DOI QR코드

DOI QR Code

ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients

  • Kim, Seongho (Biostatistics Core, Karmanos Cancer Institute, Wayne State University)
  • Received : 2015.09.25
  • Accepted : 2015.11.20
  • Published : 2015.11.30

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

References

  1. Abdi, H. (2007). Kendall rank correlation, In N.J. Salkind (Ed.), Encyclopedia of Measurement and Statistics, Thousand Oaks (CA), Sage, 508-510.
  2. Baum, E. S. and Rude, S. S. (2013). Acceptance-enhanced expressive writing prevents symptoms in participants with low initial depression, Cognitive Therapy and Research, 37, 35-42. https://doi.org/10.1007/s10608-012-9435-x
  3. Castelo, R. and Roverato, A. (2006). A robust procedure for Gaussian graphical model search from microarray data with p larger than n, Journal of Machine Learning Research, 7, 2621-2650.
  4. Drummond, D. A., Raval, A. and Wilke, C. O. (2006). A single determinant dominates the rate of yeast protein evolution, Molecular Biology and Evolution, 23, 327-337. https://doi.org/10.1093/molbev/msj038
  5. Fang, X. Z., Luo, L., Reveille, J. D. and Xiong, M. (2009). Discussion: Why do we test multiple traits in genetic association studies?, Journal of the Korean Statistical Society, 38, 17-23. https://doi.org/10.1016/j.jkss.2008.10.008
  6. Fox, J. (2005). The R Commander: A basic-statistics graphical user interface to R, Journal of Statistical Software, 14, 1-42.
  7. James, S. (2002). Applied Multivariate Statistics for the Social Sciences, Lawrence Erlbaum Associates, Inc., Mahwah, NJ.
  8. Johnson, R. A. and Wichern, D. W. (2002). Applied Multivariate Statistical Analysis, Prentice Hall.
  9. Kim, S., Koo, I., Jeong, J., Wu, S., Shi, X. and Zhang, X. (2012). Compound identification using partial and semipartial correlations for gas chromatography-mass spectrometry data, Analytical Chemistry, 12, 6477-6487.
  10. Kim, S. and Yi, S. (2007). Understanding relationship between sequence and functional evolution in yeast proteins, Genetica, 131, 151-156. https://doi.org/10.1007/s10709-006-9125-2
  11. Kim, S. and Zhang, X. (2013). Comparative analysis of mass spectral similarity measures on peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry, Computational and Mathematical Methods in Medicine, 2013, 509761.
  12. Kramer, N., Schafer, J. and Boulesteix, A. L. (2009). Regularized estimation of large scale gene association networks using Gaussian graphical models, BMC Bioinformatics, 10, 384. https://doi.org/10.1186/1471-2105-10-384
  13. Olkin, I. and Finn, J. D. (1995). Correlations redux, Psychological Bulletin, 118, 155-164. https://doi.org/10.1037/0033-2909.118.1.155
  14. Peng, J., Wang, P., Zhou, N. and Zhu, J. (2009). Partial correlation estimation by joint sparse regression models, Journal of the American Statistical Association, 104, 735-746. https://doi.org/10.1198/jasa.2009.0126
  15. Penrose, R. (1995). A generalized inverse for matrices, In Proceedings of the Cambridge Philosophical Society, 51, 406-413.
  16. R Development Core Team (2015). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: http://www.R-project.org/
  17. Schafer, J. and Strimmer, K. (2005a). A shrinkage approach to large-scale covariance matrix estimation and implications for functional Genomics, Statistical Applications in Genetics and Molecular Biology, 4, 32.
  18. Schafer, J. and Strimmer, K. (2005b). An empirical Bayes approach to inferring large-scale gene association networks, Bioinformatics, 21, 754-764. https://doi.org/10.1093/bioinformatics/bti062
  19. Sharma, J. K. (2012). Business Statistics, Pearson Education India.
  20. Sheskin, D. J. (2003). Handbook of Parametric and Nonparametric Statistical Procedures: Third Edition, CRC Press.
  21. Stanley, T. D. and Doucouliagos, H. (2012) Meta-Regression Analysis in Economics and Business, Routledge.
  22. Vanderlinden, L. A., Saba, L. M., Kechris, K., Miles, M. F., Hoffman, P. L. and Tabakoff, B. (2013). Whole brain and brain regional coexpression network interactions associated with predisposition to alcohol consumption, PLoS ONE, 8, e68878. https://doi.org/10.1371/journal.pone.0068878
  23. Watson-Haigh, N. S., Kadarmideen, H. N. and Reverter, A. (2010). PCIT: An R Package for weighted gene co-expression networks based on partial correlation and information theory approaches, Bioinformatics, 26, 411-413. https://doi.org/10.1093/bioinformatics/btp674
  24. Weatherburn, C. E. (1968). A First Course Mathematical Statistics, Cambridge.
  25. Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics, John Wiley & Sons, New York.
  26. Zhu, W. S. and Zhang, H. P. (2009). Why do we test multiple traits in genetic association studies?, Journal of the Korean Statistical Society, 38, 1-10. https://doi.org/10.1016/j.jkss.2008.10.006

Cited by

  1. Statistical link between external climate forcings and modes of ocean variability 2017, https://doi.org/10.1007/s00382-017-3832-5
  2. The genetic architecture of UV floral patterning in sunflower vol.120, pp.1, 2017, https://doi.org/10.1093/aob/mcx038
  3. Shoot growth of woody trees and shrubs is predicted by maximum plant height and associated traits 2017, https://doi.org/10.1111/1365-2435.12972
  4. Factors affecting the inter-annual to centennial timescale variability of Indian summer monsoon rainfall 2018, https://doi.org/10.1007/s00382-017-3879-3
  5. Development of an Objective Autism Risk Index Using Remote Eye Tracking vol.55, pp.4, 2016, https://doi.org/10.1016/j.jaac.2016.01.011
  6. Automated Assessment of Non-Native Learner Essays: Investigating the Role of Linguistic Features 2018, https://doi.org/10.1007/s40593-017-0142-3
  7. Brief communication: An analysis of dental development in Pleistocene Homo using skeletal growth and chronological age vol.163, pp.3, 2017, https://doi.org/10.1002/ajpa.23228
  8. An ecological connectivity network maintains genetic diversity of a flagship wildflower, Pulsatilla vulgaris vol.212, 2017, https://doi.org/10.1016/j.biocon.2017.05.026
  9. Casting doubt on the causal link between intelligence and age at first intercourse: A cross-generational sibling comparison design using the NLSY vol.59, 2016, https://doi.org/10.1016/j.intell.2016.08.008
  10. Methyleugenol DNA adducts in human liver are associated with SULT1A1 copy number variations and expression levels vol.91, pp.10, 2017, https://doi.org/10.1007/s00204-017-1955-4
  11. Tracking Cognitive Decline in Amnestic Mild Cognitive Impairment and Early-Stage Alzheimer Dementia: Mini-Mental State Examination versus Neuropsychological Battery vol.44, pp.1-2, 2017, https://doi.org/10.1159/000478520
  12. Diurnal preference, circadian phase of entrainment and time perspectives: Just what are the relationships? vol.112, 2017, https://doi.org/10.1016/j.paid.2017.02.051
  13. Gene-Family Extension Measures and Correlations vol.6, pp.3, 2016, https://doi.org/10.3390/life6030030
  14. Transcriptional polymorphism of pi RNA regulatory genes underlies the mariner activity in Drosophila simulans testes vol.26, pp.14, 2017, https://doi.org/10.1111/mec.14145
  15. Behavior and temperature modulate a thermoregulation-predation risk trade-off in juvenile gopher tortoises vol.123, pp.12, 2017, https://doi.org/10.1111/eth.12695
  16. A global atlas of the dominant bacteria found in soil vol.359, pp.6373, 2018, https://doi.org/10.1126/science.aap9516
  17. Systems genetic analysis of brown adipose tissue function vol.50, pp.1, 2018, https://doi.org/10.1152/physiolgenomics.00091.2017
  18. Anatomical defences against bark beetles relate to degree of historical exposure between species and are allocated independently of chemical defences within trees pp.01407791, 2018, https://doi.org/10.1111/pce.13449
  19. Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation vol.7, pp.2050-084X, 2018, https://doi.org/10.7554/eLife.37059
  20. Ecological Analyses of Mycobacteria in Showerhead Biofilms and Their Relevance to Human Health vol.9, pp.5, 2018, https://doi.org/10.1128/mBio.01614-18
  21. Cognitive abilities and speakers’ adaptation of a new acoustic form: A case of a /o/-raising in Seoul Korean* vol.10, pp.3, 2018, https://doi.org/10.13064/KSSS.2018.10.3.1
  22. Cognitive abilities and speakers’ adaptation of a new acoustic form: A case of a /o/-raising in Seoul Korean* vol.10, pp.3, 2018, https://doi.org/10.13064/KSSS.2018.10.3.001
  23. Arabidopsis Heat Stress-Induced Proteins Are Enriched in Electrostatically Charged Amino Acids and Intrinsically Disordered Regions vol.19, pp.8, 2018, https://doi.org/10.3390/ijms19082276
  24. Whole-brain atrophy assessed by proportional- versus registration-based pipelines from 3T MRI in multiple sclerosis vol.8, pp.8, 2018, https://doi.org/10.1002/brb3.1068
  25. Environmental conditions synchronize waterbird mortality events in the Great Lakes vol.55, pp.3, 2018, https://doi.org/10.1111/1365-2664.13063
  26. Quantitative comparison of different iron forms in the temporal cortex of Alzheimer patients and control subjects vol.8, pp.1, 2018, https://doi.org/10.1038/s41598-018-25021-7
  27. vol.210, pp.3, 2018, https://doi.org/10.1534/genetics.118.301349
  28. Ancient exapted transposable elements promote nuclear enrichment of human long noncoding RNAs vol.29, pp.2, 2019, https://doi.org/10.1101/gr.229922.117
  29. Comparison of EQ-5D-5L and SPVU-5D for measuring quality of life in patients with venous leg ulcers in an Australian setting pp.1573-2649, 2019, https://doi.org/10.1007/s11136-019-02128-6