The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
Principal component analysis (PCA) is an important tool for understanding relationships in continuous multivariate data. When the first two principal components (PCs) explain a significant portion of the variance in the data, you can visualize the data by projecting the observations onto the span of the first two PCs. In
Understanding multivariate statistics requires mastery of high-dimensional geometry and concepts in linear algebra such as matrix factorizations, basis vectors, and linear subspaces. Graphs can help to summarize what a multivariate analysis is telling us about the data. This article looks at four graphs that are often part of a principal
Every year at Halloween, I post an article that shows a SAS trick that is a real treat. This article shows how to use the INTNX function to find dates that are related to a specified date. The INTNX function is a sweet treat, indeed. I previously wrote an article
A common task in SAS programming is to specify a list of variables that satisfy some pattern. You can specify lists for the KEEP= or DROP= data set options, and you can use lists of variables on many SAS statements such as the VAR and MODEL statements. Although SAS has
In response to a recent article about how to compute the cosine similarity of observations, a reader asked whether it is practical (or even possible) to perform these types of computations on data sets that have many thousands of observations. The problem is that the cosine similarity matrix is an
Computing rates and proportions is a common task in data analysis. When you are computing several proportions, it is helpful to visualize how the rates vary among subgroups of the population. Examples of proportions that depend on subgroups include: Mortality rates for various types of cancers Incarceration rates by race