akima: interp.old – R documentation

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interp.old

Gridded Bivariate Interpolation for Irregular Data

Description

These functions implement bivariate interpolation onto a grid for irregularly spaced input data. These functions are only for backward compatibility, use interp instead.

Usage

interp.old(x, y, z, xo= seq(min(x), max(x), length = 40),
           yo=seq(min(y), max(y), length = 40), ncp = 0,
           extrap=FALSE, duplicate = "error", dupfun = NULL)
interp.new(x, y, z, xo = seq(min(x), max(x), length = 40),
           yo = seq(min(y), max(y), length = 40), linear = FALSE,
           ncp = NULL, extrap=FALSE, duplicate = "error", dupfun = NULL)

Arguments

`x`	vector of x-coordinates of data points or a `SpatialPointsDataFrame` object. Missing values are not accepted.
`y`	vector of y-coordinates of data points. Missing values are not accepted. If left as NULL indicates that `x` should be a `SpatialPointsDataFrame` and `z` names the variable of interest in this dataframe.
`z`	vector of z-coordinates of data points or a character variable naming the variable of interest in the `SpatialPointsDataFrame` `x`. Missing values are not accepted. `x`, `y`, and `z` must be the same length (execpt if `x` is a `SpatialPointsDataFrame`) and may contain no fewer than four points. The points of `x` and `y` cannot be collinear, i.e, they cannot fall on the same line (two vectors `x` and `y` such that `y = ax + b` for some `a`, `b` will not be accepted). `interp` is meant for cases in which you have `x`, `y` values scattered over a plane and a `z` value for each. If, instead, you are trying to evaluate a mathematical function, or get a graphical interpretation of relationships that can be described by a polynomial, try `outer()`.
`xo`	vector of x-coordinates of output grid. The default is 40 points evenly spaced over the range of `x`. If extrapolation is not being used (`extrap=FALSE`, the default), `xo` should have a range that is close to or inside of the range of `x` for the results to be meaningful.
`yo`	vector of y-coordinates of output grid; analogous to `xo`, see above.
`linear`	logical – indicating wether linear or spline interpolation should be used. supersedes old `ncp` parameter
`ncp`	deprecated, use parameter `linear`. Now only used by `interp.old()`. old meaning was: number of additional points to be used in computing partial derivatives at each data point. `ncp` must be either `0` (partial derivatives are not used), or at least 2 but smaller than the number of data points (and smaller than 25).
`extrap`	logical flag: should extrapolation be used outside of the convex hull determined by the data points?
`duplicate`	character string indicating how to handle duplicate data points. Possible values are `"error"` produces an error message, `"strip"` remove duplicate z values, `"mean"`,`"median"`,`"user"` calculate mean , median or user defined function (`dupfun`) of duplicate z values.
`dupfun`	a function, applied to duplicate points if `duplicate= "user"`.

Details

see interp

Value

list with 3 components:

`x,y`	vectors of x- and y- coordinates of output grid, the same as the input argument `xo`, or `yo`, if present. Otherwise, their default, a vector 40 points evenly spaced over the range of the input `x`.
`z`	matrix of fitted z-values. The value `z[i,j]` is computed at the x,y point `xo[i], yo[j]`. `z` has dimensions `length(xo)` times `length(yo)`.

If input is a SpatialPointsDataFrame a SpatialPixelssDataFrame is returned.

Note

interp.new is deprecated and interp.old will soon be deprecated.

References

Akima, H. (1978). A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points. ACM Transactions on Mathematical Software 4, 148-164.

Akima, H. (1996). Algorithm 761: scattered-data surface fitting that has the accuracy of a cubic polynomial. ACM Transactions on Mathematical Software 22, 362–371.