steeptest | R Documentation |

Estimates statistical significance for steepness measure on the basis of dyadic dominance indices corrected for chance *Dij* or based on proportions of wins *Pij*.

steeptest(X, rep, names=NULL, method=c("Dij","Pij"), order=TRUE)

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

`rep` |
Number of simulations for carrying out the randomization test. |

`names` |
Character vector with individuals' names. |

`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. |

`order` |
Logical, if TRUE, results for Dij, DS and NormDS are ordered according to the individuals' NormDS values. TRUE by default. |

`steeptest`

estimates statistical significance for steepness measures based on dyadic dominance index corrected for chance *Dij* or based on the matrix of win proportions *Pij*, depending on the `method`

specified. 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* or *Pij*. Specifically, it computes normalized David's scores, 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 when results are shown by means of `summary`

method:
*p=NS+1/NOS+1*
Where *NS* is computed as:

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

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.

`steeptest`

returns an object of class steeptest containing the following components:

`call ` |
Function call. |

`names` |
Character vector with individuals' names. |

`method` |
A character string indicating which dyadic dominance measure is used for the computation of David's scores. |

`rep` |
Number of simulations for carrying out the randomization test. |

`matdom` |
If |

`DS` |
David's scores based on |

`NormDS` |
Normalized David's scores based on dyadic dominance indices corrected for chance or on proportions of wins in dyadic encounters. |

`Stp` |
Steepness value based on Normalized David's scores. |

`interc` |
Intercept of the fitted line based on Normalized David's scores. |

`Stpsim` |
The function provides results of the randomization procedure for the steepness measure based on |

David Leiva dleivaur@ub.edu & Han de Vries J.deVries1@uu.nl.

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.

`getDij`

, `getPij`

, `getNormDS`

############################################################################## ### 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") STP <- steeptest(X, rep=9999, names=individuals, method="Dij", order=TRUE) summary(STP) plot(STP)

steepness documentation built on May 6, 2022, 9:07 a.m.

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