R: Compute and plot cross-validated mean squared prediction...

cv.spls {spls} | R Documentation

## Compute and plot cross-validated mean squared prediction error for SPLS regression

### Description

Draw heatmap of v-fold cross-validated mean squared prediction error and return optimal eta (thresholding parameter) and K (number of hidden components).

### Usage

    cv.spls( x, y, fold=10, K, eta, kappa=0.5,
            select="pls2", fit="simpls",
            scale.x=TRUE, scale.y=FALSE, plot.it=TRUE )

### Arguments

`x` | Matrix of predictors.
`y` | Vector or matrix of responses.
`fold` | Number of cross-validation folds. Default is 10-folds.
`K` | Number of hidden components.
`eta` | Thresholding parameter. `eta` should be between 0 and 1.
`kappa` | Parameter to control the effect of the concavity of the objective function and the closeness of original and surrogate direction vectors. `kappa` is relevant only when responses are multivariate. `kappa` should be between 0 and 0.5. Default is 0.5.
`select` | PLS algorithm for variable selection. Alternatives are `"pls2"` or `"simpls"`. Default is `"pls2"`.
`fit` | PLS algorithm for model fitting. Alternatives are `"kernelpls"`, `"widekernelpls"`, `"simpls"`, or `"oscorespls"`. Default is `"simpls"`.
`scale.x` | Scale predictors by dividing each predictor variable by its sample standard deviation?
`scale.y` | Scale responses by dividing each response variable by its sample standard deviation?
`plot.it` | Draw heatmap of cross-validated mean squared prediction error?

### Value

Invisibly returns a list with components:

`mspemat` | Matrix of cross-validated mean squared prediction error. Rows correspond to `eta` and columns correspond to the number of components (`K`).
`eta.opt` | Optimal `eta`.
`K.opt` | Optimal `K`.

### Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

### References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

### See Also

`print.spls`, `plot.spls`, `predict.spls`, and `coef.spls`.

### Examples

        data(yeast)
        set.seed(1)

        # MSPE plot. eta is searched between 0.1 and 0.9 and
        # number of hidden components is searched between 1 and 10

        ## Not run:
            cv <- cv.spls(yeast$x, yeast$y, K = c(1:10), eta = seq(0.1,0.9,0.1))

            # Optimal eta and K
            cv$eta.opt
            cv$K.opt
            (spls(yeast$x, yeast$y, eta=cv$eta.opt, K=cv$K.opt))

    ## End(Not run)

[Package spls version 2.2-2 Index]