R: Class "lssvm"

lssvm-class {kernlab} | R Documentation

## Class "lssvm"

### Description

The Gaussian Processes object

### Objects from the Class

Objects can be created by calls of the form `new("lssvm", ...)`. or by calling the `lssvm` function

### Slots

`kernelf`:

  Object of class `"kfunction"` contains the kernel function used

`kpar`:

  Object of class `"list"` contains the kernel parameter used

`param`:

  Object of class `"list"` contains the regularization parameter used.

`kcall`:

  Object of class `"call"` contains the used function call

`type`:

  Object of class `"character"` contains type of problem

`coef`:

  Object of class `"ANY"` contains the model parameter

`terms`:

  Object of class `"ANY"` contains the terms representation of the symbolic model used (when using a formula)

`xmatrix`:

  Object of class `"matrix"` containing the data matrix used

`ymatrix`:

  Object of class `"output"` containing the response matrix

`fitted`:

  Object of class `"output"` containing the fitted values

`b`:

  Object of class `"numeric"` containing the offset

`lev`:

  Object of class `"vector"` containing the levels of the response (in case of classification)

`scaling`:

  Object of class `"ANY"` containing the scaling information performed on the data

`nclass`:

  Object of class `"numeric"` containing the number of classes (in case of classification)

`alpha`:

  Object of class `"listI"` containing the computes alpha values

`alphaindex`

  Object of class `"list"` containing the indexes for the alphas in various classes (in multi-class problems).

`error`:

  Object of class `"numeric"` containing the training error

`cross`:

  Object of class `"numeric"` containing the cross validation error

`n.action`:

  Object of class `"ANY"` containing the action performed in NA

`nSV`:

  Object of class `"numeric"` containing the number of model parameters

### Methods

alpha

  `signature(object = "lssvm")`: returns the alpha vector

cross

  `signature(object = "lssvm")`: returns the cross validation error

error

  `signature(object = "lssvm")`: returns the training error

fitted

  `signature(object = "vm")`: returns the fitted values

kcall

  `signature(object = "lssvm")`: returns the call performed

kernelf

  `signature(object = "lssvm")`: returns the kernel function used

kpar

  `signature(object = "lssvm")`: returns the kernel parameter used

param

  `signature(object = "lssvm")`: returns the regularization parameter used

lev

  `signature(object = "lssvm")`: returns the response levels (in classification)

type

  `signature(object = "lssvm")`: returns the type of problem

scaling

  `signature(object = "ksvm")`: returns the scaling values

xmatrix

  `signature(object = "lssvm")`: returns the data matrix used

ymatrix

  `signature(object = "lssvm")`: returns the response matrix used

### Author(s)

Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at

### See Also

`lssvm`, `ksvm-class`

### Examples

    # train model
    data(iris)
    test <- lssvm(Species~.,data=iris,var=2)
    test
    alpha(test)
    error(test)
    lev(test)

[Package kernlab version 0.9-25 Index]