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

In srdyal/diffusiontest: Analysis of Diffusion and Contagion Processes on Networks

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)

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.

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.

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.

Author(s)

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

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)

srdyal/diffusiontest documentation built on Dec. 9, 2024, 1:14 a.m.