View source: R/compare_predictions.R
compare_predictions | R Documentation |
This function allows you to assess the importance of the frailty term in prediction by comparing the predictive accuracy of an ERGM to an FERGM. Note: Prior to estimating this function, ensure the network object of interest is saved to the global environment and named "net."
compare_predictions(ergm.fit = NULL, fergm.fit = NULL, seed = NULL, replications = 500)
ergm.fit |
A model object returned by the |
fergm.fit |
A model object returned by the |
seed |
An integer that sets the seed for the random number generator to assist in replication. Defaults to a null value for no seed setting. |
replications |
The number of networks to be simulated to assess predictions. Defaults to 500. |
The compare_predictions function returns a matrix reflecting the number of correctly predicted ties for the ERGM and FERGM for each network simulated.
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/.
# load example data library(fergm) data("ergm.fit") data("fergm.fit") # Use built in compare_predictions function to compare predictions of ERGM and FERGM, # few replications due to example # Make sure "net" is an object defined in the global environment. net <- ergm.fit$network predict_out <- compare_predictions(ergm.fit = ergm.fit, fergm.fit = fergm.fit, replications = 10, seed=12345) # 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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