# qrnn-rbf: Radial basis function kernel In qrnn: Quantile Regression Neural Network

## 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

 `1` ```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.

`qrnn.fit`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```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) ```

### Example output

```tau=0.5
1/1
1 0.08825163
* 0.08825163
```

qrnn documentation built on Sept. 13, 2019, 9:04 a.m.