oldRegionalNull: Randomize community data matrix with regional null model
In eliotmiller/metricTester: Test Metric and Null Model Statistical Performance

Description Usage Arguments Details Value References Examples

Null model that simulates community assembly where species probabilities of occurrence are proportional to their regional abundance.

1	oldRegionalNull(picante.cdm, tree, regional.abundance)

`picante.cdm`	Picante-style community data matrix with communities/plots/plots/etc as rows and species as columns
`tree`	Ape-style phylogeny
`regional.abundance`	Vector of species names, where each species' name is repeated the number of times necessary to accommodate its abundance in the regional species pool. Note that this can be created, if needed, from the observed picante.cdm with the abundanceVector() function.

Although nowhere near as fast as, e.g. randomizeMatrix, this function still runs fairly quickly (no for or while loops). It works by drawing the total number of individuals observed in the input plot from the regional abundance vector. Thus, while a randomized plot will not necessarily have the same number of species as the observed plot, over many iterations it will likely be sampled. We can then concatenate the results by richness at the end, which will only compare observed values to random plots of the same richness. As an example, an observed plot might have two individuals of speciesA and two of speciesB. If the regional abundance vector is c("spA","spA","spA","spA","spB","spB","spB","spC"), and we draw four individuals, it would be possible to draw 1, 2, or 3 species, but in general, two species would be seen in the randomized plots.

A matrix with all species in the input tree in phylogenetic order, and the same number of randomized plots as used in the input community data matrix

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

tree <- geiger::sim.bdtree(b=0.1, d=0, stop="taxa", n=50)

#prep the data for the simulation
prepped <- prepSimulations(tree, arena.length=300, mean.log.individuals=2, 
	length.parameter=5000, sd.parameter=50, max.distance=20, proportion.killed=0.2,
	competition.iterations=3)

positions <- competitionArena(prepped)

boundResults <- plotPlacer(no.plots=15, arena.length=300, plot.length=30)

#return a CDM in picante format
cdmTemp <- plotContents(positions$arena, boundResults)

test <- oldRegionalNull(cdmTemp$picante.cdm, tree, 
regional.abundance=abundanceVector(cdmTemp$picante.cdm))