# tune.rcc: Estimate the parameters of regularization for Regularized CCA In mixOmics: Omics Data Integration Project

## 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

 1 2 3 4 5 6 7 8 9 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. NAs are allowed. Y numeric matrix or data frame (n \times q), the observations on the Y variables. NAs 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, Ignacio González, Kim-Anh Lê Cao, Al J Abadi

 1 2 3 4 5 6 7 data(nutrimouse) X <- nutrimouse$lipid Y <- nutrimouse$gene ## this can take some seconds tune.rcc(X, Y, validation = "Mfold")