# spls

0th

Percentile

##### Fit SPLS regression models

Fit a SPLS regression.

Keywords
multivariate, regression
##### Usage
spls( x, y, K, eta, kappa=0.5, select="pls2", fit="simpls",
scale=TRUE, center=TRUE, scale.y=FALSE, eps=1e-4, maxstep=100)
##### 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
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
Scale the predictors by dividing each predictor variable by its sample standard deviation?
center
Center the predictors?
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.
##### 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.

##### 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).

print, plot, predict, coef, ci.spls, coefplot.spls methods for spls.

• 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) )
Documentation reproduced from package spls, version 1.0-0, License: GPL (>= 2)

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