indicator_plrange: Power-law range indicator
In spatialwarnings: Spatial Early Warning Signals of Ecosystem Degradation

Description Usage Arguments Details Value References See Also Examples

Compute the power-law range of a matrix

1	indicator_plrange(mat, merge = FALSE, xmin_bounds = NULL)

`mat`	A logical matrix, or a list of logical matrices
`merge`	Controls whether the patch-size distributions of the input matrices are merged together before computing the power-law range. Setting this value to `TRUE` makes the function return a single value even if multiple matrices are given as input.
`xmin_bounds`	A vector of two integer values, defining a range in which to search for the best xmin (see Details).

Some ecosystems show typical changes in their patch-size distribution as they become more and more degraded. In particular, an increase in the truncation of the patch-size distribution (PSD) is expected to occur. The power-law range (PLR) measures the truncation of the PSD in a single value (see also patchdistr_spews for more details).

To compute the PLR, power-laws are fitted with a variable minimum patch size (xmin) and the one with the lowest Kolmogorov-Smirnov distance to the empirical distribution is retained. PLR is then computed using this best-fitting xmin:

(log(xmax) - log(xmin))/(log(xmax) - log(xsmallest))

where x_max is the maximum observed patch size, and x_smallest is the minimum observed patch size.

A data.frame with columns minsize, maxsize which are the observed minimum and maximum patch sizes. The estimated x_min and the value of the power-law range. If multiple matrices were provided, then a list of data.frames is returned

Clauset, A., Shalizi, C. R., & Newman, M. E. (2009). Power-law distributions in empirical data. SIAM review, 51(4), 661-703.

Berdugo, M., Kefi, S., Soliveres, S. & Maestre, F.T. (2017). Plant spatial patterns identify alternative ecosystem multifunctionality states in global drylands. Nature in Ecology and Evolution.

patchdistr_spews

## Not run: 
forestgap.plr <- indicator_plrange(forestgap) 
do.call(rbind, forestgap.plr) # convert results to data.frame

# Restrict to small xmins 
forestgap.plr2 <- indicator_plrange(forestgap, xmin_bounds = c(1, 10)) 
do.call(rbind, forestgap.plr2) 

## End(Not run)