R: Cladistic information content of a tree

CladisticInfo {TreeTools}

R Documentation

Cladistic information content of a tree

Description

CladisticInfo() calculates the cladistic (phylogenetic) information content of a phylogenetic object, sensu Thorley et al. (1998).

Usage

CladisticInfo(x)

PhylogeneticInfo(x)

## S3 method for class 'phylo'
CladisticInfo(x)

## S3 method for class 'Splits'
CladisticInfo(x)

## S3 method for class 'list'
CladisticInfo(x)

## S3 method for class 'multiPhylo'
CladisticInfo(x)

PhylogeneticInformation(x)

CladisticInformation(x)

Arguments

`x`	Tree of class `phylo`, or a list thereof.

Details

The CIC is the logarithm of the number of binary trees that include the specified topology. A base two logarithm gives an information content in bits.

The CIC was originally proposed by Rohlf (1982), and formalised, with an information-theoretic justification, by Thorley et al. (1998). Steel and Penny (2006) term the equivalent quantity "phylogenetic information content" in the context of individual characters.

The number of binary trees consistent with a cladogram provides a more satisfactory measure of the resolution of a tree than simply counting the number of edges resolved (Page 1992).

Value

CladisticInfo() returns a numeric giving the cladistic information content of the input tree(s), in bits. If passed a Splits object, it returns the information content of each split in turn.

Author(s)

Martin R. Smith (martin.smith@durham.ac.uk)

References

Page RD (1992). “Comments on the information content of classifications.” Cladistics, 8(1), 87–95. doi:10.1111/j.1096-0031.1992.tb00054.x.

Rohlf FJ (1982). “Consensus indices for comparing classifications.” Mathematical Biosciences, 59(1), 131–144. doi:10.1016/0025-5564(82)90112-2.

Steel MA, Penny D (2006). “Maximum parsimony and the phylogenetic information in multistate characters.” In Albert VA (ed.), Parsimony, Phylogeny, and Genomics, 163–178. Oxford University Press, Oxford.

Thorley JL, Wilkinson M, Charleston M (1998). “The information content of consensus trees.” In Rizzi A, Vichi M, Bock H (eds.), Advances in Data Science and Classification, 91–98. Springer, Berlin. ISBN 978-3-540-64641-9, doi:10.1007/978-3-642-72253-0.