# epspath: Fit the entire 'epsilon' path for Support Vector Regression In svrpath: The SVR Path Algorithm

## 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

 ```1 2``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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.