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Create spatial derivative of raster stacks

Source: R/utils-predictors.R
predictor_derivate.Rd

This function creates derivatives of existing covariates and returns them in Raster format. Derivative variables can in the machine learning literature commonly be understood as one aspect of feature engineering. They can be particularly powerful in introducing non-linearities in otherwise linear models, for example is often done in the popular Maxent framework.

Usage

predictor_derivate(
  env,
  option,
  nknots = 4,
  deriv = NULL,
  int_variables = NULL,
  method = NULL,
  ...
)

Arguments

env

A SpatRaster object.

option

A vector stating whether predictors should be preprocessed in any way (Options: 'none', 'quadratic', 'hinge', 'kmeans', 'thresh', 'bin').

nknots

The number of knots to be used for the transformation (Default: 4).

deriv

A vector with character of specific derivates to create (Default: NULL).

int_variables

A vector with length greater or equal than 2 specifying the covariates (Default: NULL).

method

As 'option' for more intuitive method setting. Can be left empty (in this case option has to be set).

...

other options (Non specified).

Value

Returns the derived adjusted SpatRaster objects of identical resolution.

Details

Available options are:

  • 'none' - The original layer(s) are returned.

  • 'quadratic' - A quadratic transformation (\(x^{2}\)) is created of the provided layers.

  • 'hinge' - Creates hinge transformation of covariates, which set all values lower than a set threshold to 0 and all others to a range of \([0,1]\). The number of thresholds and thus new derivates is specified via the parameter 'nknots' (Default: 4).

  • 'interaction' - Creates interactions between variables. Target variables have to be specified via "int_variables".

  • 'thresh' - A threshold transformation of covariates, which sets all values lower than a set threshold at 0 and those larger to 1. The number of thresholds and thus new derivates is specified via the parameter 'nknots' (Default: 4).

  • 'bin' - Creates a factor representation of a covariates by cutting the range of covariates by their percentiles. The number of percentile cuts and thus new derivates is specified via the parameter 'nknots' (Default: 4).

  • 'kmeans' Creates a factor representation of a covariates through a kmeans() clustering. The number of clusters are specified via the parameter 'nknots'.

See also

predictor_transform

Examples

# Dummy raster
 r_ori <- terra::rast(nrows = 10, ncols = 10, res = 0.05, xmin = -1.5, xmax = 1.5, ymin = -1.5, ymax = 1.5, vals = rpois(3600, 10))

# Create a hinge transformation with 4 knots of one or multiple SpatRaster.
new <- predictor_derivate(r_ori, option = "hinge", knots = 4)
terra::plot(new)


# Or a quadratic transformation
new2 <- predictor_derivate(r_ori, option = "quad", knots = 4)
terra::plot(new2)