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kmmd-class

Class "kqr"

Description

The Kernel Maximum Mean Discrepancy object class

Objects from the Class

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

Slots

kernelf:

Object of class "kfunction" contains the kernel function used

xmatrix:

Object of class "kernelMatrix" containing the data used

H0

Object of class "logical" contains value of : is H0 rejected (logical)

AsympH0

Object of class "logical" contains value : is H0 rejected according to the asymptotic bound (logical)

mmdstats

Object of class "vector" contains the test statistics (vector of two)

Radbound

Object of class "numeric" contains the Rademacher bound

Asymbound

Object of class "numeric" contains the asymptotic bound

Methods

kernelf

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

H0

signature(object = "kmmd"): returns the value of H0 being rejected

AsympH0

signature(object = "kmmd"): returns the value of H0 being rejected according to the asymptotic bound

mmdstats

signature(object = "kmmd"): returns the values of the mmd statistics

Radbound

signature(object = "kmmd"): returns the value of the Rademacher bound

Asymbound

signature(object = "kmmd"): returns the value of the asymptotic bound

Author(s)

See Also

Examples

# create data
x <- matrix(runif(300),100)
y <- matrix(runif(300)+1,100)


mmdo <- kmmd(x, y)

H0(mmdo)
kernlab

Kernel-Based Machine Learning Lab

v0.9-29
GPL-2
Authors
Alexandros Karatzoglou [aut, cre], Alex Smola [aut], Kurt Hornik [aut], National ICT Australia (NICTA) [cph], Michael A. Maniscalco [ctb, cph], Choon Hui Teo [ctb]
Initial release

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