kernlab (version 0.9-24)

lssvm-class: 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

See Also

lssvm, ksvm-class

Examples

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