Smooth.wmppp {dbmss}

Spatial smoothing of individual dbmss's

Description

Performs spatial smoothing of the individual values of distance-based measures computed in the neighborhood of each point (Marcon and Puech, 2023).

Usage

  ## S3 method for class 'wmppp'
 Smooth(X, fvind, distance = NULL, Quantiles = FALSE, 
      sigma = bw.scott(X, isotropic = TRUE), Weighted = TRUE, Adjust = 1, 
      Nbx = 128, Nby = 128, ..., CheckArguments = TRUE)

Arguments

X

A point pattern (wmppp.object).

fvind

An object of class fv, see fv.object, obtained a distance-based method, such as Mhat with individual values (argument Individual = TRUE).

distance

The distance at which the function value must be considered. The default value is the median distance used to calculate the function values.

Quantiles

If FALSE (default), the dbmss is smoothed to produce a map of the measure. If TRUE, its quantiles (computed by Mhat with argument Quantiles = TRUE) are smoothed to produce a map of the confidence level of the measure.

Weighted

If TRUE (default), the point weights are taken into account for smoothing.

sigma

The bandwidth used for smoothing. A Gaussian kernel is used (see Smooth.ppp). Its bandwidth is chosen by default according to Scott's rule (see bw.scott).

Adjust

Force the selected bandwidth (sigma) to be multiplied by Adjust. Setting it to values smaller than one (1/2 for example) will sharpen the estimation.

Nbx, Nby

The number of columns and rows (pixels) of the resulting map, 128 by default. Increase it for quality, paid by increasing computing time.

...

Extra arguments, passed to Smooth.ppp.

CheckArguments

If TRUE (default), the function arguments are verified. Should be set to FALSE to save time in simulations for example, when the arguments have been checked elsewhere.

Value

An image that can be plotted. If quantiles have been computed in fvind, attributes "High" and "Low" contain logical vectors to indentify significantly high and low quantiles.

References

Marcon, E. and Puech, F. (2023). Mapping distributions in non-homogeneous space with distance-based methods. Journal of Spatial Econometrics 4(1), 13.

Examples

  ReferenceType <- "V. Americana"
  NeighborType <- "Q. Rosea"
  # Calculate individual intertype M(distance) values
  fvind <- Mhat(paracou16, r=c(0, 30), ReferenceType, NeighborType, Individual=TRUE)
  # Plot the point pattern with values of M(30 meters)
  p16_map <- Smooth(paracou16, fvind, distance=30)
  plot(p16_map, main = "")
  # Add the reference points to the plot
  is.ReferenceType <- paracou16$marks$PointType == ReferenceType
  points(x=paracou16$x[is.ReferenceType], y=paracou16$y[is.ReferenceType], pch=20)
  # Add contour lines
  contour(p16_map, nlevels = 5, add = TRUE)