PCAtest: testing the statistical significance of Principal Component Analysis in R.

ABSTRACT: 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 sig

SUBMITTER: Camargo A 

PROVIDER: S-EPMC8858582 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature