mpaRaoS {rasterdiv}R Documentation

Multidimensional sequential Parametric Rao's index of quadratic entropy (Q)

Description

This function calculates the multidimensional parametric Rao's index of quadratic entropy (Q) using a sequential method. It is particularly useful in contexts where parallel computation is not feasible or desired. The function applies a moving window approach to the provided raster data stack.

Usage

mpaRaoS(
  x,
  alpha,
  window,
  dist_m,
  na.tolerance,
  rescale,
  lambda,
  diag,
  debugging,
  isfloat,
  mfactor,
  np
)

Arguments

x

input list.

alpha

Numeric; alpha value for order of diversity in Hill's Index.

window

Numeric; half of the side of the square moving window used for calculation.

dist_m

Character; type of distance used in the analysis.

na.tolerance

Numeric; a threshold between 0.0 and 1.0 indicating the allowable proportion of NA values within each moving window. If the proportion of NA values exceeds this, the window's value is set as NA; otherwise, the computation uses the non-NA values.

rescale

Logical; if TRUE, scales and centres the values in each element of 'x'.

lambda

Numeric; lambda value used for Minkowski distance calculation.

diag

Logical; if TRUE, includes the diagonal of the distance matrix in computations.

debugging

Logical; if TRUE, additional diagnostic messages are output, useful for debugging. Default is FALSE.

isfloat

Logical; specifies if the input data are floats.

mfactor

Numeric; multiplication factor applied if input data are float numbers.

np

Number of processes for parallel computation.

Value

A list of matrices, each representing a layer of the input RasterStack, containing calculated Rao's index values. The dimensions correspond to those of the input, and the list length is equal to the length of 'alpha'.

Author(s)

Duccio Rocchini duccio.rocchini@unibo.it, Matteo Marcantonio marcantoniomatteo@gmail.com

See Also

paRao for the parallelized version of the Rao's index computation.


[Package rasterdiv version 0.3.4 Index]