epspath: Fit the entire 'epsilon' path for Support Vector Regression

Description Usage Arguments Value Author(s) See Also Examples

View source: R/epspath.R

Description

The Suport Vector Regression (SVR) employs epsilon-intensive loss which ignores errors smaller than epsilon. This algorithm computes the entire paths for SVR solution as a function of epsilon at a given regularization parameter lambda, which we call epsilon path.

Usage

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epspath(x, y, lambda = 1, kernel.function = radial.kernel,
  param.kernel = 1, ridge = 1e-08, eps = 1e-07, eps.min = 1e-08, ...)

Arguments

x

The data matrix (n x p) with n rows (observations) on p variables (columns)

y

The real number valued response variable

lambda

The regularization parameter value.

kernel.function

User defined kernel function. See svmpath.

param.kernel

Parameter(s) of the kernels. See svmpath.

ridge

Sometimes the algorithm encounters singularities; in this case a small value of ridge can help, default is ridge = 1e-8

eps

A small machine number which is used to identify minimal step sizes

eps.min

The smallest value of epsilon for termination of the algorithm. Default is eps.min = 1e-8

...

Generic compatibility

Value

An 'epspath' object is returned.

Author(s)

Do Hyun Kim, Seung Jun Shin

See Also

predict.epspath, plot.epspath, svrpath

Examples

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set.seed(1)
n <- 30
p <- 50

x <- matrix(rnorm(n*p), n, p)
e <- rnorm(n, 0, 1)
beta <- c(1, 1, rep(0, p-2))
y <- x %*% beta + e
lambda <- 1
eobj <- epspath(x, y, lambda = lambda)

svrpath documentation built on May 2, 2019, 9:13 a.m.

Related to epspath in svrpath...