diverrare: Rarefaction curves using Jost diversities
In walterxie/ComMA: Community Matrix Analysis

Description Usage Arguments Details Value Examples

Create rarefaction curves using Jost diversities.

getSubsamplesForDiverRare(cm, sample.size, replicates = 10,
  levels = rep(c("gamma", "alpha", "beta"), 3), qs = rep(0:2, each = 3),
  is.transposed = FALSE, progressBar = TRUE, verbose = TRUE)

getDiverRare(cm, sample.sizes = 10, replicates = 10,
  levels = rep(c("gamma", "alpha", "beta"), 3), qs = rep(0:2, each = 3),
  progressBar = TRUE, min.sample.abundance = NA,
  non.rarefied.subsamples = TRUE, verbose = TRUE)

getMultiDiverRare(cm.list, min.sample.abundance.list = NA, ...)

ggDiverRare(diver.rare, multi.cm = FALSE, diversity = c(),
  line.or.point = 3, point.size = 2, title = "", x.lab = "sample size",
  y.lab = "diversity", ...)

`cm`	The community matrix.
`sample.size`	An interger number representing subsample size for rarefying community using `rrarefy`. It must be smaller than the minimun sample abundance, which can be calculated by `summaryCM.Vector`.
`replicates`	The number of replicates to repeat subsampling.
`levels`	The levels of Jost diversities in `d`. Use `paste(levels, qs, sep="")` to get the actual measurements.
`qs`	The order of Jost diversities in `d`.
`is.transposed`	If TRUE, then `cm` is already the transposed matrix of `vegdist` input. Default to FASLE.
`progressBar`	Whether to print progress bar, default to TRUE.
`sample.sizes`	The vector of subsample sizes for rarefying community using `rrarefy`. It can be also an integer to define the length of vector, as default to 10, which allows to automatically create the vector of subsample sizes ranged by `min.sample.abundance` and `max.sample.abundance` of `cm`
`min.sample.abundance`	If `min.sample.abundance` of `cm` is smaller than this threshold, then return NULL.
`non.rarefied.subsamples`	If TRUE, as default, the subsamples will include non rarefied ones, whose `sample.size` > `min.sample.abundance`. `rrarefy` will also create warnings.
`cm.list`	The list of community matrices.
`min.sample.abundance.list`	The list of `min.sample.abundance` for each `cm`, the default is NA.
`diver.rare`	The rarefaction curves of one or more community matrices. Use `multi.cm` to determine.
`multi.cm`	If FALSE, as default, the `diver.rare` is calculated from one community matrix and no colour, otherwise it will be treated as a list of rarefaction curves from a list of community matrices and coloured by `cm`.
`diversity`	The vector of diversity included in the graph, such as `c("gamma0","alpha0","beta1")`. The default is empty vector to inlcude all diversities in `diver.rare`.

getSubsamplesForDiverRare calculates subsamples for the rarefaction using Jost diversities, given a community matrix.

getDiverRare returns a list of subsamples calculated from getSubsamplesForDiverRare given a community matrix. Note: this community matrix must not be transposed.

getMultiDiverRare returns a list of rarefaction curves calculated from getDiverRare given a list of community matrices. Note: every community matrices must not be transposed.

ggDiverRare plots the rarefaction curves calculated from getDiverRare given a community matrix, or from getMultiDiverRare given a list of community matrices, and returns a ggplot object.

getSubsamplesForDiverRare returns a list of results: mean is a named vector of means of each diversity measurement, sterr is the standard error of the mean, subsamples is a matrix of all subsampled points to calculate means and errors, where the columns are diversity measurements, rows are replicates.

diver.rare <- getSubsamplesForDiverRare(cm, 2000, 5)

diver.rare <- getDiverRare(cm, replicates=5)

multi.diver.rare <- getMultiDiverRare(cm.list, replicates=5)

gg.plot <- ggDiverRare(diver.rare)
gg.plot <- ggDiverRare(multi.diver.rare, multi.cm=T, x.trans="log", auto.scale.x=T)