# Kernel density derivative estimate

### Description

Kernel density derivative estimate for 1- to 6-dimensional data.

### Usage

1 2 3 4 5 6 |

### Arguments

`x` |
matrix of data values |

`H,h` |
bandwidth matrix/scalar bandwidth. If these are missing, |

`deriv.order` |
derivative order (scalar) |

`gridsize` |
vector of number of grid points |

`gridtype` |
not yet implemented |

`xmin,xmax` |
vector of minimum/maximum values for grid |

`supp` |
effective support for standard normal |

`eval.points` |
points at which estimate is evaluated |

`binned` |
flag for binned estimation. Default is FALSE. |

`bgridsize` |
vector of binning grid sizes |

`positive` |
flag if 1-d data are positive. Default is FALSE. |

`adj.positive` |
adjustment applied to positive 1-d data |

`w` |
vector of weights. Default is a vector of all ones. |

`deriv.vec` |
flag to compute all derivatives in vectorised derivative. Default is TRUE. If FALSE then only the unique derivatives are computed. |

`verbose` |
flag to print out progress information. Default is FALSE. |

`object` |
object of class |

`...` |
other parameters |

### Details

For each partial derivative, for grid estimation, the estimate is a
list whose elements
correspond to the partial derivative indices in the rows of `deriv.ind`

.
For points estimation, the estimate is a matrix whose columns correspond to
rows of `deriv.ind`

.

If the bandwidth `H`

is missing from `kdde`

, then
the default bandwidth is the plug-in selector
`Hpi`

. Likewise for missing `h`

.

The effective support, binning, grid size, grid range, positive
parameters are the same as `kde`

.

### Value

A kernel density derivative estimate is an object of class
`kdde`

which is a list with fields:

`x` |
data points - same as input |

`eval.points` |
points at which the estimate is evaluated |

`estimate` |
density derivative estimate at |

`h` |
scalar bandwidth (1-d only) |

`H` |
bandwidth matrix |

`gridtype` |
"linear" |

`gridded` |
flag for estimation on a grid |

`binned` |
flag for binned estimation |

`names` |
variable names |

`w` |
weights |

`deriv.order` |
derivative order (scalar) |

`deriv.ind` |
each row is a vector of partial derivative indices |

### See Also

`kde`

### Examples

1 2 3 4 5 6 |

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