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.
Last week I got the following message: Dear Rick: How can I create a normal distribution within a specified range (min and max)? I need to simulate a normal distribution that fits within a specified range. I realize that a normal distribution is by definition infinite... Are there any alternatives,
Last week I wrote about the 10 most popular articles from The DO Loop in 2017. My most popular articles tend to be about elementary statistics or SAS programming tips. Less popular are the articles about advanced statistical and programming techniques. However, these technical articles fill an important niche. Not
A SAS programmer asked how to label multiple regression lines that are overlaid on a single scatter plot. Specifically, he asked to label the curves that are produced by using the REG statement with the GROUP= option in PROC SGPLOT. He wanted the labels to be the slope and intercept
I wrote more than 100 posts for The DO Loop blog in 2017. The most popular articles were about SAS programming tips, statistical data analysis, and simulation and bootstrap methods. Here are the most popular articles from 2017 in each category. General SAS programming techniques INTCK and INTNX: Do you
I previously showed an easy way to visualize a regression model that has several continuous explanatory variables: use the SLICEFIT option in the EFFECTPLOT statement in SAS to create a sliced fit plot. The EFFECTPLOT statement is directly supported by the syntax of the GENMOD, LOGISTIC, and ORTHOREG procedures in
Slice, slice, baby! You've got to slice, slice, baby! When you fit a regression model that has multiple explanatory variables, it is a challenge to effectively visualize the predicted values. This article describes how to visualize the regression model by slicing the explanatory variables. In SAS, you can use the
The SAS language is large. Even after 20+ years of using SAS, there are many features that I have never used. Recently it became necessary for me to learn about DICTIONARY tables in PROC SQL (and the associated SASHELP views) because I needed to programmatically obtain the text for the
Happy holidays to all my readers! My greeting-card to you is an image of a self-similar Christmas tree. The image (click to enlarge) was created in SAS by using two features that I blog about regularly: matrix computations and ODS statistical graphics. Self-similarity in Kronecker products I have previously shown
In a previous article, I showed how to use SAS to perform mean imputation. However, there are three problems with using mean-imputed variables in statistical analyses: Mean imputation reduces the variance of the imputed variables. Mean imputation shrinks standard errors, which invalidates most hypothesis tests and the calculation of confidence
Imputing missing data is the act of replacing missing data by nonmissing values. Mean imputation replaces missing data in a numerical variable by the mean value of the nonmissing values. This article shows how to perform mean imputation in SAS. It also presents three statistical drawbacks of mean imputation. How
Missing values present challenges for the statistical analyst and data scientist. Many modeling techniques (such as regression) exclude observations that contain missing values, which can reduce the sample size and reduce the power of a statistical analysis. Before you try to deal with missing values in an analysis (for example,
When you run an optimization, it is often not clear how to provide the optimization algorithm with an initial guess for the parameters. A good guess converges quickly to the optimal solution whereas a bad guess might diverge or require many iterations to converge. Many people use a default value
A statistical programmer read my article about the beta-binomial distribution and wanted to know how to compute the cumulative distribution (CDF) and the quantile function for this distribution. In general, if you know the PDF for a discrete distribution, you can also compute the CDF and quantile functions. This article
This article shows how to simulate beta-binomial data in SAS and how to compute the density function (PDF). The beta-binomial distribution is a discrete compound distribution. The "binomial" part of the name means that the discrete random variable X follows a binomial distribution with parameters N (number of trials) and
Did you know that a SAS/IML function can recover from a run-time error? You can specify how to handle run-time errors by using a programming technique that is similar to the modern "try-catch" technique, although the SAS/IML technique is an older implementation. Preventing errors versus handling errors In general, SAS/IML
Debugging is the bane of every programmer. SAS supports a DATA step debugger, but that debugger can't be used for debugging SAS/IML programs. In lieu of a formal debugger, many SAS/IML programmers resort to inserting multiple PRINT statements into a function definition. However, there is an easier way to query
A SAS programmer wanted to display a table in which the rows have different formats. An example is shown below. The programmer wanted columns that represent statistics and rows that represent variables. She wanted to display formats (such as DOLLAR) for some variables—but only for certain statistics. For example, the
Have you ever seen the "Fit Summary" table from PROC LOESS, as shown to the right? Or maybe you've seen the "Model Information" table that is displayed by some SAS analytical procedures? These tables provide brief interesting facts about a statistical procedure, hence they are called factoids. In SAS, a
A SAS/IML programmer asked whether you can pass the name of a function as an argument to a SAS/IML module and have the module call the function that is passed in. The answer is "yes." The basic idea is to create a string that represents the function call and then
This article demonstrates a SAS programming technique that I call Kuhfeld's template modification technique. The technique enables you to dynamically modify an ODS template and immediately call the modified template to produce a new graph or table. By following the five steps in this article, you can implement the technique
This article describes the advantages and disadvantages of principal component regression (PCR). This article also presents alternative techniques to PCR. In a previous article, I showed how to compute a principal component regression in SAS. Recall that principal component regression is a technique for handling near collinearities among the regression
A common question on discussion forums is how to compute a principal component regression in SAS. One reason people give for wanting to run a principal component regression is that the explanatory variables in the model are highly correlated which each other, a condition known as multicollinearity. Although principal component
In a previous article, I discussed the lines plot for multiple comparisons of means. Another graph that is frequently used for multiple comparisons is the diffogram, which indicates whether the pairwise differences between means of groups are statistically significant. This article discusses how to interpret a diffogram. Two related plots
In a previous article, I discussed the lines plot for multiple comparisons of means. Another graph that is frequently used for multiple comparisons is the diffogram, which indicates whether the pairwise differences between means of groups are statistically significant. This article discusses how to interpret a diffogram. Two related plots
Last week Warren Kuhfeld wrote about a graph called the "lines plot" that is produced by SAS/STAT procedures in SAS 9.4M5. (Notice that the "lines plot" has an 's'; it is not a line plot!) The lines plot is produced as part of an analysis that performs multiple comparisons of
The article "Fisher's transformation of the correlation coefficient" featured a Monte Carlo simulation that generated sample correlations from bivariate normal data. The simulation used three steps: Simulate B samples of size N from a bivariate normal distribution with correlation ρ. Use PROC CORR to compute the sample correlation matrix for
Correlations between variables are typically displayed in a matrix. Because the correlation matrix is determined by the order of the variables, it is difficult to find the largest and smallest correlations, which is why analysts sometimes use colors to visualize the correlation matrix. Another visualization option is the pairwise correlation
If you perform a weighted statistical analysis, it can be useful to produce a statistical graph that also incorporates the weights. This article shows how to construct and interpret a weighted histogram in SAS. How to construct a weighted histogram Before constructing a weighted histogram, let's review the construction of
How can you specify weights for a statistical analysis? Hmmm, that's a "weighty" question! Many people on discussion forums ask "What is a weight variable?" and "How do you choose a weight for each observation?" This article gives a brief overview of weight variables in statistics and includes examples of
In a large simulation study, it can be convenient to have a "control file" that contains the parameters for the study. My recent article about how to simulate multivariate normal clusters demonstrates a simple example of this technique. The simulation in that article uses an input data set that contains
