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.

1 |

`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 |

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

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
data(sinc)
x <- sinc$x
y <- sinc$y
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 = c(1, 2))
``` |

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