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

PeerJ

article

Arley Camargo¹ 

[1] Centro Universitario Regional Noreste, Universidad de la República

关键词: Principal component analysis;    Statistical significance;    Permutation;    R function;    PCAtest;   

DOI  :  10.7717/peerj.12967

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY