# A simple toy example:
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
# The same example using MCMC MLE:
fit2 <- mtergm(networks ~ edges + istar(2) +
edgecov(covariates))
summary(fit2)
# For an example with real data, see help("knecht").
# Examples for parallel processing:
# Some preliminaries:
# - "Forking" means running the code on multiple cores in the same
# computer. It's fast but consumes a lot of memory because all
# objects are copied for each node. It's also restricted to
# cores within a physical computer, i.e. no distribution over a
# network or cluster. Forking does not work on Windows systems.
# - "MPI" is a protocol for distributing computations over many
# cores, often across multiple physical computers/nodes. MPI
# is fast and can distribute the work across hundreds of nodes
# (but remember that R can handle a maximum of 128 connections,
# which includes file access and parallel connections). However,
# it requires that the Rmpi package is installed and that an MPI
# server is running (e.g., OpenMPI).
# - "PSOCK" is a TCP-based protocol. It can also distribute the
# work to many cores across nodes (like MPI). The advantage of
# PSOCK is that it can as well make use of multiple nodes within
# the same node or desktop computer (as with forking) but without
# consuming too much additional memory. However, the drawback is
# that it is not as fast as MPI or forking.
# The following code provides examples for these three scenarios.
# btergm works with clusters via the parallel package. That is, the
# user can create a cluster object (of type "PSOCK", "MPI", or
# "FORK") and supply it to the 'cl' argument of the 'btergm'
# function. If no cluster object is provided, btergm will try to
# create a temporary PSOCK cluster (if parallel = "snow") or it
# will use forking (if parallel = "multicore").
# To use a PSOCK cluster without providing an explicit cluster
# object:
require("parallel")
fit <- btergm(networks ~ edges + istar(2) + edgecov(covariates),
R = 100, parallel = "snow", ncpus = 25)
# Equivalently, a PSOCK cluster can be provided as follows:
require("parallel")
cores <- 25
cl <- makeCluster(cores, type = "PSOCK")
fit <- btergm(networks ~ edges + istar(2) + edgecov(covariates),
R = 100, parallel = "snow", ncpus = cores, cl = cl)
stopCluster(cl)
# Forking (without supplying a cluster object) can be used as
# follows.
require("parallel")
cores <- 25
fit <- btergm(networks ~ edges + istar(2) + edgecov(covariates),
R = 100, parallel = "multicore", ncpus = cores)
stopCluster(cl)
# Forking (by providing a cluster object) works as follows:
require("parallel")
cores <- 25
cl <- makeCluster(cores, type = "FORK")
fit <- btergm(networks ~ edges + istar(2) + edgecov(covariates),
R = 100, parallel = "snow", ncpus = cores, cl = cl)
stopCluster(cl)
# To use MPI, a cluster object MUST be created beforehand. In
# this example, a MOAB HPC server is used. It stores the number of
# available cores as a system option:
require("parallel")
cores <- as.numeric(Sys.getenv("MOAB_PROCCOUNT"))
cl <- makeCluster(cores, type = "MPI")
fit <- btergm(networks ~ edges + istar(2) + edgecov(covariates),
R = 100, parallel = "snow", ncpus = cores, cl = cl)
stopCluster(cl)
# In the following example, the Rmpi package is used to create a
# cluster. This may not work on all systems; consult your local
# support staff or the help files on your HPC server to find out how
# to create a cluster object on your system.
# snow/Rmpi start-up
if (!is.loaded("mpi_initialize")) {
library("Rmpi")
}
library(snow);
mpirank <- mpi.comm.rank (0)
if (mpirank == 0) {
invisible(makeMPIcluster())
} else {
sink (file="/dev/null")
invisible(slaveLoop (makeMPImaster()))
mpi.finalize()
q()
}
# End snow/Rmpi start-up
cl <- getMPIcluster()
fit <- btergm(networks ~ edges + istar(2) + edgecov(covariates),
R = 100, parallel = "snow", ncpus = 25, cl = cl)Run the code above in your browser using DataLab