<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />Team,
Brady Brady and Scott Wise presented a well received presentation on Creating Robust Linear Models Using Generalized Linear Regression (GLR) at the 2015 JSM conference in Seattle, WA on 08/10/15. The audience was impressed with the ease of use and flexibility of the new Generalized Linear Regression platform in JMP Pro 12. A longer version of the presentation will also be featured at the upcoming 2015 Discovery conference in San Diego, CA.
Attaching per request:
- JSM Genreg.jrn > Journal used during the presentation (including scripted slides)
- South African Heart Disease 2.jmp > Genreg Elastic Net demo including additional model comparison scripts
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Scott Wise, JMP Principal Systems Engineer, SAS
Brady Brady, JMP Technical Enablement Engineer, SAS
Creating Robust Linear Models Using Generalized Linear Regression (GLR)
Penalized regression techniques are a viable alternative to traditional linear modeling approaches, particularly in cases where the typical assumptions of orthogonality, homoscedasticity or response normality are violated. They can also be used to automatically select which predictors are most useful in predicting a given response. While these techniques are not new, their use in applied settings has historically been limited, due both to their computational expense and their relative obscurity outside of academia. Today, the proliferation of affordable, powerful computers makes it feasible to implement these techniques in desktop analytic software. This session will explain penalized regression techniques and explore the use of the Generalized Regression personality of the JMP Fit Model platform. Attendees will find that these models are easy to set up, run and analyze, yet are robust in the presence of outliers, correlated inputs, and non-normal and/or zero-inflated responses.
The journal is missing some scripts (e.g., bias vs. regression)
This dataset is included in Trevor Hastie’s The Elements of Statistical Learning, a popular machine-learning text.
Requested information on the source and backstory of the data:
A description of the dataset is at the link below. As you’ll find, CHD is “coronary heart disease” and the dataset is a subset of a dataset from a 1983 journal article.
Cheers,
Brady
https://web.stanford.edu/~hastie/ElemStatLearn/datasets/SAheart.info.txt
