metricTester (version 1.0.0)

metricsNnulls: Parallelized function that calculates metrics on randomized matrices

Description

This function sends out jobs to as many cores as are specified. Each randomizes the input CDM according to all defined null models, then calculates each observed metric on each randomized matrix.

Usage

metricsNnulls(tree, picante.cdm, optional.dists = NULL,
  regional.abundance = NULL, distances.among = NULL, randomizations = 2,
  cores = 1, nulls, metrics)

Arguments

tree

Phylo object

picante.cdm

A picante-style community data matrix with sites as rows, and species as columns

optional.dists

A symmetric distance matrix can be directly supplied. This option is experimental. Behavior depends on metric being used. If the metric in question relies on the phylogenetic distance matrix from a call to cophenetic(tree), then this optional distance mat

regional.abundance

A character vector in the form "s1, s1, s1, s2, s2, s3, etc". Optional, will be generated from the input CDM if not provided.

distances.among

A symmetric distance matrix, summarizing the distances among all plots from the cdm. Optional, only used by some null models.

randomizations

The number of times the input CDM should be randomized and the metrics calculated across it.

cores

This function can run in parallel. In order to do so, the user must specify the desired number of cores to utilize.

nulls

Optional list of named null model functions to use. If invoked, this option will likely be used to run a subset of the defined null models.

metrics

Optional list of named metric functions to use. If invoked, this option will likely be used to run a subset of the defined metrics.

Value

• A list of lists of vectors. The first level has as many elements as there are randomizations. The second level has one list for each null model. Each element of this second level is a named vector corresponding to the calculated metric at each plot.

Details

This function sends out jobs to as many cores as are specified. Each randomizes the input CDM according to all defined null models, then calculates each observed metric on each randomized matrix.

References

Miller, E. T., D. R. Farine, and C. H. Trisos. 2015. Phylogenetic community structure metrics and null models: a review with new methods and software. bioRxiv 025726.

Examples

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

sim.abundances <- round(rlnorm(5000, meanlog=2, sdlog=1)) + 1

cdm <- simulateComm(tree, richness.vector=10:25, abundances=sim.abundances)

rawResults <- metricsNnulls(tree, cdm, randomizations=3, cores=1,
	nulls=list("richness"=metricTester:::my_richnessNull,
	"frequency"=metricTester:::my_frequency))