Discretizes the data into a regular grid (computes a binned approximation)
using the multivariate linear binning technique described in Wand (1994).

binning(x, y = NULL, nbin = NULL, set.NA = FALSE, window = NULL, ...)
as.bin.data(object, ...)
# S3 method for data.grid
as.bin.data(object, data.ind = 1, weights.ind = NULL, ...)
# S3 method for bin.data
as.bin.data(object, ...)
# S3 method for SpatialGridDataFrame
as.bin.data(object, data.ind = 1, weights.ind = NULL, ...)

## Arguments

x |
vector or matrix of covariates (e.g. spatial coordinates).
Columns correspond with covariates (coordinate dimension) and rows with data. |

y |
vector of data (response variable). |

nbin |
vector with the number of bins on each dimension. |

set.NA |
logical. If `TRUE` , sets the bin averages corresponding
to cells without data to `NA` . |

window |
spatial window (values outside this window will be masked), currently an sp-object of class
extending `SpatialPolygons` . |

... |
further arguments passed to `mask.bin.data()` . |

object |
(gridded data) used to select a method. |

data.ind |
integer (or character) with the index (or name) of the component
containing the bin averages. |

weights.ind |
integer (or character) with the index (or name) of the component
containing the bin counts/weights (if not specified, they are set to
`as.numeric( is.finite( object[[data.ind]] ))` ). |

## Value

If `y != NULL`

, an S3 object of `class`

`bin.data`

(gridded binned data; extends `bin.den`

) is returned.
A `data.grid`

object with the following 4 components:

binyvector or array (dimension `nbin`

) with the bin averages.

binwvector or array (dimension `nbin`

) with the bin counts (weights).

grida `grid.par`

-`class`

object with the grid parameters.

dataa list with 3 components:

If y == NULL,

bin.den is called and a

bin.den-

class object is returned.

## Details

If parameter `nbin`

is not specified is set to `pmax(25, rule.binning(x))`

.

Setting `set.NA = TRUE`

(equivalent to `biny[binw == 0] <- NA`

)
may be useful for plotting the binned averages `$biny`

(the hat matrix should be handled with care when using `locpol`

).

## References

Wand M.P. (1994) Fast Computation of Multivariate Kernel Estimators.
*Journal of Computational and Graphical Statistics*, **3**, 433-445.

## See also

## Examples