R: Summary Methods for CCA and PLS objects

summary {mixOmics} | R Documentation

## Summary Methods for CCA and PLS objects

### Description

Produce `summary` methods for class `"rcc"`, `"pls"` and `"spls"`.

### Usage

    ## S3 method for class 'rcc'
    summary(object, what = c("all", "communalities", "redundancy"),
            cutoff = NULL, digits = 4, ...)

    ## S3 method for class 'pls'
    summary(object, what = c("all", "communalities", "redundancy",
            "VIP"), digits = 4, keep.var = FALSE, ...)

    ## S3 method for class 'spls'
    summary(object, what = c("all", "communalities", "redundancy",
            "VIP"), digits = 4, keep.var = FALSE, ...)

### Arguments

`object` | object of class inheriting from `"rcc"`, `"pls"` or `"spls"`.
`cutoff` | real between 0 and 1. Variables with all correlations components below this cutoff in absolute value are not showed (see Details).
`digits` | integer, the number of significant digits to use when printing. Defaults to `4`.
`what` | character string or vector. Should be a subset of `c("all"`, `"summarised"`, `"communalities"`, `"redundancy"`, `"VIP"`). `"VIP"` is only available for (s)PLS. See Details.
`keep.var` | boolean. If `TRUE` only the variables with loadings not zero (as selected by `spls`) are showed. Defaults to `FALSE`.
`...` | not used currently.

### Details

The information in the `rcc`, `pls` or `spls` object is summarised, it includes: the dimensions of `X` and `Y` data, the number of variates considered, the canonical correlations (if `object` of class `"rcc"`) and the (s)PLS algorithm used (if `object` of class `"pls"` or `"spls"`) and the number of variables selected on each of the sPLS components (if `x` of class `"spls"`).

`"communalities"` in `what` gives Communalities Analysis. `"redundancy"` display Redundancy Analysis. `"VIP"` gives the Variable Importance in the Projection (VIP) coefficients fit by `pls` or `spls`. If `what` is `"all"`, all are given.

For class `"rcc"`, when a value to `cutoff` is specified, the correlations between each variable and the equiangular vector between X- and Y-variates are computed. Variables with at least one correlation componente bigger than `cutoff` are showed. The defaults is `cutoff=NULL` all the variables are given.

### Value

The function `summary` returns a list with components:

`ncomp` | the number of components in the model.
`cor` | the canonical correlations.
`cutoff` | the cutoff used.
`keep.var` | list containing the name of the variables selected.
`mode` | the algoritm used in `pls` or `spls`.
`Cm` | list containing the communalities.
`Rd` | list containing the redundancy.
`VIP` | matrix of VIP coefficients.
`what` | subset of `c("all"`, `"communalities"`, `"redundancy"`, `"VIP"`).
`digits` | the number of significant digits to use when printing.
`method` | method used: `rcc`, `pls` or `spls`.

### Author(s)

Sébastien Déjean, Ignacio González and Kim-Anh Lê Cao.

### See Also

`rcc`, `pls`, `spls`, `vip`.

### Examples

    ## summary for objects of class 'rcc'
    data(nutrimouse)
    X <- nutrimouse$lipid
    Y <- nutrimouse$gene
    nutri.res <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)
    more <- summary(nutri.res, cutoff = 0.65)

    ## summary for objects of class 'pls'
    data(linnerud)
    X <- linnerud$exercise
    Y <- linnerud$physiological
    linn.pls <- pls(X, Y)
    more <- summary(linn.pls)

    ## summary for objects of class 'spls'
    data(liver.toxicity)
    X <- liver.toxicity$gene
    Y <- liver.toxicity$clinic
    toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50),
                          keepY = c(10, 10, 10))
    more <- summary(toxicity.spls, what = "redundancy", keep.var = TRUE)

[Package mixOmics version 6.3.2 Index]