##Description
The “socialh” package is a set of functions developed to facilitate the establishment of the rank and social hierarchy for gregarious animals by the Si method developed by Kondo & Hurnik (1990). It is also possible to determine the number of agonistic interactions between two individuals, sociometric and dyadics matrix from dataset obtained through electronic bins.
##Function description
| Function | Description |
|---|---|
replacement |
Identify replacements between actor and reactor from electronic bins data. |
smatrix |
Build a square matrix contained dyadic frequency of dominance-related behaviors. |
dmatrix |
Determine the Sij dyadic dominance relationship from a sociomatrix. |
dvalue |
Determine the dominance value, social rank and hierarchy from Sij dyadic. |
landau_index |
Calculate the linearity index developed by Landau (1951). |
devries_index |
Calculate the linearity index improved by de Vries (1995). |
##Application
#First, install and load the socialh R package
install.packages(socialh)
library(socialh)
#Load the dataset
exemple.data <- read.csv(behaviour_data.csv)
# Apply the replacement(x, sec) function to create a data table with actor and reactor and save as an object to use later.
replace <- replacement (exemple.data, 14)
head(replace)
#Use the smatrix() function to create sociometrix by a replacemente data table and save as an object to use later.
social <- smatrix (replace)
head(social)
# actor
# reactor 2164251 2164252 2164255 2164259 2164261 2164263
# 2164251 0 32 62 17 37 23
# 2164252 43 0 10 19 8 14
# 2164255 56 12 0 7 26 16
# 2164259 15 5 10 0 3 10
# 2164261 34 9 37 6 0 15
# 2164263 26 16 16 11 8 0
#Apply the dmatrix()function to transform the sociometrix in a dyadic matrix and save as an object to use later.
dyadic <- dmatrix (social)
head(dyadic)
# actor
# reactor 2164251 2164252 2164255 2164259 2164261 2164263
# 2164251 0 -1 1 1 1 -1
# 2164252 1 0 -1 1 -1 -1
# 2164255 -1 1 0 -1 -1 0
# 2164259 -1 -1 1 0 -1 -1
# 2164261 -1 1 1 1 0 1
# 2164263 1 1 0 1 -1 0
#Employ the dvalue()function to determine dominance value, social rank and social hierarchy by a dyadic matrix.
dominance <- dvalue (dyadic)
head(dominance)
# dominance_value animal_id social_hierarchy social_rank
#1: -46 2164494 subordinate lower
#2: -37 2164490 subordinate lower
#3: -36 2164482 subordinate lower
#4: -30 2164477 subordinate lower
#5: -28 2164265 subordinate lower
#6: -27 2164529 subordinate lower
tail(dominance)
# dominance_value animal_id social_hierarchy social_rank
#1: 23 2164285 dominant high
#2: 26 2164381 dominant high
#3: 27 2164332 dominant high
#4: 29 2164308 dominant high
#5: 30 2164267 dominant high
#6: 35 2164321 dominant high
#Apply the landau_index()function to determine the linearity index by a dyadic matrix.
landau <- landau_index (dyadic)
print(landau)
#[1] 0.1743385
#Apply the devries_index()function to determine the improved linearity index by a dyadic matrix and a sociomatrix.
devries <- landau_index (dyadic, social)
print(devries)
#[1] 0.1754908