R: Connectedness of Dissimilarities

distconnected {vegan} | R Documentation

## Connectedness of Dissimilarities

### Description

Function `distconnected` finds groups that are connected disregarding dissimilarities that are at or above a threshold or `NA`. The function can be used to find groups that can be ordinated together or transformed by `stepacross`. Function `no.shared` returns a logical dissimilarity object, where `TRUE` means that sites have no species in common. This is a minimal structure for `distconnected` or can be used to set missing values to dissimilarities.

### Usage

    distconnected(dis, toolong = 1, trace = TRUE)

    no.shared(x)

### Arguments

`dis` | Dissimilarity data inheriting from class `dist` or a an object, such as a matrix, that can be converted to a dissimilarity matrix. Functions `vegdist` and `dist` are some functions producing suitable dissimilarity data.
`toolong` | Shortest dissimilarity regarded as `NA`. The function uses a fuzz factor, so that dissimilarities close to the limit will be made `NA`, too. If `toolong = 0` (or negative), no dissimilarity is regarded as too long.
`trace` | Summarize results of `distconnected`
`x` | Community data.

### Details

Data sets are disconnected if they have sample plots or groups of sample plots which share no species with other sites or groups of sites. Such data sets cannot be sensibly ordinated by any unconstrained method because these subsets cannot be related to each other. For instance, correspondence analysis will polarize these subsets with eigenvalue 1. Neither can such dissimilarities be transformed with `stepacross`, because there is no path between all points, and result will contain `NA`s. Function `distconnected` will find such subsets in dissimilarity matrices. The function will return a grouping vector that can be used for sub-setting the data. If data are connected, the result vector will be all 1s. The connectedness between two points can be defined either by a threshold `toolong` or using input dissimilarities with `NA`s.

Function `no.shared` returns a `dist` structure having value `TRUE` when two sites have nothing in common, and value `FALSE` when they have at least one shared species. This is a minimal structure that can be analysed with `distconnected`. The function can be used to select dissimilarities with no shared species in indices which do not have a fixed upper limit.

Function `distconnected` uses depth-first search (Sedgewick 1990).

### Value

Function `distconnected` returns a vector for observations using integers to identify connected groups. If the data are connected, values will be all `1`. Function `no.shared` returns an object of class `dist`.

### Author(s)

Jari Oksanen

### References

Sedgewick, R. (1990). Algorithms in C. Addison Wesley.

### See Also

`vegdist` or `dist` for getting dissimilarities, `stepacross` for a case where you may need `distconnected`, and for connecting points `spantree`.

### Examples

    ## There are no disconnected data in vegan, and the following uses an
    ## extremely low threshold limit for connectedness. This is for
    ## illustration only, and not a recommended practice.
    data(dune)
    dis <- vegdist(dune)
    gr <- distconnected(dis, toolong=0.4)
    # Make sites with no shared species as NA in Manhattan dissimilarities
    dis <- vegdist(dune, "manhattan")
    is.na(dis) <- no.shared(dune)

[Package vegan version 2.4-3 Index]