docs/references.md
In variani/cpca: Methods to perform Common Principal Component Analysis (CPCA)
Papers
About
This documents contains a temporal list of references, that were encountered ocasionally and saved for further consideration.
R packages
- multigroup package: This package includes several methods
to study multigroup data, where the same set of variables are measured on different groups of individuals.
- See function
FCPCA (that means Flury's Common Principal Component Analysis), where FG algorithm
proposed by Flury is implemented.
- MetaPCA Simultaneous dimension reduction using PCA when multiple studies are combined. ; available on CRAN
- eigenvalue maximization approach and angle minimization approach
- extension for Robust PCA and Sparse PCA in the meta-analysis realm
- Dongwan Don Kang Google Scholar
Multivariate Methods
- Zhang et al., Feature Transformation with Class Conditional Decorrelation, 2013 (pdf):
Fisher DA = Whitenning + PCA. Implementation of Whitenning was proposed to be Class Conditional Correlation (CCD).
CCD takes a list of class-related covariance matrices and diagonalizes them simultaneously.
Tests
- Rublik, A TEST OF THE HYPOTHESIS OF PARTIAL COMMON PRINCIPAL COMPONENTS, 2009 (pdf):
See Introduction for literature review.
- Boente et al., Robust tests for the common principal components model, 2009 (pdf):
See Section 7 Final Comments for a good summary.
- Diego et al., (2014) GAW19 contribution.
PCA tests
- Duscussion: http://stats.stackexchange.com/questions/33917/how-to-determine-significant-principal-components-using-bootstrapping-or-monte-c
- R package for permutation tests: http://cran.r-project.org/web/packages/jackstraw/jackstraw.pdf
- CCA tests (two groups needed): http://cran.r-project.org/web/packages/CCP/CCP.pdf
- Example of application: http://alstatr.blogspot.com.es/2015/01/canonical-correlation-analysis-on.html
- Articles
variani/cpca documentation built on May 3, 2019, 4:34 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Papers
About
This documents contains a temporal list of references, that were encountered ocasionally and saved for further consideration.
R packages
- multigroup package: This package includes several methods
to study multigroup data, where the same set of variables are measured on different groups of individuals.
- See function
FCPCA(that means Flury's Common Principal Component Analysis), where FG algorithm proposed by Flury is implemented.
- See function
- MetaPCA Simultaneous dimension reduction using PCA when multiple studies are combined. ; available on CRAN
- eigenvalue maximization approach and angle minimization approach
- extension for Robust PCA and Sparse PCA in the meta-analysis realm
- Dongwan Don Kang Google Scholar
Multivariate Methods
- Zhang et al., Feature Transformation with Class Conditional Decorrelation, 2013 (pdf): Fisher DA = Whitenning + PCA. Implementation of Whitenning was proposed to be Class Conditional Correlation (CCD). CCD takes a list of class-related covariance matrices and diagonalizes them simultaneously.
Tests
- Rublik, A TEST OF THE HYPOTHESIS OF PARTIAL COMMON PRINCIPAL COMPONENTS, 2009 (pdf): See Introduction for literature review.
- Boente et al., Robust tests for the common principal components model, 2009 (pdf): See Section 7 Final Comments for a good summary.
- Diego et al., (2014) GAW19 contribution.
PCA tests
- Duscussion: http://stats.stackexchange.com/questions/33917/how-to-determine-significant-principal-components-using-bootstrapping-or-monte-c
- R package for permutation tests: http://cran.r-project.org/web/packages/jackstraw/jackstraw.pdf
- CCA tests (two groups needed): http://cran.r-project.org/web/packages/CCP/CCP.pdf
- Example of application: http://alstatr.blogspot.com.es/2015/01/canonical-correlation-analysis-on.html
- Articles
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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