plotTest: Calculate if single, observed metrics deviate beyond...
In metricTester: Test Metric and Null Model Statistical Performance

Description Usage Arguments Details Value References Examples

Given a table of results, where means, SDs, and CIs are bound to the observed scores at the corresponding richness or plot, this function calculates whether each observed score is significantly less or ore than expected at that plot or richness.

1	plotTest(results.table, concat.by)

`results.table`	Data frame of observed metrics with expected mean, SD and CI bound in. See example
`concat.by`	Whether to concatenate results by richness, plot or both. If richness, observed scores are compared to all randomized scores where the plot had the corresponding richness. If plot, observed scores (e.g. those from plot 1) are compared to all randomized plot 1 scores. If both, both are run and each is saved as a separate data frame in a single list.

Given a table of results, where means, SDs, and CIs are bound to the observed scores at the corresponding richness or plot, this function returns 0, 1, or 2, corresponding to not significant, significantly clustered, and significantly overdispersed. Previously the metrics being passed to the function needed to be explicitly specified, but the function now attempts to determine the names of the metrics via the results.table input.

A data frame of 0s, 1s, and 2s.

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

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

#simulate a log normal abundance distribution
sim.abundances <- round(rlnorm(5000, meanlog=2, sdlog=1)) + 1

#simulate a community of varying richness
cdm <- simulateComm(tree, richness.vector=10:25, abundances=sim.abundances)

#below not run for example timing issues on CRAN

#run the metrics and nulls combo function
rawResults <- metricsNnulls(tree=tree, picante.cdm=cdm, randomizations=2, cores="seq",
nulls=c("richness","frequency"), metrics=c("richness","NAW_MPD"))

#reduce the randomizations to a more manageable format
reduced <- reduceRandomizations(rawResults)

#calculate the observed metrics from the input CDM
observed <- observedMetrics(tree, cdm, metrics=c("richness","NAW_MPD"))

#summarize the means, SD and CI of the randomizations
summarized <- lapply(reduced, summaries, concat.by="richness")

#merge the observations and the summarized randomizations to facilitate significance
#testing
merged <- lapply(summarized, merge, observed)

#calculate the standardized scores of each observed metric as compared to the richness
#null model randomization.
plotTest(merged$richness, "richness")

#do the same as above but across all null models
#temp <- lapply(1:length(merged), function(x) plotTest(merged[[x]], "richness"))