sgpls: Fit SGPLS classification models
In spls: Sparse Partial Least Squares (SPLS) Regression and Classification

Description Usage Arguments Details Value Author(s) References See Also Examples

Fit a SGPLS classification model.

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sgpls( x, y, K, eta, scale.x=TRUE,
        eps=1e-5, denom.eps=1e-20, zero.eps=1e-5, maxstep=100,
        br=TRUE, ftype='iden' )

`x`	Matrix of predictors.
`y`	Vector of class indices.
`K`	Number of hidden components.
`eta`	Thresholding parameter. `eta` should be between 0 and 1.
`scale.x`	Scale predictors by dividing each predictor variable by its sample standard deviation?
`eps`	An effective zero for change in estimates. Default is 1e-5.
`denom.eps`	An effective zero for denominators. Default is 1e-20.
`zero.eps`	An effective zero for success probabilities. Default is 1e-5.
`maxstep`	Maximum number of Newton-Raphson iterations. Default is 100.
`br`	Apply Firth's bias reduction procedure?
`ftype`	Type of Firth's bias reduction procedure. Alternatives are `"iden"` (the approximated version) or `"hat"` (the original version). Default is `"iden"`.

The SGPLS method is described in detail in Chung and Keles (2010). SGPLS provides PLS-based classification with variable selection, by incorporating sparse partial least squares (SPLS) proposed in Chun and Keles (2010) into a generalized linear model (GLM) framework. y is assumed to have numerical values, 0, 1, ..., G, where G is the number of classes subtracted by one.

A sgpls object is returned. print, predict, coef methods use this object.

Dongjun Chung and Sunduz Keles.

Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", Statistical Applications in Genetics and Molecular Biology, Vol. 9, Article 17.

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.

print.sgpls, predict.sgpls, and coef.sgpls.

    data(prostate)

    # SGPLS with eta=0.6 & 3 hidden components
    (f <- sgpls(prostate$x, prostate$y, K=3, eta=0.6, scale.x=FALSE))

    # Print out coefficients
    coef.f <- coef(f)
    coef.f[coef.f!=0, ]