Relevance Vector Machine Class

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

.
or by calling the `rvm`

function.

`tol`

:Object of class

`"numeric"`

contains tolerance of termination criteria used.`kernelf`

:Object of class

`"kfunction"`

contains the kernel function used`kpar`

:Object of class

`"list"`

contains the hyperparameter used`kcall`

:Object of class

`"call"`

contains the function call`type`

:Object of class

`"character"`

contains type of problem`terms`

:Object of class

`"ANY"`

containing the terms representation of the symbolic model used (when using a formula interface)`xmatrix`

:Object of class

`"matrix"`

contains the data matrix used during computation`ymatrix`

:Object of class

`"output"`

contains the response matrix`fitted`

:Object of class

`"output"`

with the fitted values, (predict on training set).`lev`

:Object of class

`"vector"`

contains the levels of the response (in classification)`nclass`

:Object of class

`"numeric"`

contains the number of classes (in classification)`alpha`

:Object of class

`"listI"`

containing the the resulting alpha vector`coef`

:Object of class

`"ANY"`

containing the the resulting model parameters`nvar`

:Object of class

`"numeric"`

containing the calculated variance (in case of regression)`mlike`

:Object of class

`"numeric"`

containing the computed maximum likelihood`RVindex`

:Object of class

`"vector"`

containing the indexes of the resulting relevance vectors`nRV`

:Object of class

`"numeric"`

containing the number of relevance vectors`cross`

:Object of class

`"numeric"`

containing the resulting cross validation error`error`

:Object of class

`"numeric"`

containing the training error`n.action`

:Object of class

`"ANY"`

containing the action performed on NA

- RVindex
`signature(object = "rvm")`

: returns the index of the relevance vectors- alpha
`signature(object = "rvm")`

: returns the resulting alpha vector- cross
`signature(object = "rvm")`

: returns the resulting cross validation error- error
`signature(object = "rvm")`

: returns the training error- fitted
`signature(object = "vm")`

: returns the fitted values- kcall
`signature(object = "rvm")`

: returns the function call- kernelf
`signature(object = "rvm")`

: returns the used kernel function- kpar
`signature(object = "rvm")`

: returns the parameters of the kernel function- lev
`signature(object = "rvm")`

: returns the levels of the response (in classification)- mlike
`signature(object = "rvm")`

: returns the estimated maximum likelihood- nvar
`signature(object = "rvm")`

: returns the calculated variance (in regression)- type
`signature(object = "rvm")`

: returns the type of problem- xmatrix
`signature(object = "rvm")`

: returns the data matrix used during computation- ymatrix
`signature(object = "rvm")`

: returns the used response

```
# NOT RUN {
# create data
x <- seq(-20,20,0.1)
y <- sin(x)/x + rnorm(401,sd=0.05)
# train relevance vector machine
foo <- rvm(x, y)
foo
alpha(foo)
RVindex(foo)
fitted(foo)
kernelf(foo)
nvar(foo)
## show slots
slotNames(foo)
# }
```

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