getting started vignette

inst/doc/getting_started.R

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+## ----eval=FALSE----------------------------------------------------------
+#  install.packages("GERGM")
+
+## ----eval=FALSE----------------------------------------------------------
+#  install.packages("devtools")
+
+## ----eval=FALSE----------------------------------------------------------
+#  devtools::install_github("matthewjdenny/GERGM")
+
+## ----eval=FALSE----------------------------------------------------------
+#  install.packages( pkgs = c("BH","RcppArmadillo","ggplot2","methods",
+#      "stringr","igraph", "plyr", "parallel", "coda", "vegan", "scales",
+#  	  "RcppParallel","slackr"), dependencies = TRUE)
+
+## ----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, 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----------------------------------------------------------
+#  formula <- net ~  mutual(0.8) + ttriads(0.8) + out2stars(0.8) +
+#    sender("log_GDP") + netcov(net_exports) +
+#    receiver("log_GDP") + nodemix("G8", base = "No")
+#  
+#  
+#  result <- gergm(formula,
+#                covariate_data = covariate_data_2005,
+#                number_of_networks_to_simulate = 400000,
+#                thin = 1/100,
+#                proposal_variance = 0.05,
+#                MCMC_burnin = 200000,
+#                seed = 456,
+#                convergence_tolerance = 0.8,
+#                hyperparameter_optimization = TRUE,
+#                target_accept_rate = 0.25,
+#                weighted_MPLE = TRUE,
+#                theta_grid_optimization_list = list(grid_steps = 2,
+#                                                    step_size = 0.1,
+#                                                    cores = 30,
+#                                                    iteration_fraction = 1))
+