SAS Analytics R&D News

March went out like a lion here in Cary, North Carolina, with howling winds, rain, and colder than normal temperatures. Signs of spring have sprung, however. The current stage of spring is my least favorite: the Pollening. If you have not experienced that special feature of spring in the South, you really should pay us a visit.

Plans for the Joint Statistical Meetings (JSM) in Washington, DC, in August are in full swing in Cary. That is not the only conference in the works. PharmaSUG meets live in Austin, Texas, next month.

Fortunately, spring has not brought us just pollen. We also have two new releases of SAS^® Viya^® software! You can read about the new features in the What’s New section of the documentation. A personal highlight for me is the addition of the REPEATED statement to the GENSELECT procedure in the February 2022 release (2021.2.4).

Recently, Rick Wicklin hit a major milestone in his continuing contributions to the SAS Communities. If you are active in the Statistical Procedures community, you have certainly seen Rick’s involvement in this area. In February, Rick had his 1000th response marked as a solution to a community post! That is quite an achievement. Congratulations, Rick!

Finally, if you are looking to be part of a dynamic team, we are looking for a Principal Systems Architect to lead architectural initiatives within Analytics R&D. You will be charged with providing technical direction and ensuring excellence as we continue to release cloud-first, cloud-native, as-a-service offerings. Apply today!

Sincerely,

Phil Gibbs

Manager, Advanced Analytics Technical Support

Technical Highlights

How to Train Generalized Additive Models (GAMs) in Model Studio

Listen to Brian Gaines, a senior machine learning developer in the Analytics Division of R&D at SAS, as he discusses one of his favorite machine learning models: the generalized additive model, or GAM for short. In addition to providing an overview of GAMs and their attractive features, he shows how easy it is to use Model Studio to train a GAM and compare it to other models. Check out his demo on the SAS Users YouTube channel.

Brian also provides an article on SAS Communities that provides complete step-by-step instructions to reproduce the data analysis in the SAS Data Science blog post.

How Streaming IoT Data and Advanced Analytics Help Maintain COVID-19 Vaccine Integrity

The North Carolina Collaboratory relies on SAS^® Analytics for IoT on Azure to harness the complex system of cold chain logistics, ensuring safe transport, storage, and availability of COVID-19 vaccines statewide. Read more.

Thoughts from SAS Analytics R&D

SAS Data Science Blog

Yongqiao Xiao, Maggie Cech, and Patrick Koch describe how an ONNX model can be integrated into ASTORE and used within SAS^® environments, as well as providing examples. Pankaj Telang shows you new image-specific processing capabilities in SAS^® Visual Data Mining and Machine Learning software. Courtney Ambrozic highlights how to use SAS Visual Data Mining and Machine Learning to assess lesion response to chemotherapy for patients with colorectal cancer that spread to the liver, and Xuejun Liao weighs the pros and cons of collaborative filtering and supervised learning and explores their use in a unified framework.

The DO Loop

Distinguished Research Statistician Developer Rick Wicklin answers questions about McNemar’s test in SAS and presents new methods for solving differential equations in SAS. He also discusses how to compute first-order and second-order finite-difference derivatives for smooth functions in SAS.

Tech Support Points Out

Predictive Margins and Average Marginal Effects

Version 2.0 of the Margins macro is now available. This release removes the requirement for SAS/IML^® software and includes some new options and fixes.

Predictive margins are estimates of the response mean and, unlike LS-means, do not require fixing all predictors in the model at specified values. For example, for a model including gender as a predictor, the predictive margin for males treats all observations as males and is the average over the predicted values computed using the observed values in the other predictors. In this way, all observations, not just the males, are used. The marginal effect of a continuous predictor at an observation is the instantaneous rate of change of the response mean at that point. The average of the marginal effects over the observations (AME) is often used as a measure of the effect of the continuous predictor on the response mean. A similar measure is the marginal effect estimated at the mean of the other predictors (MEM). For small samples, the AME is considered the better measure. A measure of the effect of a categorical predictor on the response mean can similarly be obtained as the difference in predictive margins at two of its levels. This is often considered the marginal effect of a binary categorical predictor.

The Margins macro fits the specified generalized linear or GEE model and estimates and tests predictive margins and/or marginal effects (AMEs and MEMs) for the requested variables in the model. Differences and contrasts of predictive margins and/or marginal effects with confidence limits are also available. Margins and effects can be estimated at specified values of other model variables or at computed values such as means or medians. For count response models, margins and effects on the rate, rather than the mean count, are now available.

Talks and Tutorials

Wednesday May 25 | 11:00 AM ET

Ask the Expert: How Do I Advance My Machine Learning Applications?

Join this webinar to learn the key benefits of using SAS^® Visual Machine Learning and how SAS supports your migration to this platform. You will learn:

How easy it is to build machine learning pipelines and find good models through Model Studio.
About SAS tools that are well integrated, covering the full analytics life cycle.
How intelligent automation combined with human oversight provides robust decision making.
Which tools can support your transition from SAS^® Enterprise Miner^™ to SAS Visual Machine Learning.

Tuesday, June 7 | 10:00 AM ET

Ask the Expert: What Are the Analytical Capabilities of SAS^® Visual Analytics?

Join this webinar to learn the analytical capabilities of SAS Visual Analytics to dive deeper into your data by bridging the gap between data exploration and advanced analytics. You will learn how to:

Use the Automated Explanation object.
Trust the results of SAS’ modeling objects in SAS Visual Analytics.
Take the next step.

Tuesday, June 14 | 10:00 AM ET

Ask the Expert: How Do I Use Open Source with SAS Viya?

This webinar will help you learn the many ways SAS Viya integrates with open source. You will learn how to:

Access the power of SAS using your existing skills, like SAS, open source, or other programming skills.
Use Python or R in the analytical flow of pipelines.
Use Python or R through the SWAT package.
Use the Python Editor within SAS Studio.

Quick Links

Statistics & Operations Research Home

Bayesian Resources

SAS Analytics: 14.3, 14.2, 14.1, and 13.2

FASTats: Frequently Asked-For Statistics

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Analytical SAS Software Video Portal

SAS/STAT Example Programs

SAS/ETS Example Programs

SAS^® Statistics and Operations Research News

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