metricTester (version 1.3.6)

calcMetrics: Calculate phylogenetic community structure metrics

Description

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

Usage

calcMetrics(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 defineMetrics. If only a subset of those metrics is desired, then metrics should take the form of a character vector corresponding to named functions from defineMetrics. The available metrics can be determined by running names(defineMetrics()). 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 of all input "communities".

Details

Determine which metrics will be calculated by running names(defineMetrics()). If only a subset of these is desired, supply metrics with a character vector of the named, available metrics. IMPORTANTLY, note that some downstream functions expect the first column returned from this function to be the species richness of each plot. It is best practice therefore to always pass "richness" along as the first metric, even when only a subset of metrics is being calculated. It is possible to provide this function with both predefined metrics and metrics that are defined on the fly, but the call is rather convoluted. See examples.

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 <- calcMetrics(prepped)

#an example of how to only calculate a subset of pre-defined metrics
results2 <- calcMetrics(prepped, metrics=c("richness","NAW_MPD"))

#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 <- apply(metrics.input$picante.cdm, 1, lengthNonZeros)
output
}

calcMetrics(prepped, metrics=list("richness"=metricTester:::my_richness,
"example"=exampleMetric), new_=TRUE)
# }