# Calculation of an overfitting histogram

### Description

The function returns an overfitting histogram when a data matrix is given as an input. The output is an evaluation tree which is grown with greedy growing. The evaluation tree defines a partition of the sample space. The evaluation tree may be pruned to get a density estimate.

### Usage

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### Arguments

`dendat` |
n*d data matrix |

`minobs` |
non-negative integer; splitting of a bin will be continued if the bin containes "minobs" or more observations |

`leaf` |
internal (maximal number of leafs in the evaluation tree) |

`method` |
"loglik" or "projec"; the contrast function |

`splitscan` |
internal (random selection of splits) |

`seedf` |
internal |

`suppo` |
2*d vector of real numbers; the rectangle to be splitted; the rectangle has to contain the data |

### Value

Returns an evaluation tree as a list of vectors.

`direc ` |
integer in 1,...,d; variable which is splitted |

`split ` |
real number; splitting point |

`mean ` |
nonnegative number; value of the histogram on the rectangle corresponding to the node |

`nelem ` |
nonnegative integer; number of observations in the rectangle corresponding to the node |

`ssr ` |
real number; value of the likelihood criterion |

`volume ` |
non-negative number; volume of the rectangle corresponding to the node |

`left ` |
non-negative integer; link to the left child, 0 if terminal node |

`right ` |
non-negative integer; link to the right child, 0 if terminal node |

`low ` |
the lower vertice of the rectangles |

`upp ` |
the upper vertice of the rectangles |

`N ` |
the number of grid points at each direction |

`support` |
the support of the histogram |

### Author(s)

Jussi Klemela

### See Also

`prune`

,
`eval.pick`

### Examples

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