asnipe (version 1.1.17)

LRA: Dyadic Lagged Association Rate

Description

Calculate lagged association rate g(tau) from Whitehead (2008) for each dyad individually

Usage

LRA(group_by_individual, times, timejump, output_style = 1, 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, association_rate = TRUE)

Value

If output_style == 1 then a stack of matrices is returned that is N x N x tau. If output_style == 2 then a dataframe is returned containing the focal ID, associate, tau, and lagged association rate.

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

output_style

either 1 or 2, see details

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)

association_rate

calculate lagged rate of association (see details)

Author

Damien R. Farine

Details

Calculates the dyadic lagged association rate. The lagged rate of association incorporates the number of observations of each individuals as a simple ratio index within each time period, leading to a better estimation of the assocation rate for data where many observations of individuals can be made within a single time period.

References

Expanded from Whitehead (2008)

Examples

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

## calculate lagged association rate
lagged_rates <- LRA(gbi,times,3600, classes=inds$SPECIES, which_classes="GRETI", output_style=2)

## do something (run a model, plot a surface, etc..)