svrpath {svrpath}

Fit the entire regularization path for Support Vector Regression

Description

This algorithm computes the entire regularization path for the support vector regression with a relatively low cost compared to quadratic programming problem.

Usage

svrpath(x, y, svr.eps = 1, kernel.function = radial.kernel,
  param.kernel = 1, ridge = 1e-08, eps = 1e-08, lambda.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

svr.eps

An epsilon in epsilon-insensitive loss function

kernel.function

This is a user-defined function. Provided are poly.kernel (the default, with parameter set to default to a linear kernel) and radial.kernel

param.kernel

The parameter(s) for the kernel. For this radial kernel, the parameter is known in the fields as "gamma". For the polynomial kernel, it is the "degree"

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

lambda.min

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

...

Generic compatibility

Value

A 'svrpath' object is returned, for which there are lambda values and corresponding values of theta for each data point.

Author(s)

Do Hyun Kim, Seung Jun Shin

See Also

predict.svrpath, plot.svrpath, epspath

Examples

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
svr.eps <- 1
obj <- svrpath(x, y, svr.eps = svr.eps)