metricTester (version 1.2.2)

nullPerformance: Summarize null model performance of a series of summarized simulation results

Description

Flexible function that summarizes null performance after reading in and testing simulation results with either sesIndiv or plotOverall.

Usage

nullPerformance(summarized.results, simulations = "all", metrics = "all", concat.by = "both")

Arguments

summarized.results

The results of a call to sesIndiv() or plotOverall()

simulations

Default is "all". Alternatively, can supply a vector of simulation names to summarize the results over.

metrics

Default is "all". Alternatively, can supply a vector of metric names to summarize the results over.

concat.by

Default is "both". Alternatively, can supply either "plot" or "richness".

Value

A data frame of summarized results

Details

If an overall picture of null performance is desired, this function can provide it. It can also be used to summarize null performance over a specific subset of simulations, metrics, and concatenation options. If provided with the results of a call to plotOverall, the options are more limited. Currently, if provided with such a result, the assumption is that there are three spatial simulations, "random", "filtering", and "competition". It then assumes that any clustered or overdispersed plots for the random simulation, or any overdispersed or clustered for the filtering or competition simulations, respectively, count as typeI errors. It assumes that any plots that are not clustered or overdispersed for the filtering or competition simulations, respectively, count as typeII errors.

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

#not run
#results <- readIn()
#summ <- sesIndiv(results)
#examp <- nullPerformance(summ)