getDS {steepness}

David's scores -DS-

Description

Function to obtain David's scores from the observed sociomatrix.

Usage

   getDS(X, names=NULL, method=c("Dij","Pij"))

Arguments

X

Empirical sociomatrix containing wins-losses frequencies in dyadic encounters. The matrix must be square and numeric.

names

Character vector with the names of individuals. This vector is NULL by default

method

A character string indicating which dyadic dominance measure is to be used for the computation of David's scores. One of "Dij" or "Pij", can be abbreviated.

Details

getDS is obtained by means of the following expression: DS = w1 + w2 - l1 - l2 where w1 is the sum of i's Dij or Pij values (depending on the method specification); w2 is the weighted sum of i‘s dyadic dominance indices corrected for chance or the weighted sum of i’s win proportions; l1 is the sum of i's Dji or Pji values and l2 is the sum of i's dyadic lose indices corrected for chance or the weighted sum of i's lose proportions.

Value

DS

David's scores based on dyadic dominance indices corrected for chance or on win proportions.

Author(s)

David Leiva [e-mail] & Han de Vries [e-mail].

References

David, H. A. (1988). The Method of Paired Comparisons. London: C. Griffin.

de Vries, H., Stevens, J. M. G., & Vervaecke, H. (2006). Measuring and testing the steepness of dominance hierarchies. Animal Behaviour, 71, 585-592.

See Also

getDij, getPij, getwl.

Examples

##############################################################################
###               Example taken from Vervaecke et al. (2007):              ###
##############################################################################

X <- matrix(c(0,58,50,61,32,37,29,39,25,8,0,22,22,9,27,20,10,48,
              3,3,0,19,29,12,13,19,8,5,8,9,0,33,38,35,32,57,
              4,7,9,1,0,28,26,16,23,4,3,0,0,6,0,7,6,12,
              2,0,4,1,4,4,0,5,3,0,2,1,1,5,8,3,0,10,3,1,3,0,0,4,1,2,0),
              nrow=9,byrow=TRUE)

individuals <- c("V","VS","B","FJ","PR","VB","TOR","MU","ZV")

res <- getDS(X,names=individuals,method="Dij")

print(res,digits=3)