Providing software solutions since 1976
Capabilities
- Analysis of Variance
- Bayesian Analysis
- Categorical Data Analysis
- Cluster Analysis
- Descriptive Statistics
- Distribution Analysis
- Discriminant Analysis
- Exact Methods
- Group Sequential Design and Analysis
- Market Research
- Missing Value Imputation
- Mixed Models
- Multivariate Analysis
- Nonparametric Statistics
- Power and Sample Size
- Psychometric Analysis
- Regression
- Spatial Analysis
- Structural Equations
- Survey Sampling and Analysis
- Survival Analysis
- Transformations
- SAS/STAT Procedures A-Z
The PRINQUAL procedure
- performs principal component analysis (PCA) of qualitative, quantitative, or mixed data
- finds linear and nonlinear transformations of variables, using the method of alternating least squares, that optimize properties of the transformed variables’ correlation or covariance matrix. Nonoptimal transformations such as logarithm and rank are also available.
- fit metric and nonmetric principal component analyses
- perform metric and nonmetric multidimensional preference (MDPREF) analyses
- reduce the number of variables for subsequent use in regression analyses, cluster analyses, and other analyses
- detect nonlinear relationships
- provides three methods, each of which seeks to optimize a different property of
the transformed variables’ covariance or correlation matrix. These methods are as follows:
- maximum total variance, or MTV
- minimum generalized variance, or MGV
- maximum average correlation, or MAC
- transform nominal variables by optimally scoring the categories
- transform ordinal variables monotonically by scoring the ordered categories so that order is weakly preserved (adjacent categories can be merged) and the covariance matrix is optimized. You can undo ties optimally or leave them tied. You can also transform ordinal variables to ranks.
- transform interval and ratio scale of measurement variables linearly, or transform them nonlinearly with spline transformations or monotone spline transformations . In addition, nonoptimal transformations for logarithm, rank, exponential, power, logit, and inverse trigonometric sine are available.
- estimate missing data without constraint, with category constraints (missing values within the same group get the same value), and with order constraints (missing value estimates in adjacent groups can be tied to preserve a specified ordering).
- perform weighted estimation
- obtain separate analyses on observations in groups
- output SAS data set containing original variables, transformed variables, components, or data approximations
- uses ODS to create a SAS data set corresponding to any table
- supports ODS Graphics
For further details see the SAS/STAT User's Guide:
The PRINQUAL Procedure
( PDF | HTML )
Examples
Statistics and Operations Research Home Page | SAS/STAT Software
