The starting point for your SAS® and Viya™ projects
Discover technical articles from the community
Snippets & Tutorials
FAQ & Help
Business Use Cases
Full Catalog & Examples
Preparation Scripts
SAS & Python Integration
News, tech watch and site updates
Participate in the life of the site
The boxPlot action calculates quantiles, high and low whiskers, and outliers for...
The compileCategory action builds a categories model from a set of category rule...
Reduces the dimensionality of nominal variables by using a multiple corresponden...
The 'computedOnDemand' subparameter (alias: 'compOnDemand', default: FALSE) in 'modelTable' controls whether computed variables are created when the table is loaded (TRUE) or when the action begins (FALSE).
The 'event' parameter specifies the formatted value of the response (target) variable that represents the event of interest.
The minCut action can produce two main output tables: 'outCutSets' to store the links that form the minimum cut sets, and 'outPartitions' to store the minimum cut partitions of the nodes.
Specifies the tolerance for percentile computation. Alias: pctlEpsilon. Default: 1E-05.
The `method` parameter allows you to choose the estimation technique. 'MLE' stands for Maximum Likelihood Estimation, which is the default. 'CAL' stands for the calibration method, which is typically faster but might be less accurate.