spantree: Minimum Spanning Tree
In pattakosn/Rworkshop: Community Ecology Package

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Function spantree finds a minimum spanning tree connecting all points, but disregarding dissimilarities that are at or above the threshold or NA.

spantree(d, toolong = 0)
## S3 method for class 'spantree'
cophenetic(x)
spandepth(x)
## S3 method for class 'spantree'
plot(x, ord, cex = 0.7, type = "p", labels, dlim,
     FUN = sammon,  ...)
## S3 method for class 'spantree'
lines(x, ord, display="sites", ...)

`d`	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.
`x`	A `spantree` result object.
`ord`	An ordination configuration, or an ordination result known by `scores`.
`cex`	Character expansion factor.
`type`	Observations are plotted as points with `type="p"` or `type="b"`, or as text label with `type="t"`. The tree (lines) will always be plotted.
`labels`	Text used with `type="t"` or node names if this is missing.
`dlim`	A ceiling value used to highest `cophenetic` dissimilarity.
`FUN`	Ordination function to find the configuration from cophenetic dissimilarities.
`display`	Type of `scores` used for `ord`.
`...`	Other parameters passed to functions.

Function spantree finds a minimum spanning tree for dissimilarities (there may be several minimum spanning trees, but the function finds only one). Dissimilarities at or above the threshold toolong and NAs are disregarded, and the spanning tree is found through other dissimilarities. If the data are disconnected, the function will return a disconnected tree (or a forest), and the corresponding link is NA. Connected subtrees can be identified using distconnected.

Function cophenetic finds distances between all points along the tree segments. Function spandepth returns the depth of each node. The nodes of a tree are either leaves (with one link) or internal nodes (more than one link). The leaves are recursively removed from the tree, and the depth is the layer at with the leaf was removed. In disconnected spantree object (in a forest) each tree is analysed separately and disconnected nodes not in any tree have depth zero.

Function plot displays the tree over a supplied ordination configuration, and lines adds a spanning tree to an ordination graph. If configuration is not supplied for plot, the function ordinates the cophenetic dissimilarities of the spanning tree and overlays the tree on this result. The default ordination function is sammon (package MASS), because Sammon scaling emphasizes structure in the neighbourhood of nodes and may be able to beautifully represent the tree (you may need to set dlim, and sometimes the results will remain twisted). These ordination methods do not work with disconnected trees, but you must supply the ordination configuration. Function lines will overlay the tree in an existing plot.

Function spantree uses Prim's method implemented as priority-first search for dense graphs (Sedgewick 1990). Function cophenetic uses function stepacross with option path = "extended". The spantree is very fast, but cophenetic is slow in very large data sets.

Function spantree returns an object of class spantree which is a list with two vectors, each of length n-1. The number of links in a tree is one less the number of observations, and the first item is omitted. The items are

`kid`	The child node of the parent, starting from parent number two. If there is no link from the parent, value will be `NA` and tree is disconnected at the node.
`dist`	Corresponding distance. If `kid = NA`, then `dist = 0`.
`labels`	Names of nodes as found from the input dissimilarities.
`call`	The function call.

In principle, minimum spanning tree is equivalent to single linkage clustering that can be performed using hclust or agnes. However, these functions combine clusters to each other and the information of the actually connected points (the “single link”) cannot be recovered from the result. The graphical output of a single linkage clustering plotted with ordicluster will look very different from an equivalent spanning tree plotted with lines.spantree.

Jari Oksanen

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

vegdist or dist for getting dissimilarities, and hclust or agnes for single linkage clustering.

data(dune)
dis <- vegdist(dune)
tr <- spantree(dis)
## Add tree to a metric scaling 
plot(tr, cmdscale(dis), type = "t")
## Find a configuration to display the tree neatly
plot(tr, type = "t")
## Depths of nodes
depths <- spandepth(tr)
plot(tr, type = "t", label = depths)