newRegionalNull: Randomize community data matrix with second-gen regional null...
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	newRegionalNull(picante.cdm, regional.abundance)

`picante.cdm`	Picante-style community data matrix with communities/plots/plots/etc as rows and species as columns
`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.

This model is slightly different than the regional null model introduced in our Ecography paper. It samples from the same regional abundance vector, but fills individuals into a blank CDM until the richness of that plot in the input CDM is reached. Thus, the results are equally valid whether concatenated by richness or by plot. Unlike the original regional null model, however, the same number of individuals per plot is not maintained. Also, because the model uses a while loop, it is conceivable that some parameters could result in long runs, e.g., while the requisite number of species are sampled from a highly skewed abundance distribution. Preliminary tests did not show this to be an issue, but worth paying attention to if plots contain large numbers of species. There is a check for the most extreme situation: when the abundance vector contains fewer unique species than are in the plot with the maximum number of species.

A matrix of the same size and names as 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 <- newRegionalNull(cdmTemp$picante.cdm,
regional.abundance=abundanceVector(cdmTemp$picante.cdm))