spls {spls}R Documentation

Fit SPLS regression models

Description

Fit a SPLS regression.

Usage

spls( x, y, K, eta, kappa=0.5, select="pls2", fit="simpls",
    scale.x=TRUE, scale.y=FALSE, eps=1e-4, maxstep=100, trace=FALSE)

Arguments

x Matrix of predictors.
y Vector or matrix of responses.
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 the original and surrogate direction vectors. kappa is relevant only when the 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 the predictors by dividing each predictor variable by its sample standard deviation?
scale.y Scale the responses by dividing each response variable by its sample standard deviation?
eps An effective zero. Default is 1e-4.
maxstep Maximum number of iterations when fitting direction vectors. Default is 100.
trace Print out the progress of the variable selection?

Details

The SPLS method is described in detail in Chun and Keles (2007). SPLS directly imposes sparsity on the dimension reduction step of PLS in order to achieve accurate prediction and variable selection simultaneously. The option select refers to the PLS algorithm for variable selection. The option fit refers to the PLS algorithm for model fitting and spls utilizes the algorithms offered by the pls package for this purpose. See the help files of the function plsr in the pls package for more detail. The user should install the pls package before using spls functions. The choices for select and fit are independent.

Value

A spls object is returned. print, plot, predict, coef, ci.spls, coefplot.spls methods use this object.

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun, H. and Keles, S. (2007). "Sparse partial least squares for simultaneous dimension reduction and variable selection", (http://www.stat.wisc.edu/~keles/Papers/SPLS_Nov07.pdf).

See Also

print.spls, plot.spls, predict.spls, coef.spls, ci.spls, and coefplot.spls.

Examples

data(yeast)
# SPLS with eta=0.7 & 8 hidden components
f <- spls( yeast$x, yeast$y, K=8, eta=0.7 )
print(f)
# Print out coefficients
coef.f <- coef(f)
coef.f[,1]
# Coefficient path plot
plot( f, yvar=1 )
x11()
# Coefficient plot of the selected variables
coefplot.spls( f, xvar=c(1:4) )

[Package spls version 1.0-3 Index]