rgraph {sna} | R Documentation |
rgraph
generates random draws from a Bernoulli graph distribution, with various parameters for controlling the nature of the data so generated.
rgraph(n, m=1, tprob=0.5, mode="digraph", diag=FALSE, replace=FALSE, tielist=NULL)
n |
The size of the vertex set (|V(G)|) for the random graphs |
m |
The number of graphs to generate |
tprob |
Information regarding tie (edge) probabilities; see below |
mode |
``digraph'' for directed data, ``graph'' for undirected data |
diag |
Should the diagonal entries (loops) be set to zero? |
replace |
Sample with or without replacement from a tie list (ignored if tielist==NULL |
tielist |
A vector of edge values, from which the new graphs should be bootstrapped |
rgraph
is a reasonably versatile routine for generating random network data. The graphs so generated are either Bernoulli graphs (graphs in which each edge is a Bernoulli trial, independent conditional on the Bernoulli parameters), or are bootstrapped from a user-provided edge distribution (very handy for CUG tests). In the latter case, edge data should be provided using the tielist
argument; the exact form taken by the data is irrelevant, so long as it can be coerced to a vector. In the former case, Bernoulli graph probabilities are set by the tprob
argument as follows:
tprob
contains a single number, this number is used as the probability of all edges.
tprob
contains a vector, each entry is assumed to correspond to a separate graph (in order). Thus, each entry is used as the probability of all edges within its corresponding graph.
tprob
contains a matrix, then each entry is assumed to correspond to a separate edge. Thus, each entry is used as the probability of its associated edge in each graph which is generated.
tprob
contains a three-dimensional array, then each entry is assumed to correspond to a particular edge in a particular graph, and is used as the associated probability parameter.
Finally, note that rgraph
will symmetrize all generated networks if mode
is set to ``graph'' by copying down the upper triangle. The lower half of tprob
, where applicable, must still be specified, however.
A graph stack
Carter T. Butts buttsc@uci.edu
Wasserman, S., and Faust, K. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.
#Generate three graphs with different densities g<-rgraph(10,3,tprob=c(0.1,0.9,0.5)) #Generate from a matrix of Bernoulli parameters g.p<-matrix(runif(25,0,1),nrow=5) g<-rgraph(5,2,tprob=g.p)