R: mixOmics wrapper for Regularised Generalised Canonical...

wrapper.rgcca {mixOmics} | R Documentation

## mixOmics wrapper for Regularised Generalised Canonical Correlation Analysis (rgcca)

### Description

Wrapper function to perform Regularized Generalised Canonical Correlation Analysis (rGCCA), a generalised approach for the integration of multiple datasets. For more details, see the `help(rgcca)` from the RGCCA package.

### Usage

    wrapper.rgcca(X,
        design = 1 - diag(length(X)),
        tau = rep(1, length(X)),
        ncomp = 1,
        keepX,
        scheme = "horst",
        scale = TRUE,
        init = "svd.single",
        tol = .Machine$double.eps,
        max.iter=1000,
        near.zero.var = FALSE,
        all.outputs = TRUE)

### Arguments

`X` | a list of data sets (called 'blocks') matching on the same samples. Data in the list should be arranged in samples x variables. `NA`s are not allowed.
`design` | numeric matrix of size (number of blocks in X) x (number of blocks in X) with values between 0 and 1. Each value indicates the strenght of the relationship to be modelled between two blocks using sGCCA; a value of 0 indicates no relationship, 1 is the maximum value. If `Y` is provided instead of `indY`, the `design` matrix is changed to include relationships to `Y`.
`tau` | numeric vector of length the number of blocks in `X`. Each regularization parameter will be applied on each block and takes the value between 0 (no regularisation) and 1. If tau = "optimal" the shrinkage paramaters are estimated for each block and each dimension using the Schafer and Strimmer (2005) analytical formula.
`ncomp` | the number of components to include in the model. Default to 1.
`keepX` | A vector of same length as X. Each entry keepX[i] is the number of X[[i]]-variables kept in the model.
`scheme` | Either "horst", "factorial" or "centroid" (Default: "horst").
`scale` | boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE)
`init` | Mode of initialization use in the algorithm, either by Singular Value Decompostion of the product of each block of X with Y ("svd") or each block independently ("svd.single") . Default to "svd.single".
`tol` | Convergence stopping value.
`max.iter` | integer, the maximum number of iterations.
`near.zero.var` | boolean, see the internal `nearZeroVar` function (should be set to TRUE in particular for data with many zero values). Setting this argument to FALSE (when appropriate) will speed up the computations. Default value is FALSE
`all.outputs` | boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = `TRUE`.

### Details

This wrapper function performs rGCCA (see RGCCA) with 1, … ,`ncomp` components on each block data set. A supervised or unsupervised model can be run. For a supervised model, the `unmap` function should be used as an input data set. More details can be found on the package RGCCA.

### Value

`wrapper.rgcca` returns an object of class `"rgcca"`, a list that contains the following components:

`data` | the input data set (as a list).
`design` | the input design.
`variates` | the sgcca components.
`loadings` | the loadings for each block data set (outer wieght vector).
`loadings.star` | the laodings, standardised.
`tau` | the input tau parameter.
`scheme` | the input schme.
`ncomp` | the number of components included in the model for each block.
`crit` | the convergence criterion.
`AVE` | Indicators of model quality based on the Average Variance Explained (AVE): AVE(for one block), AVE(outer model), AVE(inner model)..
`names` | list containing the names to be used for individuals and variables.

More details can be found in the references.

### Author(s)

Arthur Tenenhaus, Vincent Guillemot and Kim-Anh Lê Cao.

### References

Tenenhaus A. and Tenenhaus M., (2011), Regularized Generalized Canonical Correlation Analysis, Psychometrika, Vol. 76, Nr 2, pp 257-284.

Schafer J. and Strimmer K., (2005), A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statist. Appl. Genet. Mol. Biol. 4:32.

### See Also

`wrapper.rgcca`, `plotIndiv`, `plotVar`, `wrapper.sgcca` and http://www.mixOmics.org for more details.

### Examples

    data(nutrimouse)
    # need to unmap the Y factor diet
    Y = unmap(nutrimouse$diet)
    data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
    # with this design, gene expression and lipids are connected to the diet factor
    # design = matrix(c(0,0,1,
    #                   0,0,1,
    #                   1,1,0), ncol = 3, nrow = 3, byrow = TRUE)

    # with this design, gene expression and lipids are connected to the diet factor
    # and gene expression and lipids are also connected
    design = matrix(c(0,1,1,
                      1,0,1,
                      1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
    #note: the tau parameter is the regularization parameter
    wrap.result.rgcca = wrapper.rgcca(X = data, design = design, tau = c(1, 1, 0),
                                      ncomp = 2,
                                      scheme = "centroid")
    #wrap.result.rgcca

[Package mixOmics version 6.3.2 Index]