PCAtest: testing the statistical significance of Principal Component Analysis in R
Principal Component Analysis (PCA) is one of the most broadly used statistical methods for the ordination and dimensionality-reduction of multivariate datasets across many scientific disciplines. Trivial PCs can be estimated from data sets without any correlational structure among the original varia...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-02-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/12967.pdf |
| _version_ | 1797425946810122240 |
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| author | Arley Camargo |
| author_facet | Arley Camargo |
| author_sort | Arley Camargo |
| collection | DOAJ |
| description | Principal Component Analysis (PCA) is one of the most broadly used statistical methods for the ordination and dimensionality-reduction of multivariate datasets across many scientific disciplines. Trivial PCs can be estimated from data sets without any correlational structure among the original variables, and traditional criteria for selecting non-trivial PC axes are difficult to implement, partially subjective or based on ad hoc thresholds. PCAtest is an R package that implements permutation-based statistical tests to evaluate the overall significance of a PCA, the significance of each PC axis, and of contributions of each observed variable to the significant axes. Based on simulation and empirical results, I encourage R users to routinely apply PCAtest to test the significance of their PCA before proceeding with the direct interpretation of PC axes and/or the utilization of PC scores in subsequent evolutionary and ecological analyses. |
| first_indexed | 2024-03-09T08:23:18Z |
| format | Article |
| id | doaj.art-13f5b7cc5735442b85a31acceb1edd8c |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:23:18Z |
| publishDate | 2022-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-13f5b7cc5735442b85a31acceb1edd8c2023-12-02T21:37:42ZengPeerJ Inc.PeerJ2167-83592022-02-0110e1296710.7717/peerj.12967PCAtest: testing the statistical significance of Principal Component Analysis in RArley CamargoPrincipal Component Analysis (PCA) is one of the most broadly used statistical methods for the ordination and dimensionality-reduction of multivariate datasets across many scientific disciplines. Trivial PCs can be estimated from data sets without any correlational structure among the original variables, and traditional criteria for selecting non-trivial PC axes are difficult to implement, partially subjective or based on ad hoc thresholds. PCAtest is an R package that implements permutation-based statistical tests to evaluate the overall significance of a PCA, the significance of each PC axis, and of contributions of each observed variable to the significant axes. Based on simulation and empirical results, I encourage R users to routinely apply PCAtest to test the significance of their PCA before proceeding with the direct interpretation of PC axes and/or the utilization of PC scores in subsequent evolutionary and ecological analyses.https://peerj.com/articles/12967.pdfPrincipal component analysisStatistical significancePermutationR functionPCAtest |
| spellingShingle | Arley Camargo PCAtest: testing the statistical significance of Principal Component Analysis in R PeerJ Principal component analysis Statistical significance Permutation R function PCAtest |
| title | PCAtest: testing the statistical significance of Principal Component Analysis in R |
| title_full | PCAtest: testing the statistical significance of Principal Component Analysis in R |
| title_fullStr | PCAtest: testing the statistical significance of Principal Component Analysis in R |
| title_full_unstemmed | PCAtest: testing the statistical significance of Principal Component Analysis in R |
| title_short | PCAtest: testing the statistical significance of Principal Component Analysis in R |
| title_sort | pcatest testing the statistical significance of principal component analysis in r |
| topic | Principal component analysis Statistical significance Permutation R function PCAtest |
| url | https://peerj.com/articles/12967.pdf |
| work_keys_str_mv | AT arleycamargo pcatesttestingthestatisticalsignificanceofprincipalcomponentanalysisinr |