spls: cv.spls – R documentation

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cv.spls

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.

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)

spls

Sparse Partial Least Squares (SPLS) Regression and Classification

v2.2-3

GPL (>= 2)

Authors

Dongjun Chung <chungdon@stat.wisc.edu>, Hyonho Chun <chun@stat.wisc.edu>, Sunduz Keles <keles@stat.wisc.edu>

Initial release

2019-05-04