This function sends out jobs to as many cores as are specified. Each randomizes the input CDM according to all defined null models, then calculates each observed metric on each randomized matrix.
metricsNnulls(tree, picante.cdm, optional.dists = NULL,
regional.abundance = NULL, distances.among = NULL,
randomizations = 2, cores = "seq", nulls, metrics)Phylo object
A picante-style community data matrix with sites as rows, and species as columns
A symmetric distance matrix can be directly supplied. This option is experimental. Behavior depends on metric being used. If the metric in question relies on the phylogenetic distance matrix from a call to cophenetic(tree), then this optional distance matrix will be inserted instead.
A character vector in the form "s1, s1, s1, s2, s2, s3, etc". Optional, will be generated from the input CDM if not provided.
A symmetric distance matrix, summarizing the distances among all plots from the cdm. Optional, only used by some null models.
The number of times the input CDM should be randomized and the metrics calculated across it.
This function can run in parallel. In order to do so, the user must specify the desired number of cores to utilize. The default is "seq", which runs the calculations sequentially.
Optional. If not provided, defines the nulls as all of those in defineNulls. If only a subset of those is desired, then nulls should take the form of a character vector corresponding to named functions from defineNulls. The available nulls can be determined by running names(defineNulls()). Otherwise, if the user would like to define a new null on the fly, the argument nulls can take the form of a named list of new functions (nulls).
Optional. If not provided, defines the metrics as all of those in defineMetrics. If only a subset of those 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 can take the form of a named list of new functions (metrics). Note that some functions, particularly the summaries, assume that the first metric calculated is species richness. This is a design flaw and will be fixed in the future, but for the time-being, we recommend always calculating "richness" as the first metric. This happens by default if all metrics are used, but if provided with a vector of metric names, richness should be the first element in that vector.
A list of lists of vectors. The first level has as many elements as there are randomizations. The second level has one list for each null model. Each element of this second level is a named vector corresponding to the calculated metric at each plot.
This function sends out jobs to as many cores as are specified. Each randomizes the input CDM according to all defined null models, then calculates each observed metric on each randomized matrix.
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
# 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)) + 1
cdm <- simulateComm(tree, richness.vector=10:25, abundances=sim.abundances)
rawResults <- metricsNnulls(tree, cdm, randomizations=3,
nulls=c("richness", "frequency"))
# }
Run the code above in your browser using DataLab