R: Estimate the parameters of regularization for Regularized CCA

tune.rcc {mixOmics} | R Documentation

## Estimate the parameters of regularization for Regularized CCA

### Description

Computes leave-one-out or M-fold cross-validation scores on a two-dimensional grid to determine optimal values for the parameters of regularization in `rcc`.

### Usage

    tune.rcc(X, Y, grid1 = seq(0.001, 1, length = 5),
                grid2 = seq(0.001, 1, length = 5),
                validation = c("loo", "Mfold"),
                folds = 10, plot = TRUE)

### 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.
`grid1, grid2` | vector numeric defining the values of `lambda1` and `lambda2` at which cross-validation score should be computed. Defaults to `grid1=grid2=seq(0.001, 1, length=5)`.
`validation` | character string. What kind of (internal) cross-validation method to use, (partially) matching one of `"loo"` (leave-one-out) or `"Mfolds"` (M-folds). See Details.
`folds` | positive integer. Number of folds to use if `validation="Mfold"`. Defaults to `folds=10`.
`plot` | logical argument indicating whether a image map should be plotted by calling the `imgCV` function.

### Details

If `validation="Mfolds"`, M-fold cross-validation is performed by calling `Mfold`. When `folds` is given, the elements of `folds` should be integer vectors specifying the indices of the validation sample and the argument `M` is ignored. Otherwise, the folds are generated. The number of cross-validation folds is specified with the argument `M`.

If `validation="loo"`, leave-one-out cross-validation is performed by calling the `loo` function. In this case the arguments `folds` and `M` are ignored.

The estimation of the missing values can be performed by the reconstitution of the data matrix using the `nipals` function. Otherwise, missing values are handled by casewise deletion in the `rcc` function.

### Value

The returned value is a list with components:

`opt.lambda1,` |
`opt.lambda2` | value of the parameters of regularization on which the cross-validation method reached it optimal.
`opt.score` | the optimal cross-validation score reached on the grid.
`grid1, grid2` | original vectors `grid1` and `grid2`.
`mat` | matrix containing the cross-validation score computed on the grid.

### Author(s)

Sébastien Déjean and Ignacio González.

### See Also

`image.tune.rcc` and http://www.mixOmics.org for more details.

### Examples

    data(nutrimouse)
    X <- nutrimouse$lipid
    Y <- nutrimouse$gene

    ## this can take some seconds
    ## Not run:
    tune.rcc(X, Y, validation = "Mfold")

    ## End(Not run)

[Package mixOmics version 6.3.2 Index]