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(.)
Remarks and examples stata.com Principal component analysis (PCA) is commonly thought of as a statistical technique for data reduction. It helps you reduce the number of variables in an analysis by describing a series of uncorrelated linear combinations of the variables that contain most of the variance. In addition to
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