kernlab (version 0.9-27)

gausspr-class: Class "gausspr"

Description

The Gaussian Processes object class

Objects from the Class

Objects can be created by calls of the form new("gausspr", ...). or by calling the gausspr function

Slots

tol:

Object of class "numeric" contains tolerance of termination criteria

kernelf:

Object of class "kfunction" contains the kernel function used

kpar:

Object of class "list" contains the kernel parameter used

kcall:

Object of class "list" contains the used function call

type:

Object of class "character" contains type of problem

terms:

Object of class "ANY" contains the terms representation of the symbolic model used (when using a formula)

xmatrix:

Object of class "input" containing the data matrix used

ymatrix:

Object of class "output" containing the response matrix

fitted:

Object of class "output" containing the fitted values

lev:

Object of class "vector" containing the levels of the response (in case of classification)

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).

sol

Object of class "matrix" containing the solution to the Gaussian Process formulation, it is used to compute the variance in regression problems.

scaling

Object of class "ANY" containing the scaling coefficients of the data (when case scaled = TRUE is used).

nvar:

Object of class "numeric" containing the computed variance

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

Methods

alpha

signature(object = "gausspr"): returns the alpha vector

cross

signature(object = "gausspr"): returns the cross validation error

error

signature(object = "gausspr"): returns the training error

fitted

signature(object = "vm"): returns the fitted values

kcall

signature(object = "gausspr"): returns the call performed

kernelf

signature(object = "gausspr"): returns the kernel function used

kpar

signature(object = "gausspr"): returns the kernel parameter used

lev

signature(object = "gausspr"): returns the response levels (in classification)

type

signature(object = "gausspr"): returns the type of problem

xmatrix

signature(object = "gausspr"): returns the data matrix used

ymatrix

signature(object = "gausspr"): returns the response matrix used

scaling

signature(object = "gausspr"): returns the scaling coefficients of the data (when scaled = TRUE is used)

See Also

gausspr, ksvm-class, vm-class

Examples

# NOT RUN {
# train model
data(iris)
test <- gausspr(Species~.,data=iris,var=2)
test
alpha(test)
error(test)
lev(test)
# }