Description Usage Arguments Value Examples
Generate timeevolving outcomes where outcomes at time t of i depends on outcomes of i's adjacent peers at time t1.
1  peer.process(A, max.time = 3, mprob = 0.5, epsilon = 0.3)

A 

max.time 
the maximum discrete time that direct transmission occurs. 
mprob 
the maximum susceptibility probability, i.e. maximum probability that i's outcome at time t depends on i's peers at time t1. 
epsilon 
standard deviation of error process. This adds uncertainties in outcomes. For t=1,2, ... p ~ Unif(0, mprob) Y^t_i= (1  p)Y^{t1}_i + p ∑_{j} A_ij Y^{t1}_j / ∑_{j} A_ij + N(0, ε) 
a list of timeevolving outcomes from time0
to time(max.time)
.
1 2 3 4 5 6  library(netdep)
library(igraph)
library(stats)
G = latent.netdep(n.node = 100, rho = 0.2)
A = as.matrix(get.adjacency(G))
outcomes = peer.process(A, max.time = 3, mprob = 0.3, epsilon = 0.5)

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