CCA: An R Package to Extend Canonical Correlation Analysis. Canonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/), to develop numerical and graphical outputs and to enable the user to handle missing values. The CCA package also includes a regularized version of CCA to deal with data sets with more variables than units. Illustrations are given through the analysis of a data set coming from a nutrigenomic study in the mouse.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Fieller, Nick: Basics of matrix algebra for statistics with R (2015)
- Yuan, Yun-Hao; Sun, Quan-Sen; Ge, Hong-Wei: Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition (2014)
- Lykou, Anastasia; Whittaker, Joe: Sparse CCA using a lasso with positivity constraints (2010)
- Cao, Kim-Anh L^e; Rossouw, Debra; Robert-Granié, Christèle; Besse, Philippe: A sparse PLS for variable selection when integrating omics data (2008)