Recently in the SAS Community Library: Your often contains the information you need, but not sequenced in the order required for processing. @SASJedi shows you how to properly sequence data so you can compare the data in one table to the data in another, conduct merges or joins and more.
Hi Users and Members,
While the headwinds of Open source or open source driven technologies/cloud technologies pose a serious threat to the SAS technologies and expansion, I , being a senior practitioner in this domain, is severely concerned. I have come across many posts related to SAS ETL conversion to Databricks/Python, SAS to Python , SAS to Cloud technologies and it is surprising to see Cloud vendor(s) , even with strategic alliances with SAS, are pitting their technologies against SAS. I have started going through the presentations/discussions related to the recently held SAS Innovate, but I would like to ask the larger audience( not only for SAS programming) that if concrete steps/roadmap is devised to counter the headwinds in the domains of Data integration/Data management and other areas?
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Hi! I have managed to come up with a model pipeline in python for clustering of text using DBSCAN and I wish to import this model into SAS model manager through SAS CTL for further analysis of the clusters using SAS Topic Modelling methods (can't use OS for this step) Most of the examples I see online use SASCTL to import supervised learning models. However, my output in this case is a bunch of cluster labels, and the number of clusters widely depends on every run of the model. So far, i have converted my DBSCAN model into a pickle file, and i have created a score file for my DBSCAN model (which is basically using silhouette score and Davie Bouldin score to evaluate the efficiency of the clustering). I have also created Json files for my input variables and output variables. Right now, I'm getting stuck at the part where I have to write my model properties into a JSON file and the metadata information, as my target values aren't binary. Does anyone have any examples/implementation of people importing unsupervised clustering models into SAS Model Manager with SASCTL? It would be really helpful if someone could guide me on how to move on from this step. Thank you in advance!!
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Request: Please can SAS include the option to calculate test statistics for the unadjusted and stratified MN method. Background: Miettinen-Nurminen (MN) methods are increasingly being requested by regulatory agencies. This is particularly relevant for non-inferiority trials where it's appropriate for the variance to be estimated under the null hypothesis. Within the PROC FREQ procedure, it's currently possible to calculate the treatment differences (and CIs) for both the unadjusted MN and the stratified MN methods as well as other MN tests such as the Summary Score method (also referred to as Agresti Score). However, whilst for the Summary Score method SAS allows the option to calculate the test statistics this is not yet available for the unadjusted MN method or the stratified MN method. These methods are important as they estimate variance under the null hypothesis, specifically the test statistics are of particular useful for: testing superiority and presenting a p-value studies with interim analyses, particularly when multiplicity is controlled via a group sequential design i.e. with alpha boundaries less than the typical 1-sided 2.5%/2-sided 5% alpha Example SAS code (for reference): Note: data not supplied due to proprietary nature, however code shared to illustrate current options/limitations. The unadjusted MN treatment difference can be calculated using: proc freq data=DATA;
tables TRT*AVAL / riskdiff(CL=(MN) noninferiority margin=0.1);
run; Note: whilst the (unadjusted) Wald test statistics are calculated, it would be helpful if the unadjusted MN test statistics could also be provided. The stratified MN Score treatment difference can be calculated using: proc freq data=DATA;
tables STRATA*TRT*AVAL / riskdiff(CL=(MN) noninferiority margin=0.1) COMMONRISKDIFF(CL=(MN score) test=(score));
run; Note: whilst the (adjusted) Summary Score (also referred to as Agresti Score) test statistics are calculated, it would be helpful if the stratified MN test statistics could also be provided. Specifically, if MN could also be an option within COMMONRISKDIFF(TEST=( ))
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