renyi {vegan} | R Documentation

## Renyi and Hill Diversities and Corresponding Accumulation Curves

### Description

Function `renyi` find Rényi diversities with any scale or the corresponding Hill number (Hill 1973). Function `renyiaccum` finds these statistics with accumulating sites.

### Usage

    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, ...)
    ## S3 method for class 'renyiaccum':
    plot (x, what = c("mean", "Qnt 0.025", "Qnt 0.975"), type = "l",
        ...)
    ## S3 method for class 'renyiaccum':
    persp (x, theta = 220, col = heat.colors(100), zlim, ...)
    rgl.renyiaccum(x, rgl.height = 0.2, ...)

### Arguments

`x` | Community data matrix or plotting object.
`scales` | Scales of Rényi diversity.
`hill` | Calculate Hill numbers.
`permutations` | Number of random permutations in accumulating sites.
`raw` | if `FALSE` then return summary statistics of permutations, and if `TRUE` then returns the individual permutations.
`what` | Items to be plotted.
`type` | Type of plot, where `type = "l"` means lines.
`theta` | Angle defining the viewing direction (azimuthal) in `persp`.
`col` | Colours used for surface. Single colour will be passed on, and vector colours will be selected by the midpoint of a rectangle in `persp`.
`zlim` | Limits of vertical axis.
`rgl.height` | Scaling of vertical axis.
`...` | Other arguments which are passed to `renyi` and to graphical functions.

### Details

Common `diversity` indices are special cases of Rényi diversity

H.a = 1/(1-a) log sum(p^a)

where a is a scale parameter, and Hill (1975) suggested to use so-called ``Hill numbers'' defined as N.a = exp(H.a). Some Hill numbers are the number of species with a = 0, exp(H') or the exponent of Shannon diversity with a = 1, inverse Simpson with a = 2 and 1/max(p) with a = Inf. According to the theory of diversity ordering, one community can be regarded as more diverse than another only if its Rényi diversities are all higher (Tóthmérész 1995).

The `plot` method for `renyi` uses lattice graphics, and displays the diversity values against each scale in separate panel for each site together with minimum, maximum and median values in the complete data.

Function `renyiaccum` is similar to `specaccum` but finds Rényi or Hill diversities at given `scales` for random permutations of accumulated sites. Its `plot` function uses lattice function `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. Dynamic graphics with `rgl.renyiaccum` use rgl package, and produces similar surface as `persp` with a mesh showing the empirical confidence levels.

### Value

Function `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 diveristy 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.

### Author(s)

Roeland Kindt r.kindt@cgiar.org and Jari Oksanen

### References

http://www.worldagroforestry.org/treesandmarkets/tree_diversity_analysis.asp

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.

Tóthmérész, B. (1995). Comparison of different methods for diversity ordering. Journal of Vegetation Science 6, 283–290.

### See Also

`diversity` for diversity indices, and `specaccum` for ordinaty species accumulation curves, and `xyplot`, `persp` and `rgl` for controlling graphics.

### Examples

    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)

[Package vegan version 1.8-8 Index]