Transcription of Title stata.com pca — Principal component analysis
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Title stata.com pca — Principal component analysis
Principal component analysisDescriptionQuick startMenuSyntaxOptionsOptions unique to pcamatRemarks and examplesStored resultsMethods and formulasReferencesAlso seeDescriptionpcaandpcamatdisplay the eigenvalues and eigenvectors from the Principal component analysis (PCA) eigen decomposition. The eigenvectors are returned in orthonormal form, that is, uncorrelatedand be used to reduce the number of variables or to learn about the underlying structure ofthe the correlation or covariance matrix directly. Forpca, the correlation orcovariance matrix is computed from the variables startPrincipal component analysis of dataPrincipal component analysis ofv1,v2,v3, andv4pca v1 v2 v3 v4As above, but retain only 2 componentspca v1 v2 v3 v4, components(2)As above, but retain only those components with eigenvalues greater than or equal to v1 v2 v3 v4, mineigen(.5) Principal component analysis of covariance matrix instead of correlation matrixpca v1 v2 v3 v4, covariancePrincipal component analysis of a correlation matrixPrincipal component analysis of matrixCrepresenting the correlations from 1,000 observationspcamat C, n(1000)As above, but retain only 4 componentspcamat C, n(1000) components(4)MenupcaStatistics>Multivari ate analysis >Factor and Principal component analysis > Principal component analysis (PCA)pcamatStatistics>Multivariate analysis >Factor and pr
2pca— Principal component analysis Syntax Principal component analysis of data pca varlist if in weight, options Principal component analysis of a correlation or covariance matrix pcamat matname, n(#) optionspcamat options matname is a k ksymmetric matrix or a k(k+ 1)=2 long row or column vector containing the
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