varyX: Calculate alpha or beta metrics across a set of parameters
In eliotmiller/metricTester: Test Metric and Null Model Statistical Performance

Description Usage Arguments Details Value References Examples

Takes a specified set of parameters, and holds those constant while varying one of the parameters to see variance in community structure metrics.

1 2	varyX(alpha = TRUE, tree.size, richness, delta, abundances, beta.iterations, iterations, cores = "seq")

`alpha`	Whether to calculate alpha or beta phylogenetic community structure metrics. Default is TRUE. Set to FALSE for beta metrics.
`tree.size`	Either a single number, or if the aim is to vary tree size while holding other parameters constant, then a list of numbers of species desired in each total tree. If provided as a vector of numbers, will be coerced to a list.
`richness`	Either the number of species to be placed in each plot, or if the aim is to vary richness while holding other parameters constant, then a list of vectors of number of species to be placed in each plot. See examples.
`delta`	Either a value for the delta transformation (Pagel 1999), or if the aim is to vary tree shape while holding other parameters constant, then a list of numbers of delta transformations. If provided as a vector of numbers, will be coerced to a list. Values greater than 1 push the branching events towards the root, while values less than 1 push the branching events closer to the tips. See details for particularly low delta values.
`abundances`	Either a vector of abundances, or if the aim is to vary the abundance distribution function, then a list of vectors of abundances. See examples.
`beta.iterations`	Because the type of beta-level phylogenetic community structure metrics used here return a single value per community data matrix, it is not possible to look for inter-metric correlations with only a single matrix and tree. To deal with this, the same tree can be used with different community data matrices. This argument specifies the number of matrices to be used per tree. Not needed if alpha=TRUE.
`iterations`	How many times to simulate the given set of parameters. For instance, with a single tree size, richness.vector, delta, and two sets of abundances, and 10 iterations, the result will be a list with 10 elements. Each of those 10 elements will be a list of two elements, each of which will be the calculated metrics for a given set of parameters (one for each abundance vector).
`cores`	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. The iteration aspect of the function is parallelized, so for efficiency purposes, it is best to run this over numerous iterations rather than repeating the same parameter numerous times (e.g., rather than setting deltas to rep(1, 10), set delta to 1 and iterations to 10).

If given a small value, e.g. 0.1, the delta parameter (tree shape) can occasionally result in oddly formatted trees that would cause errors. To deal with this, there is an internal check that will recreate a new tree and re-scale it with the desired delta. This has not been tested at delta < 0.1, and is currently programmed with a while loop. Care should be taken not to get R stuck in an indefinite loop at delta values even lower than 0.1

A list of lists of data frames.

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

#example of how to vary tree size
#not run
#system.time(temp <- varyX(alpha=TRUE, tree.size=c(59, 100),
#richness=40:59, delta=1,
#abundances=round(rlnorm(5000, meanlog=2, sdlog=1)) + 1, iterations=2))

#example of how to vary richness
#not run
#system.time(temp <- varyX(alpha=TRUE, tree.size=59,
#richness=list(30:39, 40:49), delta=1,
#abundances=round(rlnorm(5000, meanlog=2, sdlog=1)) + 1, iterations=2))

#example of how to vary tree shape
#not run
#system.time(temp <- varyX(alpha=TRUE, tree.size=59,
#richness=40:59, delta=c(0.1,10),
#abundances=round(rlnorm(5000, meanlog=2, sdlog=1)) + 1, iterations=2))

#example of how to vary abundance
#not run
#inputAbunds <- list(rep(2,5000), (round(rnorm(5000, 50, 15)) + 1),
#(round(rlnorm(5000, meanlog=2, sdlog=1)) + 1))

#system.time(temp <- varyX(alpha=TRUE, tree.size=59,
#richness=40:59, delta=1, abundances=inputAbunds, iterations=2))