spls (version 2.2-3)

spls: Fit SPLS regression models

Description

Fit a SPLS regression model.

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 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?

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 variable selection?

Value

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

Details

The SPLS method is described in detail in Chun and Keles (2010). 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 algorithms offered by the pls package for this purpose. See help files of the function plsr in the pls package for more details. The user should install the pls package before using spls functions. The choices for select and fit are independent.

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.

See Also

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

Examples

# NOT RUN {
    data(yeast)
    # SPLS with eta=0.7 & 8 hidden components
    (f <- spls(yeast$x, yeast$y, K=8, eta=0.7))

    # Print out coefficients
    coef.f <- coef(f)
    coef.f[,1]

    # Coefficient path plot
    plot(f, yvar=1)
    dev.new()

    # Coefficient plot of selected variables
    coefplot.spls(f, xvar=c(1:4))
# }