I recently showed someone a trick to create a graph, and he was extremely pleased to learn it. The trick is well known to many SAS users, but I hope that this article will introduce it to even more SAS users. At issue is how to use the SGPLOT procedure
A SAS user asked an interesting question on the SAS/GRAPH and ODS Graphics Support Forum. The question is: Does PROC SGPLOT support a way to display the slope of the regression line that is computed by the REG statement? Recall that the REG statement in PROC SGPLOT fits and displays
It seemed like an easy task. A SAS user asked me how to use the SGPLOT procedure to create a bar chart where the vertical axis shows percentages instead of counts. I assumed that there was some simple option that would change the scale of the vertical axis from counts
The other day I was using PROC SGPLOT to create a box plot and I ran a program that was similar to the following: proc sgplot data=sashelp.cars; title "Box Plot: Category = Origin"; vbox Horsepower / category=origin; run; An hour or so later I had a need for another box
Some SAS products such as SAS/IML Studio (which is included FREE as part of SAS/IML software) have interactive graphics. This makes it easy to interrogate a graph to determine values of "hidden" variables that might not appear in the graph. For example, in a scatter plot in SAS/IML Studio, you
In a previous article we discussed how to add axis aligned statistics table to a Lipid graph using GTL. Other graphs such as the Survival Plot also utilize the same technique to display the "at risk" statistics aligned by time or visits along the X axis. Often, we also need to display
In this blog we will discuss many aspects of the SG Procedures. This article will cover some basic features and workings of the SGPLOT procedure to establish a baseline. The single-cell graph is the work horse for data visualization. From the simple bar chart to the complex patient profiles for clinical
Sometimes you want to label only certain observations in a plot. This is useful in many ways, but one use is to label outliers on a scatter plot. In the SGPLOT procedure, the DATALABEL= option enables you to specify the name of a variable that is used to label observations.
At SAS Global Forum last week, a customer approached me with a very specific request. The conversation went something like this: Customer: My client demands a bar chart that uses a bar for one response, and a symbol for other responses, all on the same chart. We know it's possible
SAS/STAT offers a principled treatment of missing data in a frequentist paradigm. SAS' Michael Senter reveals how with a few code tweaks, you can seamlessly transition to a Bayesian analysis.
A previous article describes how to use SAS to find the inflection points of a 1-D function that you can evaluate at any point. The function must be given by a formula (or by an algorithm) because the root-finding algorithm needs to evaluate the function at arbitrary locations. However, sometimes
A colleague asked me an interesting question: Suppose you have a structured correlation matrix, such as a matrix that has a compound symmetric, banded, or an AR1(ρ) structure. If you generate a random correlation matrix that has the same eigenvalues as the structured matrix, does the random matrix have the
O Christmas tree, O Christmas tree, How lovely are your branches! SAS programmers have a long history of creating yuletide-themed graphics. Christmas trees are a popular image because of their simplicity. I admit that I have indulged more than once in this holiday tradition: An old-school ASCII art image A
While researching the topic of Latin hypercube sampling (LHS), I read an article by Emily Gao (2019) that shows how to use PROC IML in SAS to perform the algorithm. It is possible to simplify Gao's implementation of Latin hypercube sampling in SAS while also making the computation more efficient.
Order matters. The order of variables in tables and rows of a correlation matrix can make a big difference in how easy it is to observed correlations between variables or groups of variables. There are many ways to order the variables, but this article shows how to display the variables
When the SAS Global Forum 2020 conference was cancelled by the global COVID-19 pandemic, I felt sorry for the customers and colleagues who had spent months preparing their presentations. One presentation I especially wanted to attend was by Bucky Ransdell and Randy Tobias: "Introducing PROC SIMSYSTEM for Systematic Nonnormal Simulation".
In SAS, range attribute maps enable you to specify the range of values that determine the colors used for graphical elements. There are various examples that use the GTL to define a range attribute map, but fewer examples that show how to use a range attribute map with PROC SGPLOT.
A common way to visualize the sample correlations between many numeric variables is to display a heat map that shows the Pearson correlation for each pair of variables, as shown in the image to the right. The correlation is a number in the range [-1, 1], where -1 indicated perfect
SAS software supports two kinds of procedures: interactive and non-interactive. Most SAS procedures are non-interactive. They begin with a PROC statement, include one or more additional statements, and end with a RUN statement. When SAS encounters the RUN statement, the procedure executes all statements, then exits. On the other hand,
A remarkable result in probability theory is the "three-sigma rule," which is a generic name for theorems that bound the probability that a univariate random variable will appear near the center of its distribution. This article discusses the familiar three-sigma rule for the normal distribution, a less-familiar rule for unimodal
Many well-known distributions become more and more "normal looking" for large values of a parameter. Famously, the binomial distribution, Binom(p, N), can be approximated by a normal distribution when N (the sample size) is large. Similarly, the Poisson(λ) distribution is well approximated by the normal distribution when λ is large.
This article shows how to simulate data from a Poisson regression model, including how to account for an offset variable. If you are not familiar with how to run a Poisson regression in SAS, see the article "Poisson regression in SAS." A Poisson regression model is a specific type of
This article demonstrates how to use PROC GENMOD to perform a Poisson regression in SAS. There are different examples in the SAS documentation and in conference papers, but I chose this example because it uses two categorical explanatory variables. Therefore, the Poisson regression can be visualized by using a contingency
A previous article shows that you can run a simple (one-variable) isotonic regression by using a quadratic programming (QP) formulation. While I was reading a book about computational geometry, I learned that there is a connection between isotonic regression and the convex hull of a certain set of points. Whaaaaat?
Since the pandemic began in 2020, the SAS IML developers have added about 50 new functions and enhancements to the SAS IML language in SAS Viya. Among these functions are new modern methods for optimization that have a simplified syntax as compared to the older 'NLP' functions that are available
Isotonic regression (also called monotonic regression) is a type of regression model that assumes that the response variable is a monotonic function of the explanatory variable(s). The model can be nondecreasing or nonincreasing. Certain physical and biological processes can be analyzed by using an isotonic regression model. For example, a
A previous article discusses the fact that there are often multiple ways in SAS to obtain the same result. This fact results in many vigorous discussions on online programming forums as people propose different (but equivalent) methods for solving someone's problem then argue why their preferred method is better than
While reviewing a book on numerical analysis, I was reminded of a classic interpolation problem. Suppose you have n pairs of points in the plane: (x1,y1), (x2,y2), ..., (xn,yn), where the first coordinates are distinct. Then you can construct a unique polynomial of degree (at most) n-1 that passes through
A previous article discusses a formula for a confidence interval for R-square in a linear regression model (Olkin and Finn (1995) "Correlations redux", Psychological Bulletin) The formula is useful for large data sets, but should be used with caution for small samples. At the end of the previous article, I
A SAS analyst ran a linear regression model and obtained an R-square statistic for the fit. However, he wanted a confidence interval, so he posted a question to a discussion forum asking how to obtain a confidence interval for the R-square parameter. Someone suggested a formula from a textbook (Cohen,
