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The PRINCOMP Procedure This Document Is an Individual Chapter from SAS/STAT® 13.1 User’S Guide SAS/STAT® 13.1 User’s Guide The PRINCOMP Procedure This document is an individual chapter from SAS/STAT® 13.1 User’s Guide. The correct bibliographic citation for the complete manual is as follows: SAS Institute Inc. 2013. SAS/STAT® 13.1 User’s Guide. Cary, NC: SAS Institute Inc. Copyright © 2013, SAS Institute Inc., Cary, NC, USA All rights reserved. Produced in the United States of America. For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a) and DFAR 227.7202-4 and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). 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Discover all that you need on your journey to knowledge and empowerment. support.sas.com/bookstore for additional books and resources. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. © 2013 SAS Institute Inc. All rights reserved. S107969US.0613 Chapter 77 The PRINCOMP Procedure Contents Overview: PRINCOMP Procedure............................... 6619 Getting Started: PRINCOMP Procedure............................ 6621 Syntax: PRINCOMP Procedure................................ 6627 PROC PRINCOMP Statement.............................. 6627 BY Statement ...................................... 6633 FREQ Statement..................................... 6633 ID Statement....................................... 6633 PARTIAL Statement................................... 6634 VAR Statement...................................... 6634 WEIGHT Statement................................... 6634 Details: PRINCOMP Procedure................................ 6634 Missing Values...................................... 6634 Output Data Sets..................................... 6635 Computational Resources ................................ 6637 Displayed Output..................................... 6638 ODS Table Names.................................... 6638 ODS Graphics...................................... 6639 Examples: PRINCOMP Procedure............................... 6640 Example 77.1: Analyzing Mean Temperatures of US Cities . 6640 Example 77.2: Analyzing Rankings of US College Basketball Teams . 6643 Example 77.3: Analyzing Job Ratings of Police Officers . 6651 References........................................... 6667 Overview: PRINCOMP Procedure The PRINCOMP procedure performs principal component analysis. As input, you can use raw data, a correlation matrix, a covariance matrix, or a sum-of-squares-and-crossproducts (SSCP) matrix. You can create output data sets that contain eigenvalues, eigenvectors, and standardized or unstandardized principal component scores. Principal component analysis is a multivariate technique for examining relationships among several quantita- tive variables. The choice between using factor analysis and using principal component analysis depends in part on your research objectives. You should use the PRINCOMP procedure if you are interested in summarizing data and detecting linear relationships. You can use principal component analysis to reduce the 6620 F Chapter 77: The PRINCOMP Procedure number of variables in regression, clustering, and so on. For a detailed comparison of the PRINCOMP and FACTOR procedures, see Chapter 9, “Introduction to Multivariate Procedures.” You can use ODS Graphics to display the scree plot, component pattern plot, component pattern profile plot, matrix plot of component scores, and component score plots. These plots are especially valuable tools in exploratory data analysis. Principal component analysis was originated by Pearson(1901) and later developed by Hotelling(1933). The application of principal components is discussed by Rao(1964); Cooley and Lohnes(1971); Gnanadesikan (1977). Excellent statistical treatments of principal components are found in Kshirsagar(1972); Morrison (1976); Mardia, Kent, and Bibby(1979). If you have a data set that contains p numeric variables, you can compute p principal components. Each principal component is a linear combination of the original variables, with coefficients equal to the eigen- vectors of the correlation or covariance matrix. The eigenvectors are usually taken with unit length. The principal components are sorted by descending order of the eigenvalues, which are equal to the variances of the components. Principal components have a variety of useful properties (Rao 1964; Kshirsagar 1972): • The eigenvectors are orthogonal, so the principal components represent jointly perpendicular directions through the space of the original variables. • The principal component scores are jointly uncorrelated. Note that this property is quite distinct from the previous one. • The first principal component has the largest variance of any unit-length linear combination of the observed variables. The jth principal component has the largest variance of any unit-length linear combination orthogonal to the first j – 1 principal components. The last principal component has the smallest variance of any linear combination of the original variables. • The scores on the first j principal components have the highest possible generalized variance of any set of unit-length linear combinations of the original variables. • The first j principal components provide a least squares solution to the model Y XB E D C where Y is an n p matrix of the centered observed variables; X is the n j matrix of scores on the first j principal components; B is the j p matrix of eigenvectors; E is an n p matrix of residuals; and you want to minimize trace.E0E/, the sum of all the squared elements in E. In other words, the first j principal components are the best linear predictors of the original variables among all possible sets of j variables, although any nonsingular linear transformation of the first j principal components would provide equally good prediction. The same result is obtained if you want to minimize the determinant or the Euclidean (Schur, Frobenius) norm of E0E rather than the trace. • In geometric terms, the j-dimensional linear subspace that is spanned by the first j principal components provides the best possible fit to the data points as measured by the sum of squared perpendicular distances from each data point to the subspace. This contrasts with the geometric interpretation of least squares regression, which minimizes the sum of squared vertical distances. For example, suppose you have two variables. Then, the first principal component minimizes the sum of squared perpendicular distances from the points to the first principal axis. This contrasts with least squares, which would minimize the sum of squared vertical distances from the points to the fitted line. Getting Started: PRINCOMP Procedure F 6621 Principal component analysis can also be used for exploring polynomial relationships and for multivariate outlier detection (Gnanadesikan 1977), and it is related to factor analysis, correspondence analysis, allometry, and biased regression techniques (Mardia, Kent, and Bibby 1979). Getting Started: PRINCOMP Procedure The following data provide crime rates per 100,000 people in seven categories for each of the 50 US states in 1977. Because there are seven numeric variables, it is impossible to plot all the variables simultaneously. You can use principal components to summarize the data in two or three dimensions, and they help you visualize the data. The following statements
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