFH 160.02 19 VIA 175.26 19 VIM 180.34 19 UCL2 119.38 19 UCL3 157.48 19 UCL5 193.04 19 UCL6 162.56 19 Control 167.64 19 WOL 167.64 20 WOH 182.88 20 MEL 157.48 20 MEH 149.86 20 CbioL 167.64 20 CbioH 129.54 20 PCL 170.18 20 PCH 172.72 20 FFL 165.10 20 FFH 185.42 20 VIA 177.80 20 VIM 185.42 20 UCL2 121.92 20 UCL3 160.02 20 UCL5 220.98 20 UCL6 165.10 20 Control 175.26 20 WOL 180.34 21 WOH 203.20 21 MEL 160.02 21 MEH 152.40 21 CbioL 172.72 21 CbioH 129.54 21 PCL 170.18 21 PCH 177.80 21 FFL 167.64 21 FFH 185.42 21 VIA 200.66 21 VIM 218.44 21 UCL2 152.40 21 UCL3 193.04 21 UCL5 264.16 21 UCL6 180.34 21 Control 203.20 21 WOL 180.34 22 WOH 208.28 22 MEL 162.56 22 MEH 154.94 22 CbioL 175.26 22 CbioH 132.08 22 PCL 175.26 22 PCH 182.88 22 FFL 172.72 22 FFH 187.96 22 VIA 205.74 22 VIM 223.52 22 UCL2 154.94 22 UCL3 198.12 22 UCL5 266.70 22 UCL6 182.88 22 Control 208.28 22 WOL 182.88 23 WOH 210.82 23 MEL 165.10 23 MEH 157.48 23 CbioL 177.80 23 CbioH 134.62 23 PCL 177.80 23 PCH 185.42 23 FFL 175.26 23 FFH 190.50 23 VIA 208.28 23 VIM 223.52 23 UCL2 160.02 23 UCL3 198.12 23 UCL5 274.32 23 UCL6 185.42 23 Control 213.36 23 WOL 185.42 24 WOH 215.90 24 MEL 170.18 24 MEH 160.02 24 CbioL 182.88 24 CbioH 134.62 24 PCL 177.80 24 PCH 187.96 24 FFL 177.80 24 FFH 190.50 24 VIA 213.36 24 VIM 226.06 24 UCL2 162.56 24 UCL3 200.66 24 UCL5 287.02 24 UCL6 187.96 24 Control 215.90 24 WOL 190.50 25 WOH 220.98 25 MEL 175.26 25 MEH 165.10 25 CbioL 185.42 25 CbioH 134.62 25 PCL 182.88 25 PCH 193.04 25 FFL 180.34 25 FFH 193.04 25 VIA 215.90 25 VIM 274.32 25 UCL2 167.64 25 UCL3 208.28 25 UCL5 287.02 25 UCL6 190.50 25 Control 215.90 25 WOL 190.50 26 WOH 220.98 26 MEL 175.26 26 MEH 165.10 26 CbioL 185.42 26 CbioH 137.16 26 PCL 182.88 26 PCH 193.04 26 FFL 180.34 26 FFH 193.04 26 VIA 215.90 26 VIM 274.32 26 UCL2 167.64 26 UCL3 208.28 26 UCL5 287.02 26 UCL6 190.50 26 Control 215.90 26 WOL 190.50 27 WOH 220.98 27 MEL 175.26 27 MEH 165.10 27 CbioL 187.96 27 CbioH 137.16 27 PCL 182.88 27 PCH 195.58 27 FFL 180.34 27 FFH 195.58 27 VIA 215.90 27 VIM 276.86 27 UCL2 170.18 27 UCL3 208.28 27 UCL5 289.56 27 UCL6 190.50 27 Control 215.90 27 ; proc freq data=Waterford; tables frt; run; title "Response by Week"; proc sgplot data=Waterford; series x=Week y=ph / group=frt; run; proc mixed data=Waterford method=ml; class frt; model ph = frt week / s; repeated / type=ar(1) sub=frt; store out=MixedModel; /* create item store */ run; proc plm restore=MixedModel; /* use item store to create fit plots */ effectplot slicefit(x=week sliceby=frt); /* overlay */ run;     ... View more

Those diagnostic panels look like PROC REG. Please post your complete SAS program, including calls to PROC REG, PROC TRANSREG, and DATA steps.  Remember to use the "Running Man icon" (Insert SAS Code) so that the SAS code is nicely formatted. ... View more