Description Details Author(s) Examples
Extensions of Exponential Random Graph Models (ERGM).
The xergm package implements extensions of exponential random
graph models, in particular Temporal ERGMs (btergm),
Generalized ERGMs (GERGM), Temporal Network Autocorrelation
Models (tnam), and Relational Event Models. This package acts
as a meta-package for the packages btergm, GERGM,
tnam, and rem. To display citation information, type
citation("xergm")
.
Philip Leifeld (http://www.philipleifeld.com)
Skyler J. Cranmer (http://www.skylercranmer.net)
Bruce A. Desmarais (https://sites.psu.edu/desmaraisgroup/)
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# example 1: temporal exponential random graph model (see ?btergm)
library("statnet")
set.seed(5)
networks <- list()
for(i in 1:10){ # create 10 random networks with 10 actors
mat <- matrix(rbinom(100, 1, .25), nrow = 10, ncol = 10)
diag(mat) <- 0 # loops are excluded
nw <- network(mat) # create network object
networks[[i]] <- nw # add network to the list
}
covariates <- list()
for (i in 1:10) { # create 10 matrices as covariate
mat <- matrix(rnorm(100), nrow = 10, ncol = 10)
covariates[[i]] <- mat # add matrix to the list
}
fit <- btergm(networks ~ edges + istar(2) +
edgecov(covariates), R = 100)
summary(fit) # show estimation results
# example 2: temporal network autocorrelation model (see ?tnam)
data("knecht")
delinquency <- as.data.frame(delinquency)
rownames(delinquency) <- letters
friendship[[3]][friendship[[3]] == 10] <- NA
friendship[[4]][friendship[[4]] == 10] <- NA
for (i in 1:length(friendship)) {
rownames(friendship[[i]]) <- letters
}
sex <- demographics$sex
names(sex) <- letters
sex <- list(t1 = sex, t2 = sex, t3 = sex, t4 = sex)
religion <- demographics$religion
names(religion) <- letters
religion <- list(t1 = religion, t2 = religion, t3 = religion,
t4 = religion)
model1 <- tnam(
delinquency ~
covariate(sex, coefname = "sex") +
covariate(religion, coefname = "religion") +
covariate(delinquency, lag = 1, exponent = 1) +
netlag(delinquency, friendship) +
netlag(delinquency, friendship, pathdist = 2, decay = 1) +
netlag(delinquency, friendship, lag = 1) +
degreedummy(friendship, deg = 0, reverse = TRUE) +
centrality(friendship, type = "betweenness"),
re.node = TRUE, time.linear = TRUE
)
summary(model1)
## End(Not run)
|
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