R: mixOmics wrapper for Sparse Generalised Canonical Correlation...

wrapper.sgcca {mixOmics} | R Documentation

## mixOmics wrapper for Sparse Generalised Canonical Correlation Analysis (sgcca)

### Description

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

### Usage

    wrapper.sgcca(X,
    design = 1 - diag(length(X)),
    penalty = NULL,
    ncomp = 1,
    keepX,
    scheme = "horst",
    mode="canonical",
    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`.
`penalty` | numeric vector of length the number of blocks in `X`. Each penalty parameter will be applied on each block and takes the value between 0 (no variable selected) and 1 (all variables included).
`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").
`mode` | character string. What type of algorithm to use, (partially) matching one of `"regression"`, `"canonical"`, `"invariant"` or `"classic"`. See Details.
`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 sGCCA (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.

Note that this function is the same as `block.spls` with different default arguments.

More details about the PLS modes in `?pls`.

### Value

`wrapper.sgcca` returns an object of class `"sgcca"`, 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.
`penalty` | the input penalty 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.

Tenenhaus A., Phillipe C., Guillemot, V., Lê Cao K-A., Grill J., Frouin, V. Variable Selection For Generalized Canonical Correlation Analysis. 2013. (in revision)

### See Also

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

### Examples

    ## Not run:
    data(nutrimouse)
    # need to unmap the Y factor diet if you pretend this is not a classification pb.
    # see also the function block.splsda for discriminant analysis  where you dont
    # need to unmap Y.
    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 penalty parameters will need to be tuned
    wrap.result.sgcca = wrapper.sgcca(X = data, design = design, penalty = c(.3,.5, 1),
                                      ncomp = 2,
                                      scheme = "centroid")
    wrap.result.sgcca
    #did the algo converge?
    wrap.result.sgcca$crit  # yes

    ## End(Not run)

[Package mixOmics version 6.3.2 Index]