spls (version 2.2-1)

sgpls: 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".

Value

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

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.

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