An S4 class containing the output (model) of the
`ksvm`

Support Vector Machines function

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

or by calls to the `ksvm`

function.

`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

- 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

```
# NOT RUN {
## 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)
# }
```

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