plot.rarefaction: Plot rarfeaction results
In rtk: Rarefaction Tool Kit

Description Usage Arguments Details Author(s) References See Also Examples

Rarefy datasets in R or from a path.

## S3 method for class 'rtk'
plot(x, div = c("richness"),  groups = NA, col = NULL, lty = 1,
         pch = NA, fit = "arrhenius", legend = TRUE, legend.pos = "topleft",
         log.dim = "", boxplot = FALSE, ...)

`x`	a rare result object
`div`	Diversity measure to plot. Can be any of `c('richness', 'shannon', 'simpson', 'invsimpson', 'chao1', 'eve')`
`groups`	If grouping is desired a vector of factors corresponting to the input samples
`col`	Colors used for plotting. Can be a vector of any length which will be recycled if it is to small. By default a rainbow is used.
`lty`	Linetypes used for plotting. Can be a vector of any length which will be recycled if it is to small.
`pch`	Symbols used for plotting. Can be a vector of any length which will be recycled if it is to small.
`fit`	Fit the rarefaction curve. Possible values: `c("arrhenius", "michaelis-menten", "logis")`
`legend`	Logical indicating if a legend should be created or not
`legend.pos`	Position of the said legend
`log.dim`	Character vector indicating which scale log log transform for plotting rarefaction curves.
`boxplot`	If a boxplot should be added to the lineplot of the rarefaction curve.
`...`	Other plotting input will be passed to `plot` or `boxplot` repectivly

To create plots from the rarefaction results you can easily just call a plot on the resulting elements. This will either produce a rarefaction curve, if mor than one depth was rarefied to, or a boxplot for a single depth. Grouping of samples is possible by simply passing a vetor of the length of the samples to the option groups.

Rarefaction curves can be fittet to either the arrhenius-equation, the michaelis-menten (SSmicmen) equation or the logis function SSlogis. To disable fitting fit must be set to FALSE.

Falk Hildebrand, Paul Saary

Saary, Paul, et al. "RTK: efficient rarefaction analysis of large datasets." Bioinformatics (2017): btx206.

rtk, collectors.curve

require("rtk")
# generate semi sparse example data
data            <- matrix(sample(x = c(rep(0, 1500),rep(1:10, 500),1:1000),
                          size = 120, replace = TRUE), 40)
# find the column with the lowest aboundance
samplesize      <- min(colSums(data))
# rarefy the dataset, so each column contains the same number of samples
d1  <- rtk(input = data, depth = samplesize)
# rarefy to different depths between 1 and samplesize
d2  <- rtk(input = data, depth = round(seq(1, samplesize, length.out = 10)))

# just the richness of all three samples as boxplot
plot(d1, div = "richness")
#rarefaction curve for each sample with fit
plot(d2, div = "eveness", fit = "arrhenius", pch = c(1,2,3))
# Rarefaction curve with boxplot, sampels pooled together (grouped)
plot(d2, div = "richness", fit = FALSE, boxplot = TRUE, col = 1, groups = rep(1, ncol(data)))