Dear, guys   Today a new publication on NEJM says their data is sharable and would be available  on "a specialized SAS data platform". Therefore, I would like to download the data and make statistical inferences by myself. And, I don't where is the SAS data platform? Does it belong to SAS?   Thanks   Source: https://www.nejm.org/doi/suppl/10.1056/NEJMoa2501006/suppl_file/nejmoa2501006_data-sharing.pdf  ... View more

I have data on the severity of weed pressure for an agricultural study with a factorial, RCBD.  The weed pressure variable is the area in square footage within a plot where weeds were measured to be above a certain height.  It is an atypical metric for weediness, and I have not been able to find anything in the literature like it, so I'm unsure of how to analyze this data.   The raw, untransformed variable "Area_Weed", in units of square feet, exhibits right-skewness.  A few of the observations are zero.  The histogram is shown below:   One approach that I've tried is to convert the response variable "Area_Weed", in square feet, to a proportion of weed area divided by total plot area, the apply an arcsine transformation.  The residuals plot exhibits non-constant variance.  The code and residuals are shown below: proc glimmix data=df plots=studentpanel method=rspl; class Trt_Amend_App Trt_CC Block; model Area_Weed_PROP_ANG = Trt_Amend_App | Trt_CC / ddfm=kr2; random Block; run;   Considering an alternative distribution for the untransformed response variable "Area_Weed", which is positive and right-skewed, the gamma distribution seems promising, aside from the fact that it can't accommodate zeros in the data.  If I apply a transformation of adding a small constant (+1) then I can get the model to run using PROC GENMOD, with the caveat that I can't include the variable "Block" as a random effect. proc genmod data=df plots=all; class Trt_Amend_App Trt_CC Block; model Area_Weed_PLUS = Trt_Amend_App | Trt_CC / dist=gamma link=log; ods output ParameterEstimates=pe; output out=outmean pred=mu; run; My questions amount to the following: Based on what I've presented here, is the gamma distribution appropriate for my response variable, which is positive, right-skewed, and includes a few zeros? If I use PROC GENMOD and a gamma distribution to model, how might I go about evaluating the residual plots?  What might be a good option for the parameter plots aside from "all"? If I attempt to incorporate Block as a random effect using PROC GLIMMIX, the model does not converge.  How can I address this issue? Thank you for reading.  Please let me know if I can provide any other information. ... View more

Dear all   I am pleased to inform you that I have developed two distinct SAS macros designed to explain the Low Default Portfolio modeling approach suggested by Pluto &Tasche  https://arxiv.org/pdf/cond-mat/0411699v3     These macros account for both correlated and independent default events. I believe these tools will be highly valuable for the SAS community, particularly in the area of Credit Risk Modeling for Low Default or Zero Default portfolios.   From my point of view, these ones are great examples of built-in functions to be deployed as part of an particular SAS Proc Statement, such as: PROC LDPT   Best regards, Raphael Chaves ... View more

Good Day All   I am looking for the course data for Neural Network Modeling (Course: Neural Network Modeling) as I would like to do the practices in my own SAS software in preparation for the certification exam, SAS Advanced Predictive Modeling Exam.   I have not found the course data under the course materials link.   Do you perhaps know where else I can find this course data for Neural Network Modeling?   Regards   Calvin ... View more