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## ---- eval = FALSE-------------------------------------------------------
# # Load statnet which contains the ergm package and the faux.mesa.high network.
# library(statnet)
#
# # Set seed for replication
# set.seed(1)
#
# # Load faux.mea.high daa
# data("faux.mesa.high")
#
# # Rename the object
# mesa <- faux.mesa.high
## ---- echo = TRUE, eval = FALSE------------------------------------------
# # ERGM fit
# ergm.fit <- 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))
#
# # FERGM fit
# 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 = 1)
#
## ---- echo = TRUE, eval = FALSE------------------------------------------
# # Conventional rstan approach to extracting posterior summary
# stan.smry <- summary(fergm.fit$stan.fit)$summary
# beta_df <- stan.smry[grep("beta", rownames(stan.smry)),]
# est <- round(beta_df[,c(1,4,8)], 3)
# est # in order of "form"
#
# # fergm built-in function to summarize posteior
# est <- clean_summary(fergm.fit)
# est <- clean_summary(fergm.fit,
# custom_var_names = c("Edges", "Sex Homophily",
# "GradeHomophily", "Race Homophily",
# "GWESP", "Alternating K-Stars"))
# est
# # Compare substantive implications via coef plot, these are with 95% credible intervals
# coef_plot(fergm.fit = fergm.fit)
#
# coef_plot(fergm.fit = fergm.fit,
# custom_var_names = c("Edges", "Sex Homophily", "Grade Homophily",
# "Race Homophily", "GWESP", "Alternating K-Stars"))
# coef_plot(fergm.fit = fergm.fit,
# ergm.fit = ergm.fit,
# custom_var_names = c("Edges", "Sex Homophily", "Grade Homophily",
# "Race Homophily", "GWESP", "Alternating K-Stars"))
#
#
# # You can also look at the density of particular variables using the following:
#
# densities <- coef_posterior_density(fergm.fit = fergm.fit)
#
# densities <- coef_posterior_density(fergm.fit = fergm.fit,
# custom_var_names = c("Edges", "Sex Homophily",
# "Grade Homophily", "Race Homophily",
# "GWESP", "Alternating K-Stars"))
# densities[[1]]
# densities[[2]]
## ---- echo = TRUE, eval = FALSE------------------------------------------
# # Use rstan functions to assess whether chains have evidence of converging
# trace <- rstan::traceplot(fergm.fit$stan.fit, pars = "beta")
# trace
#
# # We have our own version that includes variable names and tidies it up a bit
# fergm_beta_traceplot(fergm.fit)
#
# fergm_beta_traceplot(fergm.fit,
# custom_var_names = c("Edges", "Sex Homophily",
# "Grade Homophily", "Race Homophily",
# "GWESP", "Alternating K-Stars"))
## ---- echo = TRUE, eval = FALSE------------------------------------------
# # Use fergm built in compare_predictions function to compare predictions of ERGM and FERGM
# net <- ergm.fit$network
# predict_out <- compare_predictions(ergm.fit = ergm.fit, fergm.fit = fergm.fit, replications = 100)
#
# # Use the built in compare_predictions_plot function to examine the densities of correctly predicted
# # ties from the compare_predictions simulations
# compare_predictions_plot(predict_out)
#
# # We can also conduct a KS test to determine if the FERGM fit it statistically disginguishable
# # from the ERGM fit
# compare_predictions_test(predict_out)
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