steepness (version 0.1)

steeptest: Statistical significance for steepness of dominance hierarchies statistic

Description

Estimates statistical significance for steepness measure based on dyadic dominance indices corrected for chance Dij.

Usage

steeptest(X, rep, names=NULL, option.console=FALSE)

Arguments

X

Original sociomatrix.

rep

Number of simulations for carrying out the randomization test.

names

Character vector with individuals' names.

option.console

Logical, if TRUE, results of the procedure will be printed in R console. FALSE by default.

Value

• steeptest returns a list containing the following components:
• dyadic.dominance
  Matrix of observed dyadic dominance indices corrected for chance.
• david.scores
  David's scores based on dyadic dominance indices corrected for chance.
• norm.david.scores
  Normalized David's scores based on proportions of wins in dyadic encounters.
• steepness
  Steepness value based on Normalized David's scores.
• steep.right.pvalue
  Statistical significance for the empirical steepness value under the null hypothesis of random wins.
• steep.left.pvalue
  Statistical significance for the empirical steepness value under the null hypothesis of random wins.
• intercept
  Intercept of the fitted line based on Normalized David's scores.
• results
  The function provides results of the randomization procedure for the steepness measure based on NormDS.

Details

steeptest estimates statistical significance for steepness measures based on dyadic dominance index corrected for chance Dij. This procedure simulates a number of sociomatrices under a uniform distribution by means of callings to C routine steep, then computes steepness based on Dij. Specifically, it computes normalized David's scores based on Dij, see getNormDS for more details. Then it computes the steepness measure based on these indices, see getStp. After rep simulations the sampling distribution for the statistic (Stp) is estimated. Then statistical significance is computed as follows: $p=NS+1/NOS+1$ Where NS is computed as:

1. The number of times that simulated values are greater than or equal to the empirical value, if right-tailed p value is calculated.
2. And the number of times that simulated values are lower than or equal to the empirical value, if left-tailed p value is calculated.

And NOS represents the number of simulated values.

References

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, getNormDS

Examples

X <- matrix(c(0, 1, 2, 1, 0, 1, 3, 1, 0),nrow=3,byrow=TRUE)
  rep <- 10000
  names <- c("Ag1","Ag2","Ag3")