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coor.test Monte Carlo randomization test to assess the statistical significance of singing coordinationcoor.test(X, iterations = 1000, less.than.chance = TRUE, parallel = FALSE)TRUE the test evaluates whether overlaps occur less often than expected by chance.
If FALSE the opposite pattern is evaluted (whether overlaps occur more often than expected by chance).
Default is TRUE.TRUE 2 cores are employed. If numeric, it specifies the number of cores to be used. Not available for windows OS.TRUE) of song overlaps in a coordinated singing event.
The function shuffles the sequences of signals and silence-between-signals for both individuals to produce
a null distribution of expected number of overlaps by chance. The observed number of overlaps is compared to this
expected values. The p-values are calculated as the proportion of random expected values that were lower (or higher)
than the observed value. The function runs one test for each singing event in the input data frame. The function
is equivalent to the "KeepGaps" methods described in Masco et al. 2015.#load simulated singing data (see data documentation)
data(coor.sing)
# testing if coordination happens less than expected by chance
coor.test(coor.sing, iterations = 1000, less.than.chance = T)
# testing if coordination happens more than expected by chance
coor.test(coor.sing, iterations = 1000, less.than.chance = F)Run the code above in your browser using DataLab