metricTester (version 1.3.0)

sesOverall: Overall per simulation-null-metric SES test

Description

This function provides one of many ways of summarizing and considering simulation results.

Usage

sesOverall(simulation.list, test, direction)

Arguments

simulation.list

A summarized results list such as one output from reduceResults(). See examples.

test

Either "ttest" or "wilcotest", depending on whether the user wants to run a two-sided t-test or a Wilcoxon signed rank test.

direction

Character vector that needs to be provided if spatial simulations beyond the standard "random", "filtering", and "competition" simulations are run. The character vector must be the same length as the number of spatial simulations that were run, and can take the possible values of "two.sided" (for a two-tailed test when the SES scores are expected to be centered around 0), "less" (for when the observed SES scores are expected to be less than 0), and "greater" (for when the observed SES scores are expected to be greater than 0). For instance, habitat filtering would be set to "less". The relevant simulation to which these directional tests will be applied can be determined by calling names(simulation.list).

Value

A data frame summarizing the mean, overall standardized effect sizes and the significance of those devations from expectations for each simulation, null, metric combination. This test works across all iterations, and looks for overall shifts in SES from expectations (see details for for expectations).

Details

This function provides one way of summarizing and considering simulation results. It takes as input a vector of all standardized effect sizes for all plots from a given simulation-null-metric combination, and calculates the mean of the vector and whether it differs significantly from a mean of zero. It does this either with a simple two-sided t-test, or with a Wilcoxon signed rank test. If the latter, and if there are three different spatial simulations with names random, filtering and competition, the test is two-sided, less and greater, respectively. If additional spatial simulations are included, requiring modified expectations, these can be passed along with the "direction" argument.

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
#results <- readIn()
#summ <- reduceResults(results)
#examp <- sesOverall(summ$ses, test="wilcotest")