gsThreshold: Produce a minimum planar graph (MPG) at multiple scales
In grainscape: Grains of connectivity and minimum planar graph modelling of landscape connectivity (Windows only)

Description Usage Arguments Value Note Author(s) References See Also Examples

This function performs a scalar analysis of a minimum planar graph (MPG) by building the graph at a series of link thresholds. As the threshold value increases more nodes in the graph become connected, forming increasingly fewer components, until the graph becomes connected (e.g. Brooks, 2003). N.B. Grains of connectivity (GOC) done by gsGOC is also a scalar analysis using Voronoi tessellations rather than patches (see Galpern et al., 2012).

1	gsThreshold(gsMPG, weight = "lcpPerimWeight", nThresh = NULL, doThresh = NULL)

`gsMPG`	A `gsMPG` object produced by `gsMPG`.
`weight`	A string giving the link weight or attribute to use for threshold. `"lcpPerimWeight"` uses the accumulated resistance or least-cost path distance from the perimeters of patches as the link weight. `"eucPerimWeight"` use the Euclidean distance from the perimeters of patches as the link weight.
`nThresh`	Optional. An integer giving the number of thresholds (or scales) at which to create GOC models. Thresholds are selected to produce a maximum number of unique grains (i.e. models). `nThresh` thresholds are also approximately evenly spread between 0 and the threshold at which all patches or focal points on the landscape are connected. This is a simple way to get a representative subset of all possible GOC models. Provide either `nThresh` or `doThresh` not both.
`doThresh`	Optional. A vector giving the link thresholds at which to create GOC models. Use `link{gsThreshold}` to identify thresholds of interest. Provide either `nThresh` or `doThresh` not both.

A list object with the following elements:
$summary summarizes the thresholded graphs generated and their properties
$th is a list of length nThresh or length(doThresh) giving the thresholded graph (class igraph) at each threshold.

See gsMPG for warning related to areal measurements.

Paul Galpern (pgalpern@gmail.com)

Brooks, C.P. (2003) A scalar analysis of landscape connectivity. Oikos 102:433-439.

Fall, A., M.-J. Fortin, M. Manseau, D. O'Brien. (2007) Spatial graphs: Principles and applications for habitat connectivity. Ecosystems. 10:448:461

Galpern, P., M. Manseau, P.J. Wilson. (2012) Grains of connectivity: analysis at multiple spatial scales in landscape genetics. Molecular Ecology 21:3996-4009.

gsMPG

## Not run: 
# Load raster landscape
tiny <- raster(system.file("extdata/tiny.asc", package="grainscape"))

## Create a resistance surface from a raster using an is-becomes reclassification
tinyCost <- reclassify(tiny, rcl=cbind(c(1, 2, 3, 4), c(1, 5, 10, 12)))

## Produce a patch-based MPG where patches are resistance features=1
tinyPatchMPG <- gsMPG(cost=tinyCost, patch=tinyCost==1)

## Threshold this graph at a representative subset of 10 thresholds
tinyThresh <- gsThreshold(tinyPatchMPG, nThresh=10)

## Examine the properties of one of these threshold graphs
print(tinyThresh$th[[7]], vertex=TRUE, edge=TRUE)

## End(Not run)