Introduction to SAS/IML Studio

Summary of Features

SAS/IML Studio provides tools for exploring data, analyzing distributions, fitting parametric and nonparametric regression models, and analyzing multivariate relationships. In addition, you can extend the set of available analyses by writing programs.

To explore data, you can do the following:

  • identify observations in plots

  • select observations in linked data tables, bar charts, box plots, contour plots, histograms, line plots, mosaic plots, and two- and three-dimensional scatter plots

  • exclude observations from graphs and analyses

  • search, sort, subset, and extract data

  • transform variables

  • change the color and shape of observation markers based on the value of a variable

To analyze distributions, you can do the following:

  • compute descriptive statistics

  • create quantile-quantile plots

  • create mosaic plots of cross-classified data

  • fit parametric and kernel density estimates for distributions

  • detect outliers in contaminated Gaussian data

To fit parametric and nonparametric regression models, you can do the following:

  • smooth two-dimensional data by using polynomials, loess curves, and thin-plate splines

  • add confidence bands for mean and predicted values

  • create residual and influence diagnostic plots

  • fit robust regression models and detect outliers and high-leverage observations

  • fit logistic models

  • fit the general linear model with a wide variety of response and link functions

  • include classification effects in logistic and generalized linear models

To analyze multivariate relationships, you can do the following:

  • calculate correlation matrices and scatter plot matrices with confidence ellipses for relationships among pairs of variables

  • reduce dimensionality with principal component analysis

  • examine relationships between a nominal variable and a set of interval variables with discriminant analysis

  • examine relationships between two sets of interval variables with canonical correlation analysis

  • reduce dimensionality by computing common factors for a set of interval variables with factor analysis

  • reduce dimensionality and graphically examine relationships between categorical variables in a contingency table with correspondence analysis

To extend the set of available analyses, you can do the following:

  • write, debug, and execute IMLPlus programs in an integrated development environment

  • add legends, curves, maps, or other custom features to statistical graphics

  • create new static graphics

  • animate graphics

  • execute SAS procedures or DATA steps from within your IMLPlus programs

  • develop interactive data analysis programs that use dialog boxes

  • call computational routines written in C, FORTRAN, Java, R, or the SAS/IML language