R: Fit SGPLS classification models

sgpls {spls} | R Documentation

## Fit SGPLS classification models

### Description

Fit a SGPLS classification model.

### Usage

    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' )

### Arguments

`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"`.

### Details

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.

### Value

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

### Author(s)

Dongjun Chung and Sunduz Keles.

### References

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.

### See Also

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

### Examples

        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, ]

[Package spls version 2.2-2 Index]