kernel.function {KSPM}R Documentation

Kernel Functions

Description

These functions transform a n \times p matrix into a n \times n kernel matrix.

Usage

kernel.gaussian(x, rho = ncol(x))

kernel.linear(x)

kernel.polynomial(x, rho = 1, gamma = 0, d = 1)

kernel.sigmoid(x, rho = 1, gamma = 1)

kernel.inverse.quadratic(x, gamma = 1)

kernel.equality(x)

Arguments

x

a n \times p matrix

gamma, rho, d

kernel hyperparameters (see details)

Details

Given two p-dimensional vectors x and y,

Of note, Gaussian, inverse quadratic and equality kernels are measures of similarity resulting to a matrix containing 1 along the diagonal.

Value

A n \times n matrix.

Author(s)

Catherine Schramm, Aurelie Labbe, Celia Greenwood

References

Liu, D., Lin, X., and Ghosh, D. (2007). Semiparametric regression of multidimensional genetic pathway data: least squares kernel machines and linear mixed models. Biometrics, 63(4), 1079:1088.


[Package KSPM version 0.2.1 Index]