SplitEntropy {TreeDist} | R Documentation |
Calculate the entropy, joint entropy, entropy distance and information content of two splits, treating each split as a division of n leaves into two groups. Further details are available in a vignette, MacKay (2003) and Meila (2007).
SplitEntropy(split1, split2 = split1)
split1 , split2 |
Logical vectors listing leaves in a consistent order,
identifying each leaf as a member of the ingroup ( |
A numeric vector listing, in bits:
H1
The entropy of split 1;
H2
The entropy of split 2;
H12
The joint entropy of both splits;
I
The mutual information of the splits;
Hd
The entropy distance (variation of information) of the splits.
Martin R. Smith (martin.smith@durham.ac.uk)
MacKay DJC (2003).
Information Theory, Inference, and Learning Algorithms.
Cambridge University Press, Cambridge.
https://www.inference.org.uk/itprnn/book.pdf.
Meila M (2007).
“Comparing clusterings—an information based distance.”
Journal of Multivariate Analysis, 98(5), 873–895.
doi:10.1016/j.jmva.2006.11.013.
Other information functions:
SplitSharedInformation()
,
TreeInfo
A <- TRUE
B <- FALSE
SplitEntropy(c(A, A, A, B, B, B), c(A, A, B, B, B, B))