renyi
find renyiaccum
finds these statistics with accumulating sites.renyi(x, scales = c(0, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, Inf),
hill = FALSE)
## S3 method for class 'renyi':
plot(x, ...)
renyiaccum(x, scales = c(0, 0.5, 1, 2, 4, Inf), permutations = 100,
raw = FALSE, collector = FALSE, subset, ...)
## S3 method for class 'renyiaccum':
plot(x, what = c("Collector", "mean", "Qnt 0.025", "Qnt 0.975"),
type = "l",
...)
## S3 method for class 'renyiaccum':
persp(x, theta = 220, col = heat.colors(100), zlim, ...)
how
,
or a permutation matrix where each row givesFALSE
then return summary statistics of
permutations, and if TRUE
then returns the individual
permutations.raw = TRUE
.FALSE
.type = "l"
means lines.persp
.persp
.renyi
and
to graphical functions.renyi
returns a data frame of selected
indices. Function renyiaccum
with argument raw = FALSE
returns a three-dimensional array, where the first dimension are the
accumulated sites, second dimension are the diversity scales, and
third dimension are the summary statistics mean
, stdev
,
min
, max
, Qnt 0.025
and Qnt 0.975
. With
argument raw = TRUE
the statistics on the third dimension are
replaced with individual permutation results.diversity
indices are special cases of
The plot
method for renyi
uses
Function renyiaccum
is similar to specaccum
but
finds scales
for random permutations of accumulated sites. Its plot
function uses xyplot
to display the accumulation curves for each value of scales
in a separate panel. In addition, it has a persp
method to
plot the diversity surface against scale and number and
sites. Similar dynamic graphics can be made with
rgl.renyiaccum
in
Hill, M.O. (1973). Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427--473.
Kindt R, Van Damme P, Simons AJ. 2006. Tree diversity in western
Kenya: using profiles to characterise richness and
evenness. Biodiversity and Conservation 15: 1253-1270.
diversity
for diversity indices, and
specaccum
for ordinary species accumulation curves, and
xyplot
, persp
and
rgl.renyiaccum
.data(BCI)
i <- sample(nrow(BCI), 12)
mod <- renyi(BCI[i,])
plot(mod)
mod <- renyiaccum(BCI[i,])
plot(mod, as.table=TRUE, col = c(1, 2, 2))
persp(mod)
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