ksvm-class function

Class "ksvm"

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

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)