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Interpolate values at points to a GRaster using splines

Source: R/interpSplines.r
interpSplines.Rd

This function interpolates values in the data table of a "points" GVector to a GRaster using splines with Tykhonov regularization to avoid overfitting.

Usage

# S4 method for class 'GVector,GRaster'
interpSplines(
  x,
  y,
  field,
  method = "bilinear",
  lambda = NULL,
  solver = "Cholesky",
  xlength = NULL,
  ylength = NULL,
  interpolate = TRUE,
  verbose = is.null(lambda)
)

Arguments

x

A "points" GVector.

y

A GRaster: The output will have the same extent and resolution as this raster.

field

Character or integer or numeric integer: Name or index of the column in x with values to interpolate. If NULL and if x is a 3-dimensional "points" GVector, then the interpolation will act on the z-coordinate of each point.

method

Character: The method to use for interpolation can be either "bilinear" (default) or "bicubic". Partial matching is used.

lambda

Either NULL (default) or numeric value > 0: The Tykhonov regularization parameter. If NULL, cross-validation will be used to determine the optimal parameter value. Cross-validation can take quite a while. If you use cross-validation, the output will either be a GRaster or a data.frame, depending on the value of interpolate.

solver

Character: Type of solver to use. Can be either of "Cholesky" or "cg". Partial matching is used and case is ignored.

xlength, ylength

Either NULL (default), or numeric > 0: Length of the spline step in the x- and y-directions. If NULL, these will be set to 4 times the length of the extent in the respective direction.

interpolate

Logical: If TRUE (default), then create a GRaster with interpolated values. If FALSE, return a table with lambda values from cross-validation. This argument is ignored if lambda is a numeric value.

verbose

Logical: if TRUE, display progress.

Value

Output depends on values of lambda and interpolate:

  • lambda is NULL and interpolate is TRUE: A GRaster with an attribute named lambdas. This is a data.frame with values of lambda that were assessed, plus mean (mean residual value) and rms (root mean square error). You can see the table using attr(output_raster, "lambdas", exact = TRUE).

  • lambda is NULL and interpolate is FALSE: A data.frame with values of lambdas that were assessed, plus mean (mean residual value) and rms (root mean square error). You can see the table using attr(output_raster, "lambdas", exact = TRUE).

  • lambda is a number (interpolate is ignored): A GRaster.

Details

If you receive the error, "No data within this subregion. Consider increasing spline step values, try increasing the values of xlength and ylength.

If cross-validation takes too long, or other warnings/errors persist, you can randomly subsample x to ~100 points to get an optimum value of lambda (using interpolate = FALSE), then use this value in the same function again without cross-validation (setting lambda equal to this value and interpolate = TRUE).

See also

interpIDW(), fillNAs(), GRASS tool v.surf.bspline (see grassHelp("v.surf.bspline"))