A Human Generated Introduction to Generative AI, Part 1: Synthetic Data Generation
Recent Library Articles
Recently in the SAS Community Library: In the first of two posts on applications of generative AI, SAS' @JThompson reveals the role of generating synthetic data.
This is from Wikipedia. The joint probability of the match pair when Yi1 =1 and Yi2 = 0. Note the denominator, where the P(Yi1 =1 & Yi2 =0), and P(Yi1 =0 & Yi2 =1) are the product of their own probability, respectively. Therefore, my question is, are Yi1 and Yi2 independent or dependent? Looking forward to your opinion.
The joint probability of a pair:
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Hello everyone,
I would like to know if there is a way to hide a specific column from a dataset for certain users while keeping the same dataset accessible in SAS Enterprise Guide.
For example, can we prevent a user from viewing a sensitive column (like a salary or ID number) even though they have access to the dataset?
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Hello community, I encountered an issue and needed the help of experts to see what happened. I have a SAS Studio program scheduled to run a job daily. It worked well. On Wednesday, I updated the program a little bit and tested it by running it manually. It worked at that time. Then I rescheduled a job for it. The job failed. The error message is "ERROR: The provided list of column names did not match names in table XXX" while it is to append a dataset to CAS table. SAS code is as below: DATA CASLIBNAME.XXX (APPEND=YES); SET XXX_DAILY; RUN; I just debugged and ran it manually, and it works. May I know how to resolve it and avoid this kind of thing when trying to append in the future? Thank you so much! Lin
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Hi.
I never got this warning before.
I know this is not a license renewal warning. What is this and what do I need to do?
Thanks,
WARNING: The Base SAS Software product with which RESULTS is associated will be expiring soon, and is
WARNING: currently in warning mode to indicate this upcoming expiration. Please run PROC SETINIT to
WARNING: obtain more information on your warning period.
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I'm analyzing data from a designed agricultural experiment using linear mixed models. At one point, I collected data for what I thought would serve a covariate (weed rating) for explaining crop yield, but further analysis revealed overlap between this variable and one of my experimental treatments. A major hint that these variables might be collinear was that, when both variables were included in the model as predictors for crop yield, the treatment variable was no longer significant (it previously had been consistently significant). Note that both variables are categorical, and the treatment variable has two levels while the covariate (weed rating) is an ordered variable with 5 levels (1 to 5).
I would like to determine a standard way to demonstrate redundancy between these variables, so that I can justify not including them simultaneously in models. A chi-squared test (code below) seems to be appropriate. Is this approach adequate for establishing predictor redundancy? I'm not using the term "collinearity" here, because I'm under the impression that collinearity applies more to regression and not ANOVA. I don't think I need to determine VIF here.
title "Chi-squared test for predictor redundancy";
proc freq data=df;
tables treatment * other_variable / chisq;
run;
Ultimately, my goal is to establish a causal link between the response (crop yield), the treatment, and weed rating. I've accomplished part of this by treating the covariate (weed rating) variable as the response variable and modeling that with my experimental treatments. That confirmed a high degree of correlation (not sure if "correlation" is technically correct to use here, but you know what I mean) between the treatment in question and the weed rating. Now, I believe that I just need to establish the link between weed rating and yield, and justify not including the redundant treatment in the model. Thank you for reading.
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