Customizing a Workflow in SAS Solution for IFRS 17
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Recently in the SAS Community Library: SAS' @sunilbhardwaj details steps to add a customized Excel based workflow template to the SAS solution for IFRS 17. These steps enable you to try out and test several customized workflows based on your requirements.
Rather than asking on this forum to "please check my SAS code and tell me what's wrong?", I decided to ask Anthropic's Claude for help.
Well, way too often what Claude suggested simply did not work. I asked again, and again. Claude kept trying to change the SAS code to get it to successfully execute. But, alas, it turned out to be mostly a waste of time. So, just curious what your take is. Do ANY of the LLMs know SAS fairly well, enough for us to post some code and ask it to show where problems are occurring? Your thoughts greatly appreciated.
Nicholas Kormanik
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I want to Apply Tests for Special Causes in the p-chart, but the sample size for each group is different. So, I don’t have a fixed UCL and LCL, and I will get the following warning with below code: WARNING: Asymmetric control limits encountered for yes_answer1 for at least one subgroup. proc shewhart data=tmp2;
pchart yes_answer1*quarter / subgroupn = total_count1
tests =1 to 8
TESTNMETHOD=STANDARDIZE
table
tablelegend;
run; Note: The SHEWHART procedure provides an option for working with unequal subgroup sample sizes. For example, I can use the LIMITN= option to specify a fixed (nominal) sample size for computing the control limits. Below is the sample size for each group that I have; my question is, how can I choose the number for LIMITN? quarter Subgroup 2021Q3 30 2021Q4 66 2022Q1 54 2022Q2 66 2022Q3 69 2022Q4 74 2023Q1 83 2023Q2 96
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I am building models whose dependent variable (1) is zero-inflated (or, in the econometric literature, "limited"); (2) is continuous in nature and (3) follows a lognormal distribution in the non-zero part.
I have reviewed the literature and found that a two part model maximizing a joint likelihood might be suitable. The problem is that this process does not seem to be supported by built-in procedure of SAS. So I decide to formulize the joint likelihood and let SAS maximize it and report the results.
I am not going into the details of this joint likelihood, but in case someone needs to know it, I will mention it briefly. The joint likelihood concerns the product of the probability that a given sample point exceeds zero and the expected value of that sample point given that it has exceeded zero.
Is there anyway I can tailor my likelihood function to maximize with built-in SAS procedures? References on this issue are also welcome.
Thank you!
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There are two questions revolving PROC MI that I would like to raise:
(1) How can I know to what extent has the imputation completed (i.e., the number of imputation completed) during the imputation process? Multiple imputation is a computer-intensive process. Moreover, its theory tells us to perform as many imputations as possible and putting in as many variables as possible yields the best results, which makes the user feeds in an enoromous dataset when executing imputation, a practice that may cause the time needed to further increase. I often spend much time checking my computer to see if my imputation is done, only to find it is yet to be done. Sometimes the need for an imputed dataset is imminent, so this makes me anxious.
(2) How can I know the "culprit variable" when the errror warning "An imputed variable value is not in the specified range after XXX tries" appears in my log? At present, I have to go through the vast amount of continuous variables in the imputation model to see what variable(s) cause this error. This greatly wastes my time.
Thanks!
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