metricTester (version 1.3.6)

calcBetaMetrics: Calculate phylogenetic community structure beta metrics

Description

Given a prepped metrics.input object, calculate all phylogenetic community structure metrics of interest.

Usage

calcBetaMetrics(metrics.input, metrics, new_ = FALSE)

Arguments

metrics.input

Prepped metrics.input object

metrics

Optional. If not provided, defines the metrics as all of those in defineBetaMetrics. If only a subset of those metrics is desired, then metrics should take form of a character vector corresponding to named functions from defineBetaMetrics. The available metrics can be determined by running names(defineBetaMetrics()). Otherwise, if the user would like to define a new metric on the fly, the argument metrics can take the form of a named list of new functions (metrics). If the latter, new_ must be set to TRUE.

new_

Whether or not new metrics are being defined on the fly. Default is FALSE. Set to TRUE if a new metric is being used.

Value

A data frame with the calculated metrics and the associated species richness and total abundance of all input "communities".

Details

This function first confirms that the input is of class metrics.input and, if so, then confirms that the metrics to be calculated are in a named list (via checkMetrics), then lapplies all metric functions to the input metrics.input object.

References

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

Examples

# NOT RUN {
#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))

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

prepped <- prepData(tree, cdm)

results <- calcBetaMetrics(prepped)

#an example of how to define ones own metrics for use in the metricTester framework
#this "metric" simply calculates the richness of each plot in the CDM
exampleMetric <- function(metrics.input)
{
output <- mean(apply(metrics.input$picante.cdm, 1, lengthNonZeros))
output
}

calcBetaMetrics(prepped, metrics=list("example"=exampleMetric), new_=TRUE)
# }