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(.)
Quick start Principal component analysis of data Principal component analysis of v1, v2, v3, and v4 ... Be cautious if applying these to correlation matrices. Reporting level(#) specifies the confidence level, as a percentage, for confidence intervals. ... forcepsd modifies the matrix matname to be positive semidefinite (psd) and so to be ...
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