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)Run the code above in your browser using DataLab