qrnn-rbf: Radial basis function kernel

In qrnn: Quantile Regression Neural Network

Radial basis function kernel

Description

Evaluate a kernel matrix based on the radial basis function kernel. Can be used in conjunction with qrnn.fit with Th set to linear and penalty set to a nonzero value for kernel quantile ridge regression.

Usage

qrnn.rbf(x, x.basis, sigma)

Arguments

x	covariate matrix with number of rows equal to the number of samples and number of columns equal to the number of variables.
x.basis	covariate matrix with number of rows equal to the number of basis functions and number of columns equal to the number of variables.
sigma	kernel width

Value

kernel matrix with number of rows equal to the number of samples and number of columns equal to the number of basis functions.

See Also

qrnn.fit

Examples

x <- as.matrix(iris[,"Petal.Length",drop=FALSE])
y <- as.matrix(iris[,"Petal.Width",drop=FALSE])

cases <- order(x)
x <- x[cases,,drop=FALSE]
y <- y[cases,,drop=FALSE]

set.seed(1)
kern <- qrnn.rbf(x, x.basis=x, sigma=1)

parms <- qrnn.fit(x=kern, y=y, tau=0.5, penalty=0.1,
                  Th=linear, Th.prime=linear.prime,
                  iter.max=500, n.trials=1)
p <- qrnn.predict(x=kern, parms=parms)

matplot(x, cbind(y, p), type=c("p", "l"), pch=1, lwd=1)

qrnn documentation built on May 29, 2024, 1:27 a.m.