bkde {KernSmooth} R Documentation

## Compute a Binned Kernel Density Estimate

### Description

Returns x and y coordinates of the binned kernel density estimate of the probability density of the data.

### Usage

```bkde(x, kernel = "normal", canonical = FALSE, bandwidth,
gridsize = 401, range.x, truncate = TRUE)
```

### Arguments

 `x` vector of observations from the distribution whose density is to be estimated. Missing values are not allowed. `bandwidth` the kernel bandwidth smoothing parameter. Larger values of `bandwidth` make smoother estimates, smaller values of `bandwidth` make less smooth estimates. `kernel` character string which determines the smoothing kernel. `kernel` can be: `"normal"` - the Gaussian density function (the default). `"box"` - a rectangular box. `"epanech"` - the centred beta(2,2) density. `"biweight"` - the centred beta(3,3) density. `"triweight"` - the centred beta(4,4) density. `canonical` logical flag: if `TRUE`, canonically scaled kernels are used. `gridsize` the number of equally spaced points at which to estimate the density. `range.x` vector containing the minimum and maximum values of `x` at which to compute the estimate. The default is the minimum and maximum data values, extended by the support of the kernel. `truncate` logical flag: if `TRUE`, data with `x` values outside the range specified by `range.x` are ignored.

### Details

This is the binned approximation to the ordinary kernel density estimate. Linear binning is used to obtain the bin counts. For each `x` value in the sample, the kernel is centered on that `x` and the heights of the kernel at each datapoint are summed. This sum, after a normalization, is the corresponding `y` value in the output.

### Value

a list containing the following components:

 `x` vector of sorted `x` values at which the estimate was computed. `y` vector of density estimates at the corresponding `x`.

### Background

Density estimation is a smoothing operation. Inevitably there is a trade-off between bias in the estimate and the estimate's variability: large bandwidths will produce smooth estimates that may hide local features of the density; small bandwidths may introduce spurious bumps into the estimate.

### References

Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.

`density`, `dpik`, `hist`, `ksmooth`.
```data(geyser, package="MASS")