spls {spls} | R Documentation |
Fit a SPLS regression.
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)
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? |
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.
A spls object is returned. print, plot, predict, coef, ci.spls, coefplot.spls methods use this object.
Dongjun Chung, Hyonho Chun, and Sunduz Keles.
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.spls
, plot.spls
, predict.spls
,
coef.spls
, ci.spls
, and coefplot.spls
.
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) )