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Rick WicklinMay 8, 2019 3

Discrimination, accuracy, and stability in binary classifiers

At SAS Global Forum 2019, Daymond Ling presented an interesting discussion of binary classifiers in the financial industry. The discussion is motivated by a practical question: If you deploy a predictive model, how can you assess whether the model is no longer working well and needs to be replaced? Daymond

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Analytics | Programming Tips

Rick WicklinMay 6, 2019 1

How to simulate data from a generalized linear model

Here's a simulation tip: When you simulate a fixed-effect generalized linear regression model, don't add a random normal error to the linear predictor. Only the response variable should be random. This tip applies to models that apply a link function to a linear predictor, including logistic regression, Poisson regression, and

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Analytics | Learn SAS

Rick WicklinMay 1, 2019 1

Encodings of CLASS variables in SAS regression procedures: A cheat sheet

SAS regression procedures support several parameterizations of classification variables. When a categorical variable is used as an explanatory variable in a regression model, the procedure generates dummy variables that are used to construct a design matrix for the model. The process of forming columns in a design matrix is called

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Analytics | Programming Tips

Rick WicklinApril 24, 2019 0

A CUSUM test for autregressive models

The CUSUM test has many incarnations. Different areas of statistics use different assumption and test for different hypotheses. This article presents a brief overview of CUSUM tests and gives an example of using the CUSUM test in PROC AUTOREG for autoregressive models in SAS. A CUSUM test uses the cumulative

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Analytics | Programming Tips

Rick WicklinApril 10, 2019 3

4 ways to compute an SSCP matrix

In numerical linear algebra, there are often multiple ways to solve a problem, and each way is useful in various contexts. In fact, one of the challenges in matrix computations is choosing from among different algorithms, which often vary in their use of memory, data access, and speed. This article

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Analytics | Data Visualization

Rick WicklinMarch 27, 2019 2

How to simulate multivariate outliers

In simulation studies, sometimes you need to simulate outliers. For example, in a simulation study of regression techniques, you might want to generate outliers in the explanatory variables to see how the technique handles high-leverage points. This article shows how to generate outliers in multivariate normal data that are a

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Analytics | Data Visualization

Rick WicklinMarch 4, 2019 1

Visualize SAT scores in North Carolina

Standardized tests like the SAT and ACT can cause stress for both high school students and their parents, but according to a Wall Street Journal article, the SAT and ACT "provide an invaluable measure of how students are likely to perform in college and beyond." Naturally, students wonder how their

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Analytics | Data Visualization | Learn SAS

Rick WicklinFebruary 25, 2019 0

Graphs of bootstrap statistics in PROC TTEST

When I run a bootstrap analysis, I create graphs to visualize the distribution of the bootstrap statistics. For example, in my article about how to bootstrap the difference of means in a two-sample t test, I included a histogram of the bootstrap distribution and added reference lines to indicate a

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Analytics | Data Visualization

Rick WicklinFebruary 20, 2019 7

An easier way to create a calibration plot in SAS

Last year I published a series of blogs posts about how to create a calibration plot in SAS. A calibration plot is a way to assess the goodness of fit for a logistic model. It is a diagnostic graph that enables you to qualitatively compare a model's predicted probability of

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Analytics | Learn SAS | Programming Tips

Rick WicklinFebruary 13, 2019 0

3 ways to obtain the Hessian at the MLE solution for a regression model

When you use maximum likelihood estimation (MLE) to find the parameter estimates in a generalized linear regression model, the Hessian matrix at the optimal solution is very important. The Hessian matrix indicates the local shape of the log-likelihood surface near the optimal value. You can use the Hessian to estimate

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