multiCDM: Wrapper for deriving CDMs from the results of multiple...
In metricTester: Test Metric and Null Model Statistical Performance

Description Usage Arguments Details Value References Examples

Given the results of a call to runSimulations(), this function places plots down randomly (though identically across simulations).

1	multiCDM(simulations.result, no.plots, plot.length)

`simulations.result`	List of data frames of three columns: "individuals", "X", and "Y"
`no.plots`	Number of plots to place
`plot.length`	Length of one side of desired plot

Both the size and number of plots are determined by the user. A conservative check (perhaps overly so) is in place to ensure the function doesn't get stuck looking for solutions for how to randomly place non-overlapping plots. Either decreasing the number or size of plots is necessary if this throws an error.

A list of data frames.

Miller, E. T., D. R. Farine, and C. H. Trisos. 2016. Phylogenetic community structure metrics and null models: a review with new methods and software. Ecography DOI: 10.1111/ecog.02070

#simulate tree with birth-death process
tree <- geiger::sim.bdtree(b=0.1, d=0, stop="taxa", n=50)

prepped <- prepSimulations(tree, arena.length=300, mean.log.individuals=2,
	length.parameter=1000, sd.parameter=50, max.distance=20, proportion.killed=0.2,
competition.iterations=5)

#run the spatial simulations
arenas <- runSimulations(prepped)

#derive CDMs. plots are placed in the same places across all spatial simulations.
#density of individuals per arena is low enough in this example that sometimes all
#plots contain < 2 species, and are cut, causing an error. this not run so as not to
#throw errors on CRAN
#cdms <- multiCDM(arenas, no.plots=10, plot.length=20)