generate_simulated_data_from_estimated_model {PAFit}

Generating simulated data from a fitted model

Description

This function generates simulated networks from a fitted model and performs estimations on these simulated networks with the same setting used in the original estimation. Each simulated network is generated using parameters of the fitted model, while keeping other aspects of the growth process as faithfully as possible to the original observed network.

Usage

generate_simulated_data_from_estimated_model(net_object, net_stat, result, M = 5)

Arguments

net_object

an object of class PAFit_net that contains the original network.

net_stat

An object of class PAFit_data which contains summarized statistics of the original network. This object is created by the function get_statistics.

result

An object of class Full_PAFit_result which contains the fitted model obtained by applying the function joint_estimate.

M

integer. The number of simulated networks. Default value is 5.

Value

Outputs a Simulated_Data_From_Fitted_Model object, which is a list containing the following fields:

• graph_list: a list containing M simulated graphs.

• stats_list: a list containing M objects of class PAFit_data, which are the results of applying get_statistics on the simulated graphs.

• result_list: a list containing M objects of class Full_PAFit_result, which are the results of applying joint_estimate on the simulated graphs.

Author(s)

Thong Pham [e-mail]

References

1. Pham, T., Sheridan, P. & Shimodaira, H. (2015). PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks. PLoS ONE 10(9): e0137796. (doi:10.1371/journal.pone.0137796).

2. Pham, T., Sheridan, P. & Shimodaira, H. (2016). Joint Estimation of Preferential Attachment and Node Fitness in Growing Complex Networks. Scientific Reports 6, Article number: 32558. (doi:10.1038/srep32558).

3. Pham, T., Sheridan, P. & Shimodaira, H. (2020). PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks. Journal of Statistical Software 92 (3). (doi:10.18637/jss.v092.i03).

4. Inoue, M., Pham, T. & Shimodaira, H. (2020). Joint Estimation of Non-parametric Transitivity and Preferential Attachment Functions in Scientific Co-authorship Networks. Journal of Informetrics 14(3). (doi:10.1016/j.joi.2020.101042).

See Also

get_statistics, joint_estimate, plot_contribution

Examples

## Not run: 
  
  library("PAFit")
  net_object     <- generate_net(N = 500, m = 10, s = 10, alpha = 0.5)
  net_stat       <- get_statistics(net_object) 
  result         <- joint_estimate(net_object, net_stat)
  simulated_data <- generate_simulated_data_from_estimated_model(net_object, net_stat, result)
  plot_contribution(simulated_data, result, which_plot = "PA")
  plot_contribution(simulated_data, result, which_plot = "fit")
  
## End(Not run)