R: Class "ksvm"

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 calculated from the data matrix used during computations (possibly scaled and without 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 classification)

`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 support vectors

`obj`:

  Object of class `vector` containing the value of the objective function. When using one-against-one in multiclass classification this is a vector.

`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

obj

  `signature(object = "ksvm")`: returns the value of the objective function

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

param

  `signature(object="ksvm")`: returns the parameters of the SVM in a list (C, epsilon, nu etc.)

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 0.9-25 Index]