fergm: Estimation of Frailty Exponential Random Graph Model (FERGM)...

In benjamin-w-campbell/fergm: Estimation and Fit Assessment of Frailty Exponential Random Graph Models

Estimation of Frailty Exponential Random Graph Model (FERGM) via MPLE using Stan

Description

This function estimates a FERGM

Usage

fergm(net = NULL, form = NULL, seed = 12345, chains = 4, warmup = 100,
  iter = 600, cores = 1)

Arguments

net	A network object that is to be explained using the right_hand_vars argument.
form	A character string specified as "ergm.term1 + ergm.term2", must be terms supported for ERGMs.
seed	An integer that sets the seed for the random number generator to assist in replication. Defaults to 12345. Set to null to prevent internal seed setting.
chains	An integer that sets the number of Markov chains that should be used by Stan.
warmup	The number of warm up or burn-in iterations that should be used before posterior draws are taken. Defaults to 100.
iter	The number of total number of samples that should be taken including warm ups Defaults to 600 total iterations, leading to a posterior sample size of 500.
cores	The number of cores to used should parallel processing be desired. Defaults to 1.

Value

This function returns a list that includes the Stan output (stan.fit), the data object passed to Stan (stan.dta), and the original formula (form).

References

Box-Steffensmeier, Janet M., Dino P. Christenson, and Jason W. Morgan. 2018. “Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model." Political Analysis. (26)1:3-19.

Stan Development Team (2016). RStan: the R interface to Stan. R package version 2.14.1. http://mc-stan.org/.

Examples

## Not run: 
set.seed(1)

data("faux.mesa.high")

mesa <- faux.mesa.high

mesa.fit <- ergm::ergm(mesa ~ edges +
                  nodematch('Sex') +
                  nodematch('Grade', diff = FALSE) +
                  nodematch('Race', diff = FALSE) +
                  gwesp(decay = 0.2, fixed = TRUE) +
                  altkstar(lambda = 0.6, fixed = TRUE))

library(fergm)
form <- c("edges + nodematch('Sex') + nodematch('Grade', diff = FALSE) +
          nodematch('Race', diff = FALSE) +
          gwesp(decay = 0.2, fixed = TRUE) + altkstar(lambda = 0.6, fixed = TRUE)")

fergm.fit <- fergm(net = mesa, form = form, chains = 2)

## End(Not run)

benjamin-w-campbell/fergm documentation built on April 24, 2022, 6:40 a.m.