Description Usage Arguments Value Side Effects See Also

The program calculates the distance between each pair of datapoints. The distances are grouped in groups such that even number of pairs is in each group. Then the estimated variogram for each group is calculated either by taking the mean or by a method from Cressie and Hawkins (1980). The latter method does in essence take the sum of the values^0.25. Only pair of points with distance less than certain distance are used. Zero - Zero pairs are not used if zzp is F.

1 2 3 |

`lat` |
Latitude of datapoints. If lon=0 lat is a list with components \$lat and \$lon. |

`z` |
Values at datapoints. |

`lon` |
longitude of datapoints. |

`nbins` |
Number of distance intervals used. |

`maxdist` |
maximum distance of interest. Default value is range of data*0.7. |

`Hawk` |
If true the method from Cressie and Hawkins (1980) is used, else the mean. Default value is T |

`throwout` |
If T datapoints with value zero are not used at all. Default value is F which means that they are used. In all cases zero-zero pairs are not used when the variogram is estimated. |

`scale` |
"km" or "nmi". Default is "km". |

`evennumber` |
If T distance classes are chosen so approximately the same number of points is in each distance class. Else even distance increments are used. |

`zzp` |
If true zero-zero pairs are used else not. Default value is F. |

`minnumber` |
Distance intervals with minnumber or less pairs are not included. Default is zero. |

`col.names` |
if lat is a dataframe col.names should contain the names of the vectors containing the x and y coordinates, default is c("lat","lon") |

A list with the following components: <s-example>

number: Number of pair in each distance class. dist: Mean distance in each distance class. vario: variogram for each distance class. </s-example> The list is suitable for the program variofit. The variogram can also be plotted by plvar(vagram,fit=F)

No side effects.

`variofit`

,
`pointkriging`

,
`plvar`

.

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