plot: Plot rarfeaction results

Description

Rarefy datasets in R or from a path.

Usage

# S3 method for 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, ...)

Arguments

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

Details

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.

References

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

Examples

Run this code

# NOT RUN {
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)))

# }