CBI {EloRating}

Clutton-Brock et al 1979 index (CBI)

Description

Clutton-Brock et al 1979 index (CBI)

Usage

CBI(mat)

Arguments

mat

matrix

Details

The results of this function diverge from published examples in some cases. While the function produces identical scores as the results in Gammell et al. (2003) and de Vries and Appleby (2000) there are some slight deviations for the example in Whitehead (2008). The final example from Bang et al. (2010) is fairly off, but that seems to be because these authors might have applied different definitions: Bang et al. (2010) talk about 'who dominates' while (Clutton-Brock et al. 1979) consider 'who won interactions', which are two very different conceptualizations, and which might explain the discrepancies.

Value

a named numeric vector with the indices for each individual

Author(s)

Christof Neumann

References

Clutton-Brock TH, Albon SD, Gibson RM, Guinness FE (1979). “The logical stag: adaptive aspects of fighting in red deer (Cervus elaphus L.).” Animal Behaviour, 27, 211-225. doi:10.1016/0003-3472(79)90141-6.

Bang A, Deshpande SA, Sumana A, Gadagkar R (2010). “Choosing an appropriate index to construct dominance hierarchies in animal societies: a comparison of three indices.” Animal Behaviour, 79, 631-636. doi:10.1016/j.anbehav.2009.12.009.

Gammell MP, de Vries H, Jennings DJ, Carlin CM, Hayden TJ (2003). “David's score: a more appropriate dominance ranking method than Clutton-Brock et al.'s index.” Animal Behaviour, 66, 601-605. doi:10.1006/anbe.2003.2226.

de Vries H, Appleby MC (2000). “Finding an appropriate order for a hierarchy: a comparison of the I&SI and the BBS methods.” Animal Behaviour, 59, 239-245. doi:10.1006/anbe.1999.1299.

Whitehead H (2008). Analyzing animal societies: quantitative methods for vertebrate social analysis. University of Chicago Press, Chicago.

Examples

# example from Gammell et al 2003 (table 1)
m <- matrix(0, nrow = 5, ncol = 5)
m[upper.tri(m)] <- 100
m[1, 5] <- 99
m[5, 1] <- 1
colnames(m) <- rownames(m) <- c("r", "s", "t", "u", "v")
m
CBI(m)

# example from Whitehead 2008 (table 5.8, 5.9)
m <- c(0, 2, 0, 5, 2, 2, 1, 0, 2, 0,
       0, 0, 2, 2, 1, 0, 3, 2, 1, 1,
       0, 1, 0, 1, 1, 3, 1, 1, 4, 0,
       0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
       0, 0, 0, 0, 0, 7, 1, 4, 2, 3,
       0, 0, 0, 0, 0, 0, 2, 3, 6, 10,
       0, 1, 1, 0, 2, 0, 0, 0, 0, 2,
       0, 0, 0, 1, 0, 0, 0, 0, 1, 1,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
mat <- matrix(m, nrow = 10, byrow = TRUE)
colnames(mat) <- rownames(mat) <- c("x907", "x915", "x912", "x910", "x917",
                                    "x898", "x897", "x911", "x904", "x902")
round(CBI(mat), 2)
# results in book:
# 33, 2.75, 3.08, 0.91, 0.86, 0.82, 0.92, 0.53, 0.23, 0.03

simple_dom(mat2seq(mat)$winner, mat2seq(mat)$loser)

# example from Bang et al 2010 (table 1)
m <- c(0, 1, 0, 2,
       1, 0, 4, 0,
       2, 2, 0, 3,
       3, 0, 1, 0)
m <- matrix(m, ncol = 4, byrow = TRUE)
m <- t(m)
colnames(m) <- rownames(m) <- letters[1:4]
CBI(m)
# results in paper:
# 1.43, 1, 0.7, 1

# and from de Vries and Appleby (2000, table 4)
m <- c(0, 1, 1, 4, 0, 3, 6,
       0, 0, 1, 4, 0, 0, 0,
       0, 0, 0, 1, 1, 3, 14,
       0, 0, 0, 0, 2, 2, 1,
       0, 0, 0, 0, 0, 17, 2,
       0, 0, 0, 0, 0, 0, 12,
       0, 0, 0, 0, 0, 0, 0)
m <- matrix(m, ncol = 7, byrow = TRUE)
colnames(m) <- rownames(m) <- letters[1:7]
CBI(m)
simple_dom(mat2seq(m)$winner, mat2seq(m)$loser)