netdiffuseR (version 1.23.0)

toa_diff: Difference in Time of Adoption (TOA) between individuals

Description

Creates an \(n \times n\) matrix, or for \(Q\) behaviors, a list of length \(Q\) containing \(n \times n\) matrices, that indicates the difference in adoption times between each pair of nodes.

Usage

toa_diff(obj, t0 = NULL, labels = NULL)

Value

An \(n \times n\) anti-symmetric matrix (or a list of them, for \(Q\) behaviors) indicating the difference in times of adoption between each pair of nodes.

Arguments

obj

Either an integer vector of length \(n\) containing time of adoption of the innovation, a matrix of size \(n \times Q\) (for multiple \(Q\) behaviors), or a diffnet object (both for single or multiple behaviors).

t0

Integer scalar. Sets the lower bound of the time window (e.g. 1955).

labels

Character vector of length \(n\). Labels (ids) of the vertices.

Author

George G. Vega Yon, Thomas W. Valente, and Aníbal Olivera M.

Details

Each cell \(ij\) of the resulting matrix is calculated as \(toa_j - toa_i\), so that whenever its positive it means that the j-th individual (alter) adopted the innovation sooner.

Examples

# For a single behavior -----------------------------------------------------

# Generating a random vector of time
set.seed(123)
times <- sample(2000:2005, 10, TRUE)

# Computing the TOA differences
toa_diff(times)

# For Q=2 behaviors ---------------------------------------------------------

# Generating a matrix time

times_1 <- c(2001L, 2004L, 2003L, 2008L)
times_2 <- c(2001L, 2005L, 2006L, 2008L)
times <- matrix(c(times_1, times_2), nrow = 4, ncol = 2)

# Computing the TOA differences
toa_diff(times)

# Or, from a diffnet object

graph <- lapply(2001:2008, function(x) rgraph_er(4))
diffnet <- new_diffnet(graph, times)

# Computing the TOA differences
toa_diff(diffnet)