Connectedness of Dissimilarities

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 NAs. 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 \(1\)s. The connectedness between two points can be defined either by a threshold toolong or using input dissimilarities with NAs.

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.

References

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

Author

Jari Oksanen

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)
#> Connectivity of distance matrix with threshold dissimilarity 0.4 
#> Data are disconnected: 6 groups
#> Groups sizes
#>  1  2  3  4  5  6 
#>  1 11  2  4  1  1 
# Make sites with no shared species as NA in Manhattan dissimilarities
dis <- vegdist(dune, "manhattan")
is.na(dis) <- no.shared(dune)