per the doc: Note: The COMPARE procedure omits data set labels if the line size is too small for them.   I guess you have an enormous lot of training blanks in your label ! Please trim the label!   [EDIT] ... or augment the line size with the linesize option.   Cheers, Koen ... View more

Are you sure you have labels for the datasets compared?   For me it works:   What if you do a PROC CONTENTS? In the very first output table (Results) ... do you see your label? Ciao, Koen   ... View more

PharmaSUG 2025 - Paper AP-367 SAS Macro to Calculate Standardized Mean Difference Tong Zhao, Tian Gu, LLX Solutions, LLC https://pharmasug.org/proceedings/2025/AP/PharmaSUG-2025-AP-367.pdf   ... by using the PSMATCH procedure: https://go.documentation.sas.com/doc/en/statug/latest/statug_psmatch_details22.htm   You can also do this Google search: calculate "standardized mean difference" site:communities.sas.com   Ciao, Koen ... View more

You can try DISCRETEX and DISCRETEY options on the heatmap statement. You can try  min= ,  max= , or  values=(list)  or  discreteorder= within the  xaxis  or  yaxis  statements to control the axes ranges better. You can also customize Ticks and Labels ( tickvalueattrs=  and  display= ). Ciao, Koen ... View more

Let this paper be an inspiration:   Paper SAS4341-2016 Graph A Million with the SGPLOT Procedure Prashant Hebbar and Sanjay Matange, SAS Institute Inc. https://support.sas.com/resources/papers/proceedings16/SAS4341-2016.pdf   It's using the HEATMAP statement on numerous occasions. In the third maintenance release of SAS 9.4 (SAS 9.4 M3), the SGPLOT procedure supports a HEATMAP statement.    Koen ... View more

Hello ,   I guess with longitudinal covariate you mean "time-varying covariate" or "time-dependent" covariate. PROC LIFREG can fit a Weibull survival model with time-varying covariate(s), but PROC LIFEREG does not allow for random effects.   So, you are right ... Weibull survival models with both fixed and random effects (often termed parametric frailty models) are best implemented in SAS using PROC NLMIXED. Here's the example you refer to, but in a more recent version of the documentation: The NLMIXED Procedure Example 89.5 Failure Time and Frailty Model https://go.documentation.sas.com/doc/en/statug/15.3/statug_nlmixed_examples05.htm   Maybe you can find some inspiration here: Paper 1492-2014 Introduction to Frailty Models John Amrhein, McDougall Scientific Ltd. https://support.sas.com/resources/papers/proceedings14/1492-2014.pdf   Apparently you can also fit a frailty model with PROC PHREG (PROC PHREG has a random statement), but only two frailty distributions are available in PROC PHREG: gamma and lognormal. Gamma and Weibull are like cousins but tailored for different failure patterns. That's unfortunate (Weibull not being available in PROC PHREG). The PHREG Procedure The Frailty Model https://go.documentation.sas.com/doc/en/statug/latest/statug_phreg_details16.htm Ciao and good luck with this, Koen ... View more

Hello,   Why don't you use PROC SGMAP (instead of proc gmap)? The prefix SG is for Statistical Graphics.   See here: New SGmap features in SAS 9.4m7 By Robert Allison on Graphically Speaking  September 25, 2020 https://blogs.sas.com/content/graphicallyspeaking/2020/09/25/new-sgmap-features-in-sas-9-4m7/   Ciao, Koen ... View more

Try using these options on the panelby statement (behind the forward slash): LAYOUT=panel columns=4 novarname Koen ... View more

No worries. The question has since been answered. Thank you for staying active on the SAS Communities, @WarrenKuhfeld .   Ciao, Koen ... View more

If the observations are equally spaced in time, and all the series are observed as beginning-of-period values, only the input and output data sets need to be specified. For example, the following statements interpolate any missing values in the numeric variables in the data set A using a cubic spline function, assuming that the observations are at equally spaced points in time: proc expand data=a out=b; run; For more information, see the section "Missing Values".   The default is METHOD=SPLINE.  Constraint specifications for METHOD=SPLINE can be specified. The methods supported are SPLINE, JOIN, STEP, AGGREGATE, and NONE. Ciao, Koen ... View more

If you are not dealing with ill-behaved data and/or with a small sample, there are many other ways (besides PROC ENTROPY) to deal with heteroskedasticity in regression residuals.   See here: 131-2007: Skewness, Multicollinearity, Heteroskedasticity – You Name It, Cost Data Have It! Solutions to Violations of Assumptions of the Ordinary Least Squares Regression Model Using SAS® Skewness, Multicollinearity, Heteroskedasticity - You Name It, Cost Data Have It! Solutions to Violations of Assumptions of Ordinary Least Squares Regression Models Using SAS® Leonor Ayyangar -- Health Economics Resource Center (HERC) VA Palo Alto Health Care System Menlo Park, CA (a SAS Global Forum 2007 paper, but still valid info of course) 60848 - A Simple Regression Model with Correction of Heteroscedasticity  (application of SAS/ETS PROC MODEL) Ciao, Koen ... View more

The ENTROPY procedure implements a parametric method of linear estimation based on generalized maximum entropy (GME). The ENTROPY procedure is suitable when there are outliers in the data and robustness is required, when the model is ill-posed or under-determined for the observed data, or for regressions that involve small data sets.   Is your data ill-behaved? Do you have a small sample?   PROC ENTROPY estimates tend to be biased (slightly biased), as they are a type of shrinkage estimate, but typically portray smaller variances than ordinary least squares (OLS) counterparts, making them more desirable from a mean squared error (MSE) viewpoint. ... View more

See also ... Create mosaic plots in SAS by using PROC FREQ By Rick Wicklin on The DO Loop  November 4, 2013 https://blogs.sas.com/content/iml/2013/11/04/create-mosaic-plots-in-sas-by-using-proc-freq.html   And this is a 2001 paper by a visualization guru (prof. Michael Friendly). The macros are probably no longer available and the graphics is not looking modern (better aesthetics can be achieved nowadays), but it is still very useful to read. Title: A Reader’s Guide to Visualizing Categorical Data Author: Michael Friendly  SUGI 26 -- 2001 https://support.sas.com/resources/papers/proceedings/proceedings/sugi26/p173-26.pdf Ciao, Koen ... View more

No time at this moment to work it out for you ... but maybe the author of that blog can help you out quicker. @WarrenKuhfeld  , can you? Advanced ODS Graphics: Range Attribute Maps By Warren F. Kuhfeld on Graphically Speaking  June 26, 2017 https://blogs.sas.com/content/graphicallyspeaking/2017/06/26/advanced-ods-graphics-range-attribute-maps/   Ciao, Koen ... View more

I would ask the author of this Communities-Library-article. CAS Table State Management for everybody https://communities.sas.com/t5/SAS-Communities-Library/CAS-Table-State-Management-for-everybody/ta-p/972737 That's @BrunoMueller .   Ciao, Koen ... View more