rcc {mixOmics} | R Documentation

## Regularized Canonical Correlation Analysis

### Description

The function performs the regularized extension of the Canonical Correlation Analysis to seek correlations between two data matrices.

### Usage

    rcc(
      X,
      Y,
      ncomp = 2,
      method = c("ridge", "shrinkage"),
      lambda1 = 0,
      lambda2 = 0
    )

### Arguments

`X` | numeric matrix or data frame (n \times p), the observations on the X variables. `NA`s are allowed.
`Y` | numeric matrix or data frame (n \times q), the observations on the Y variables. `NA`s are allowed.
`ncomp` | the number of components to include in the model. Default to 2.
`method` | One of "ridge" or "shrinkage". If "ridge", `lambda1` and `lambda2` need to be supplied (see also our function tune.rcc); if "shrinkage", parameters are directly estimated with Strimmer's formula, see below and reference.
`lambda1, lambda2` | a non-negative real. The regularization parameter for the X and Y data. Defaults to `lambda1=lambda2=0`. Only used if `method="ridge"`

### Details

The main purpose of Canonical Correlations Analysis (CCA) is the exploration of sample correlations between two sets of variables X and Y observed on the same individuals (experimental units) whose roles in the analysis are strictly symmetric.

The `cancor` function performs the core of computations but additional tools are required to deal with data sets highly correlated (nearly collinear), data sets with more variables than units by example.

The `rcc` function, the regularized version of CCA, is one way to deal with this problem by including a regularization step in the computations of CCA. Such a regularization in this context was first proposed by Vinod (1976), then developped by Leurgans et al. (1993). It consists in the regularization of the empirical covariances matrices of X and Y by adding a multiple of the matrix identity, that is, Cov(X)+ λ_1 I and Cov(Y)+ λ_2 I.

When `lambda1=0` and `lambda2=0`, `rcc` performs a classical CCA, if possible (i.e. when n > p+q.

The shrinkage estimates `method = "shrinkage"` can be used to bypass `tune.rcc` to choose the shrinkage parameters - which can be long and costly to compute with very large data sets. Note that both functions `tune.rcc` (which uses cross-validation) and the shrinkage parameters (which uses the formula from Schafer and Strimmer, see the corpcor package `estimate.lambda` ) may output different results.

Note: when `method = "shrinkage"` the parameters are estimated using `estimate.lambda` from the corpcor package. Data are then centered to calculate the regularised variance-covariance matrices in `rcc`.

Missing values are handled in the function, except when using `method = "shrinkage"`. In that case the estimation of the missing values can be performed by the reconstitution of the data matrix using the `nipals` function.

### Value

`rcc` returns a object of class `"rcc"`, a list that contains the following components:

`X` | the original X data.
`Y` | the original Y data.
`cor` | a vector containing the canonical correlations.
`lambda` | a vector containing the regularization parameters whether those were input if ridge method or directly estimated with the shrinkage method.
`loadings` | list containing the estimated coefficients used to calculate the canonical variates in X and Y.
`variates` | list containing the canonical variates.
`names` | list containing the names to be used for individuals and variables.
`prop_expl_var` | Proportion of the explained variance of derived components, after setting possible missing values to zero.

### Author(s)

Sébastien Déjean, Ignacio González, Francois Bartolo, Kim-Anh Lê Cao, Florian Rohart, Al J Abadi

### References

González, I., Déjean, S., Martin, P. G., and Baccini, A. (2008). CCA: An R package to extend canonical correlation analysis. Journal of Statistical Software, 23(12), 1-14.

González, I., Déjean, S., Martin, P., Goncalves, O., Besse, P., and Baccini, A. (2009). Highlighting relationships between heterogeneous biological data through graphical displays based on regularized canonical correlation analysis. Journal of Biological Systems, 17(02), 173-199.

Leurgans, S. E., Moyeed, R. A. and Silverman, B. W. (1993). Canonical correlation analysis when the data are curves. Journal of the Royal Statistical Society. Series B 55, 725-740.

Vinod, H. D. (1976). Canonical ridge and econometrics of joint production. Journal of Econometrics 6, 129-137.

Opgen-Rhein, R., and K. Strimmer. 2007. Accurate ranking of differentially expressed genes by a distribution-free shrinkage approach. Statist. emphAppl. Genet. Mol. Biol. 6:9. (http://www.bepress.com/sagmb/vol6/iss1/art9/)

Sch"afer, J., and K. Strimmer. 2005. A shrinkage approach to large-scale covariance estimation and implications for functional genomics. Statist. emphAppl. Genet. Mol. Biol. 4:32. (http://www.bepress.com/sagmb/vol4/iss1/art32/)

### See Also

`summary`, `tune.rcc`, `plot.rcc`, `plotIndiv`, `plotVar`, `cim`, `network` and http://www.mixOmics.org for more details.

### Examples

    ## Classic CCA
    data(linnerud)
    X <- linnerud$exercise
    Y <- linnerud$physiological
    linn.res <- rcc(X, Y)

    ## Not run:
    ## Regularized CCA
    data(nutrimouse)
    X <- nutrimouse$lipid
    Y <- nutrimouse$gene
    nutri.res1 <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)

    ## using shrinkage parameters
    nutri.res2 <- rcc(X, Y, ncomp = 3, method = 'shrinkage')
    nutri.res2$lambda # the shrinkage parameters

    ## End(Not run)

[Package mixOmics version 6.14.1 Index]