Without knowing more about your problem and the input data to your PROC GLIMMIX code, I can only offer a few general suggestions:   1. If you cannot obtain proper convergence for the specified model, you may need to consider fitting a simpler model, that is, a model with fewer parameters. Looking at the model below, a considerable number of parameters may be associated with the random effects that need to be estimated.  2. While convergence cannot be guaranteed, you may want to try altering the default optimization options available in the NLOPTIONS statement. See the NLOPTIONS statement description using the following links: SAS Help Center: NLOPTIONS Statement SAS Help Center: NLOPTIONS Statement: Shared Concepts and Topics For instance, changing the optimization technique (NLOPTIONS TECH= option) or increasing the maximum number of iterations (NLOPTIONS MAXITER= option) may be helpful. Options for alternative stopping criteria are also available. The options for displaying the optimization history may provide useful information.   For more information, please see the following:   SAS Help Center: Choosing an Optimization Algorithm ... View more

You cannot compute the Mahalanobis distance for your sample data because the correlation/covariance matrix of s1-s8 is fully degenerate. In this case, you cannot invert the correlation or covariance matrix to compute the distances. Computing and utilizing a generalized inverse is not even possible in this case.   If you run PROC CORR, you will see those correlations cannot be computed due to all the values of s1-s8 being 0.   ods output PearsonCorr=sample_corr; proc corr data=sample pearson; var s1-s8; run;     ... View more

The relationship between y and x1 x2 in your data can be captured by using a regression between y and just the quadratic term.  Assuming X2 is X1*X1, try running.   PROC REG DATA=groupD17; MODEL Y = X2; RUN;   And compare the Rsquare with the original model.     Here is an example that will help you understand why a model with only a quadratic term and no linear term can fit well:   data examples(drop=i); do i=1 to 1000; x1=10*ranuni(213)-5; x2=x1*x1; /*y_only_quad is a difference of squares with no linear term y_only_quad=x1^2-4 + N(0,1) error*/ y_only_quad=(x1+2)*(x1-2) + rannor(33); /*y_lin_quad is a quadratic polynomial with a linear term y_lin_quad=x1^2+4x+4 + N(0,1) error*/ y_lin_quad=(x1+2)*(x1+2) + rannor(89); output; end; run; ods graphics; proc sgplot data=examples; scatter x=x1 y=y_only_quad; scatter x=x1 y=y_lin_quad; refline 0 / axis=y; refline 0 / axis=x; run; /*linear term will not be significant assuming alpha level 0.05 rsquare is 0.98*/ proc reg data=examples; model y_only_quad= x1 x2; run; quit; /*remove linear term rsquare is still 0.98*/ proc reg data=examples; model y_only_quad= x2; run; quit; /*run model where the linear term is required the linear term is significant at alpha level 0.05*/ proc reg data=examples; model y_lin_quad= x1 x2; run; quit; ... View more

I am only aware of one way to do this using options nonotes before the proc sql step and then turning the notes back on after the quit statement.   options nonotes; proc sql; create table eval as select name from ( select name, rownum from names join filter on name like trim(pattern) where state = '+' except select name, rownum from names join filter on name like trim(pattern) where state = '-' ) order rownum ; quit; options notes; ... View more

Check out proc timeid(SAS/ETS) it will find and calculate the length of gaps in your time series: https://communities.sas.com/t5/SAS-Communities-Library/Tips-Identifying-and-Locating-Missing-Values-and-Gaps-in-Time/ta-p/223429 ... View more

PROC CLUSTER allows you to pass both coordinate data and distance data.   If the data are coordinates, PROC CLUSTER computes Euclidean distances.If you want non-Euclidean distances, You may use the DISTANCE procedure to compute an appropriate distance data set that can then be used as input to PROC CLUSTER. Assuming that you would like to use Euclidean distance then you are not required to do the extra step of converting your data to distances.   If you already have a distance matrix as input then you can use the TYPE= option on a data step to indicate to PROC CLUSTER or KMEANS that the data set is a distance matrix.   https://go.documentation.sas.com/?cdcId=pgmsascdc&cdcVersion=9.4_3.4&docsetId=ledsoptsref&docsetTarget=n1ougt1wsasgafn1xgdfsd8vofoe.htm&locale=en   I hope this information helps. ... View more