Diversity Plot
Usage
# S4 method for class 'DiversityIndex,missing'
plot(
x,
log = "x",
col.mean = "#DDAA33",
col.interval = "#004488",
lty.mean = "solid",
lty.interval = "dashed",
lwd.mean = 1,
lwd.interval = 1,
xlab = NULL,
ylab = NULL,
main = NULL,
sub = NULL,
ann = graphics::par("ann"),
axes = TRUE,
frame.plot = axes,
panel.first = NULL,
panel.last = NULL,
...
)Arguments
- x
A DiversityIndex object to be plotted.
- log
A
characterstring indicating which axes should be in log scale. Defaults tox.- col.mean, col.interval
A
characterstring specifying the color of the lines.- lty.mean, lty.interval
A
characterstring ornumericvalue specifying the line types.- lwd.mean, lwd.interval
A non-negative
numericvalue specifying the line widths.- xlab, ylab
A
charactervector giving the x and y axis labels.- main
A
characterstring giving a main title for the plot.- sub
A
characterstring giving a subtitle for the plot.- ann
A
logicalscalar: should the default annotation (title and x, y and z axis labels) appear on the plot?- axes
A
logicalscalar: should axes be drawn on the plot?- frame.plot
A
logicalscalar: should a box be drawn around the plot?- panel.first
An an
expressionto be evaluated after the plot axes are set up but before any plotting takes place. This can be useful for drawing background grids.- panel.last
An
expressionto be evaluated after plotting has taken place but before the axes, title and box are added.- ...
Further graphical parameters to be passed to
graphics::points(), particularly,cex,colandpch.
Value
plot() is called for its side-effects: it results in a graphic being
displayed (invisibly returns x).
See also
Other diversity measures:
diversity(),
evenness(),
heterogeneity(),
occurrence(),
plot.RarefactionIndex(),
profiles(),
rarefaction(),
richness(),
she(),
similarity(),
simulate(),
turnover()
Examples
# \donttest{
## Data from Conkey 1980, Kintigh 1989
data("cantabria")
## Assemblage diversity size comparison
## Warning: this may take a few seconds!
h <- heterogeneity(cantabria, method = "shannon")
h_sim <- simulate(h)
plot(h_sim)
r <- richness(cantabria, method = "observed")
r_sim <- simulate(r)
plot(r_sim)
# }
