ksvm-class {kernlab} | R Documentation

## Class "ksvm"

### Description

An S4 class containing the output (model) of the `ksvm` Support Vector Machines function

### Objects from the Class

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

### Slots

`type`:
  Object of class `"character"` containing the support vector machine type ("C-svc", "nu-svc", "C-bsvc", "spoc-svc", "one-svc", "eps-svr", "nu-svr", "eps-bsvr")
`param`:
  Object of class `"list"` containing the Support Vector Machine parameters (C, nu, epsilon)
`kernelf`:
  Object of class `"function"` containing the kernel function
`kpar`:
  Object of class `"list"` containing the kernel function parameters (hyperparameters)
`kcall`:
  Object of class `"ANY"` containing the `ksvm` function call
`scaling`:
  Object of class `"ANY"` containing the scaling information performed on the data
`terms`:
  Object of class `"ANY"` containing the terms representation of the symbolic model used (when using a formula)
`xmatrix`:
  Object of class `"input"` (`"list"` for multiclass problems or `"matrix"` for binary classification and regression problems) containing the support vectors caclulated from the data matrix used during computations (possibly scaled and whithout NA). In the case of multi-class classification each list entry contains the support vectors from each binary classification problem from the one-against-one method.
`ymatrix`:
  Object of class `"output"` the response `"matrix"` or `"factor"` or `"vector"` or `"logical"`
`fitted`:
  Object of class `"output"` with the fitted values, predictions using the training set.
`lev`:
  Object of class `"vector"` with the levels of the response (in the case of classifiaction)
`prob.model`:
  Object of class `"list"` with the class prob. model
`prior`:
  Object of class `"list"` with the prior of the training set
`nclass`:
  Object of class `"numeric"` containing the number of classes (in the case of classification)
`alpha`:
  Object of class `"listI"` containing the resulting alpha vector (`"list"` or `"matrix"` in case of multiclass classification) (support vectors)
`coef`:
  Object of class `"ANY"` containing the resulting coefficients
`alphaindex`:
  Object of class `"list"` containing
`b`:
  Object of class `"numeric"` containing the resulting offset
`SVindex`:
  Object of class `"vector"` containing the indexes of the support vectors
`nSV`:
  Object of class `"numeric"` containing the number of suppport vector machines
`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 for NA

### Methods

SVindex
  `signature(object = "ksvm")`: return the indexes of support vectors
alpha
  `signature(object = "ksvm")`: returns the complete 5 alpha vector (wit zero values)
alphaindex
  `signature(object = "ksvm")`: returns the indexes of non-zero alphas (support vectors)
cross
  `signature(object = "ksvm")`: returns the cross-validation error
error
  `signature(object = "ksvm")`: returns the training error
fitted
  `signature(object = "vm")`: returns the fitted values (predict on training set)
kernelf
  `signature(object = "ksvm")`: returns the kernel function
kpar
  `signature(object = "ksvm")`: returns the kernel parameters (hyperparameters)
lev
  `signature(object = "ksvm")`: returns the levels in case of classification
prob.model
  `signature(object="ksvm")`: returns class prob. model values
prior
  `signature(object="ksvm")`: returns the prior of the training set
kcall
  `signature(object="ksvm")`: returns the `ksvm` function call
scaling
  `signature(object = "ksvm")`: returns the scaling values
show
  `signature(object = "ksvm")`: prints the object information
type
  `signature(object = "ksvm")`: returns the problem type
xmatrix
  `signature(object = "ksvm")`: returns the data matrix used
ymatrix
  `signature(object = "ksvm")`: returns the response vector

### Author(s)

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

### See Also

`ksvm`, `rvm-class`, `gausspr-class`

### Examples

    ## simple example using the promotergene data set
    data(promotergene)

    ## train a support vector machine
    gene <- ksvm(Class~.,data=promotergene,kernel="rbfdot",kpar=list(sigma=0.015),C=50,cross=4)
    gene

    # the kernel  function
    kernelf(gene)
    # the alpha values
    alpha(gene)
    # the coefficients
    coef(gene)
    # the fitted values
    fitted(gene)
    # the cross validation error
    cross(gene)

[Package kernlab version 0.8-1 Index]