ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients
- PMID: 26688802
- PMCID: PMC4681537
- DOI: 10.5351/CSAM.2015.22.6.665
ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients
Abstract
Lack of a general matrix formula hampers implementation of the semi-partial correlation, also known as part correlation, to the higher-order coefficient. This is because the higher-order semi-partial correlation calculation using a recursive formula requires an enormous number of recursive calculations to obtain the correlation coefficients. To resolve this difficulty, we derive a general matrix formula of the semi-partial correlation for fast computation. The semi-partial correlations are then implemented on an R package ppcor along with the partial correlation. Owing to the general matrix formulas, users can readily calculate the coefficients of both partial and semi-partial correlations without computational burden. The package ppcor further provides users with the level of the statistical significance with its test statistic.
Keywords: correlation; part correlation; partial correlation; ppcor; semi-partial correlation.
Figures
References
-
- Abdi H. Kendall rank correlation. In: Salkind NJ, editor. Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage; 2007. pp. 508–510.
-
- Baum ES, Rude SS. Acceptance-enhanced expressive writing prevents symptoms in participants with low initial depression. Cognitive Therapy and Research. 2013;37:35–42.
-
- Castelo R, Roverato A. A Robust Procedure for Gaussian Graphical Model Search from Microarray Data with p Larger than n. J Mach Learn Res. 2006;7:2621–2650.
-
- Drummond DA, Raval A, Wilke CO. A Single Determinant Dominates the Rate of Yeast Protein Evolution. Molecular Biology and Evolution. 2006;23:327–337. - PubMed
Grants and funding
LinkOut - more resources
Full Text Sources