Nothing
## ----eval=FALSE----------------------------------------------------------
# install.packages("GERGM")
## ----eval=FALSE----------------------------------------------------------
# install.packages("devtools")
## ----eval=FALSE----------------------------------------------------------
# devtools::install_github("matthewjdenny/GERGM")
## ----eval=FALSE----------------------------------------------------------
# library(GERGM)
## ----eval=TRUE, fig.width=6, fig.height=6, fig.align ='center'-----------
library(GERGM)
set.seed(12345)
data("lending_2005")
data("covariate_data_2005")
data("net_exports_2005")
plot_network(lending_2005)
## ----eval=TRUE, fig.width=7, fig.height=5.5------------------------------
head(covariate_data_2005)
## ----eval=TRUE, echo=TRUE, fig.width=7, fig.height=3.5, results='hide', message=FALSE----
formula <- lending_2005 ~ edges + mutual(alpha = 0.8) + sender("log_GDP") +
receiver("log_GDP") + nodemix("G8", base = "No") + netcov(net_exports_2005)
## ----eval=TRUE, echo=TRUE, fig.width=8.5, fig.height=3.5, results='hide', message=FALSE, fig.align ='center'----
test <- gergm(formula,
covariate_data = covariate_data_2005,
number_of_networks_to_simulate = 40000,
thin = 1/100,
proposal_variance = 0.05,
MCMC_burnin = 10000,
seed = 456,
convergence_tolerance = 0.5)
## ----eval=FALSE----------------------------------------------------------
# # Generate Estimate Plot
# Estimate_Plot(test)
# # Generate GOF Plot
# GOF(test)
# # Generate Trace Plot
# Trace_Plot(test)
## ----eval=TRUE, echo=TRUE, fig.width=6.5, fig.height=3, results='hide', message=FALSE, fig.align ='center'----
Estimate_Plot(test,
coefficients_to_plot = "both",
coefficient_names = c("Mutual Dyads",
"log(GDP) Sender",
"log(GDP) Receiver",
"Non-G8 Sender, G8 Receiver",
"G8 Sender, Non-G8 Receiver",
"G8 Sender, G8 Receiver",
"intercept",
"Normalized Net Exports",
"Dispersion Parameter"),
leave_out_coefficients = "intercept")
## ----eval=TRUE, echo=TRUE, fig.width=5, fig.height=5, results='hide', message=FALSE, fig.align ='center'----
# Generate Hysteresis plots for all structural parameter estimates
hysteresis_results <- hysteresis(test,
networks_to_simulate = 1000,
burnin = 300,
range = 8,
steps = 20,
simulation_method = "Metropolis",
proposal_variance = 0.05)
## ----eval=TRUE, echo=TRUE, results='hide', message=FALSE-----------------
test2 <- conditional_edge_prediction(
GERGM_Object = test,
number_of_networks_to_simulate = 100,
thin = 1,
proposal_variance = 0.05,
MCMC_burnin = 100,
seed = 123)
## ----eval=TRUE-----------------------------------------------------------
MSE_results <- conditional_edge_prediction_MSE(test2)
## ----eval=FALSE----------------------------------------------------------
# set.seed(12345)
# # Function to generating a random positive-definite matrix with user-specified
# # positive eigenvalues. If eigenvalues are not specified, they are generated
# # from a uniform distribution.
# Posdef <- function (n, ev = runif(n, 0, 10)) {
# Z <- matrix(ncol=n, rnorm(n^2))
# decomp <- qr(Z)
# Q <- qr.Q(decomp)
# R <- qr.R(decomp)
# d <- diag(R)
# ph <- d / abs(d)
# O <- Q %*% diag(ph)
# Z <- t(O) %*% diag(ev) %*% O
# return(Z)
# }
#
# # Generate eigenvalues
# x <- rnorm(10)
# # generate a positive definite matrix
# pdmat <- Posdef(n = 10)
# # transform to correlations
# correlations <- pdmat / max(abs(pdmat))
# diag(correlations) <- 1
# net <- (correlations + t(correlations)) / 2
#
# # add in node names
# colnames(net) <- rownames(net) <- letters[1:10]
#
# # correlation GERGM specification
# formula <- net ~ edges + ttriads
#
# # model should run in under a minute
# test <- gergm(formula,
# estimation_method = "Metropolis",
# number_of_networks_to_simulate = 100000,
# thin = 1/100,
# proposal_variance = 0.2,
# MCMC_burnin = 100000,
# seed = 456,
# convergence_tolerance = 0.5,
# beta_correlation_model = TRUE)
## ----eval=FALSE----------------------------------------------------------
# formula <- lending_2005 ~ mutual(0.8) + ttriads(0.8) + out2stars(0.8) +
# sender("log_GDP") + netcov(net_exports_2005) +
# receiver("log_GDP") + nodemix("G8", base = "No")
#
#
# result <- gergm(formula,
# covariate_data = covariate_data_2005,
# number_of_networks_to_simulate = 100000,
# thin = 1/100,
# proposal_variance = 0.05,
# MCMC_burnin = 50000,
# seed = 456,
# convergence_tolerance = 0.8,
# target_accept_rate = 0.25)
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