The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
In SAS/IML programs, a common task is to write values in a matrix to a SAS data set. For some programs, the values you want to write are in a matrix and you use the CREATE FROM/APPEND FROM syntax to create the data set, as follows: proc iml; X =
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
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
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
Did you know that SAS provides built-in support for working with probability distributions that are finite mixtures of normal distributions? This article shows examples of using the "NormalMix" distribution in SAS and describes a trick that enables you to easily work with distributions that have many components. As with all
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