LAR function

Mean Lagged Association Rate

Calculate lagged association rate g(tau) from Whitehead (2008)

LAR(group_by_individual, times, timejump, min_time = NULL, max_time = NULL, 
        identities = NULL, which_identities = NULL, locations = NULL, 
        which_locations = NULL, start_time = NULL, end_time = NULL, classes = NULL, 
        which_classes = NULL)

Arguments

• group_by_individual: a K x N matrix of K groups (observations, gathering events, etc.) and N individuals (all individuals that are present in at least one group)
• times: K vector of times defining the middle of each group/event
• timejump: step length for tau
• min_time: minimum/starting value of tau
• max_time: maximum/ending value of tau
• identities: N vector of identifiers for each individual (column) in the group by individual matrix
• which_identities: vector of identities to include in the network (subset of identities)
• locations: K vector of locations defining the location of each group/event
• which_locations: vector of locations to include in the network (subset of locations)
• start_time: element describing the starting time for inclusion in the network (useful for temporal analysis)
• end_time: element describing the ending time for inclusion in the network (useful for temporal analysis)
• classes: N vector of types or class of each individual (column) in the group by individual matrix (for subsetting)
• which_classes: vector of class(es)/type(s) to include in the network (subset of classes)

Details

Calculate the lagged association rate for given timesteps.

Returns

Returns a matrix with Log(time) in the first column and the lagged association rate in the second

References

Whitehead (2008) Analyzing Animal Societies section 5.5.1

Author(s)

Damien R. Farine

Examples

data("group_by_individual")
data("times")
data("individuals")

## calculate lagged association rate for great tits
lagged_rates <- LAR(gbi,times,3600, classes=inds$SPECIES, which_classes="GRETI")

## plot the results
plot(lagged_rates, type='l', axes=FALSE, xlab="Time (hours)", ylab="LAR", ylim=c(0,1))
axis(2)
axis(1, at=lagged_rates[,1], labels=c(1:nrow(lagged_rates)))