Bivariate Spline Interpolation

Interpolates bivariate data sets using Akima spline interpolation.

Usage

akimaInterp(x, y = NULL, z = NULL, gridPoints = 21,
    xo = seq(min(x), max(x), length = gridPoints),
    yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE)

akimaInterpp(x, y = NULL, z = NULL, xo, yo, extrap = FALSE)

Arguments

x, y, z: for akimaInterp the arguments x and y are two numeric vectors of grid pounts, and z is a numeric matrix or any other rectangular object which can be transformed by the function as.matrix into a matrix object. For akimaInterpp we consider either three numeric vectors of equal length or if y and z are NULL, a list with entries x, y, z, or named data frame with x in the first, y in the second, and z in the third column.
gridPoints: an integer value specifying the number of grid points in x and y direction.
xo, yo: for akimaInterp two numeric vectors of data points spanning the grid, and for akimaInterpp two numeric vectors of data points building pairs for pointwise interpolation.
extrap: a logical, if TRUE then the data points are extrapolated.

Details

Two options are available: gridded and pointwise interpolation.

akimaInterp is a wrapper to interp provided by the contributed R package akima. The Fortran code of the Akima spline interpolation routine was written by H. Akima.

Linear surface fitting and krige surface fitting are provided by the functions linearInterp and krigeInterp.

Note

Package akima is no longer needed. Equivalent functions from package interp are now called instead.

Value

akimaInterp: returns a list with at least three entries, x, y and z. Note, that the returned values, can be directly used by the persp and contour 3D plotting methods.
akimaInterpp: returns a data.frame with columns "x", "y", and "z".

References

Akima H., 1978, A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points, ACM Transactions on Mathematical Software 4, 149-164.

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.

Examples

# \donttest{
## Does not run for r-solaris-x86
## akimaInterp -- Akima Interpolation:
if (requireNamespace("interp")) {
  set.seed(1953)
  x <- runif(999) - 0.5
  y <- runif(999) - 0.5
  z <- cos(2*pi*(x^2+y^2))
  ans <- akimaInterp(x, y, z, gridPoints = 41, extrap = FALSE)
  persp(ans, theta = -40, phi = 30, col = "steelblue",
       xlab = "x", ylab = "y", zlab = "z")
  contour(ans)
}
#> Loading required namespace: interp



## Use spatial as alternative on r-solaris-x86
## spatialInterp - Generate Kriged Grid Data:
if (requireNamespace("spatial")) {
  RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
  set.seed(4711, kind = "Marsaglia-Multicarry")
  x <- runif(999)-0.5
  y <- runif(999)-0.5
  z <- cos(2*pi*(x^2+y^2))
  ans <- krigeInterp(x, y, z, extrap = FALSE)
  persp(ans)
  title(main = "Kriging")
  contour(ans)
  title(main = "Kriging")
}
#> Warning: RNGkind: Marsaglia-Multicarry has poor statistical properties
#> Warning: RNGkind: Marsaglia-Multicarry has poor statistical properties


# }