iSFun (version 1.1.0)

spls: Sparse partial least squares

Description

This function provides penalty-based sparse partial least squares analysis for single dataset with high dimensions., which aims to have the direction of the first loading.

Usage

spls(x, y, mu1, eps = 1e-04, kappa = 0.05, scale.x = TRUE,
  scale.y = TRUE, maxstep = 50, trace = FALSE)

Arguments

x

matrix of explanatory variables.

y

matrix of dependent variables.

mu1

numeric, sparsity penalty parameter.

eps

numeric, the threshold at which the algorithm terminates.

kappa

numeric, 0 < kappa < 0.5 and the parameter reduces the effect of the concave part of objective function.

scale.x

character, "TRUE" or "FALSE", whether or not to scale the variables x. The default is TRUE.

scale.y

character, "TRUE" or "FALSE", whether or not to scale the variables y. The default is TRUE.

maxstep

numeric, maximum iteration steps. The default value is 50.

trace

character, "TRUE" or "FALSE". If TRUE, prints out its screening results of variables.

Value

An 'spls' object that contains the list of the following items.

• x: data matrix of explanatory variables with centered columns. If scale.x is TRUE, the columns of data matrix are standardized to have mean 0 and standard deviation 1.

• y: data matrix of dependent variables with centered columns. If scale.y is TRUE, the columns of data matrix are standardized to have mean 0 and standard deviation 1.

• betahat: the estimated regression coefficients.

• loading: the estimated first direction vector.

• variable: the screening results of variables.

• meanx: column mean of the original dataset x.

• normx: column standard deviation of the original dataset x.

• meany: column mean of the original dataset y.

• normy: column standard deviation of the original dataset y.

See Also

See Also as ispls, meta.spls.

Examples

# NOT RUN {
library(iSFun)
data("simData.pls")
x.spls <- do.call(rbind, simData.pls$x)
y.spls <- do.call(rbind, simData.pls$y)
res_spls <- spls(x = x.spls, y = y.spls, mu1 = 0.05, eps = 1e-3, kappa = 0.05,
                 scale.x = TRUE, scale.y = TRUE, maxstep = 50, trace = FALSE)
# }