asnipe (version 1.1.17)

LAR: Mean Lagged Association Rate

Description

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

Usage

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)

Value

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

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)

Author

Damien R. Farine

Details

Calculate the lagged association rate for given timesteps.

References

Whitehead (2008) Analyzing Animal Societies section 5.5.1

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)))