This function provides Spline interpolation over the input data enriched by
the uncertainty. The input data must be an S4 object class of
`UncertainPoints`

and grid type of `data.frame`

.
Output object is type of S4 class `UncertainInterpolation`

.

1 2 3 |

`object` |
Input data. An object of |

`grid` |
Input grid type of |

`m` |
A polynomial function of degree (m-1) will be included in the model as the drift (or spatial trend) component. Default is the value such that 2m-d is greater than zero where d is the dimension of x. |

`p` |
Polynomial power for Wendland radial basis functions. Default is 2m-d where d is the dimension of x. |

`scale.type` |
The independent variables and knots are scaled to the specified scale.type. By default the scale type is "range", whereby the locations are transformed to the interval (0,1) by forming (x-min(x))/range(x) for each x. Scale type of "user" allows specification of an x.center and x.scale by the user. The default for "user" is mean 0 and standard deviation 1. Scale type of "unscaled" does not scale the data. |

`lon.lat` |
If TRUE locations are interpreted as lognitude and latitude and great circle distance is used to find distances among locations. |

`miles` |
If TRUE great circle distances are in miles if FALSE distances are in kilometers. |

`method` |
Determines what "smoothing" parameter should be used. The default is to estimate standard GCV Other choices are: GCV.model, GCV.one, RMSE, pure error and REML. The differences are explained in the Krig help file. |

`GCV` |
If TRUE the decompositions are done to efficiently evaluate the estimate, GCV function and likelihood at multiple values of lambda. |

Returns an object of class `UncertainInterpolation`

.

`UncertainPoints-class`

, `UncertainInterpolation-class`

, `Grid.def`

,`Grid.box`

, `Grid.interpolation`

, `Tps`

, `Plot`

, `uncertaintyInterpolation2-package`

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