Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of SAS/IML software. His areas of expertise include computational statistics, simulation, statistical graphics, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.
It is easy to use PROC SGPLOT and BY-group processing to create an animated graph in SAS 9.4. Sanjay Matange previously discussed how to create an animated plot in SAS 9.4, but he used a macro loop to call PROC SGPLOT many times. It is often easier to use the
Last week I showed how to use the simple bootstrap to randomly resample from the data to create B bootstrap samples, each containing N observations. The simple bootstrap is equivalent to sampling from the empirical cumulative distribution function (ECDF) of the data. An alternative bootstrap technique is called the smooth
Last week I showed some features of SAS formats, including the fact that you can use formats to bin a continuous variable without creating a new variable in the DATA step. During the discussion I mentioned that it can be confusing to look at the output of a formatted variable
A common question is "how do I compute a bootstrap confidence interval in SAS?" As a reminder, the bootstrap method consists of the following steps: Compute the statistic of interest for the original data Resample B times from the data to form B bootstrap samples. How you resample depends on
SAS formats are flexible, dynamic, and have many uses. For example, you can use formats to count missing values and to change the order of a categorical variable in a table or plot. Did you know that you can also use SAS formats to recode a variable or to bin
My presentation at SAS Global Forum 2016 was titled "Writing Packages: A New Way to Distribute and Use SAS/IML Programs." The paper was published in the conference proceedings several months ago, but I recently recorded a short video that gives an overview of using and creating packages in SAS/IML 14.1:
In a scatter plot, the regions where observations are packed tightly are areas of high density. A contour plot or heat map of a bivariate kernel density estimate (KDE) is one way to visualize regions of high density. A SAS customer asked whether it is possible to use SAS to
This week Hillary Clinton became the first woman to be nominated for president of the US by a major political party. Although this is a first for the US, many other countries have already passed this milestone. In fact, 60 countries have already elected women as presidents and prime ministers.
A kernel density estimate (KDE) is a nonparametric estimate for the density of a data sample. A KDE can help an analyst determine how to model the data: Does the KDE look like a normal curve? Like a mixture of normals? Is there evidence of outliers in the data? In
Last week I read an interesting paper by Bob Rodriguez: "Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT Software." In it, Rodriguez summarizes five modern techniques for building predictive models and highlights recent SAS/STAT procedures that implement those techniques. The paper discusses the following high-performance (HP)
