Econometric methods for the analysis of networks.
by Bryan S. Graham, UC - Berkeley, e-mail: firstname.lastname@example.org
This package includes a Python 2.7 implementation of the two econometric network formation models introduced in Graham (2014, NBER).
This package is offered “as is”, without warranty, implicit or otherwise. While I would appreciate bug reports, suggestions for improvements and so on, I am unable to provide any meaningful user-support. Please e-mail me at email@example.com
Please cite both the code and the underlying source articles listed below when using this code in your research.
A simple example script to get started is:
>>>> # Import numpy in order to correctly read test data >>>> import numpy as np >>>> # Import urllib in order to download test data from Github repo >>>> import urllib >>>> # Append location of netrics module base directory to system path >>>> # NOTE: only required if permanent install not made >>>> # NOTE: edit path to location on netrics package on local machine >>>> import sys >>>> sys.path.append('/Users/bgraham/Dropbox/Sites/software/netrics/') >>>> # Load netrics module >>>> import netrics as netrics >>>> # Download Nyakatoke test dataset from GitHub >>>> download = '/Users/bgraham/Dropbox/' # Edit to location on your machine >>>> url = 'https://github.com/bryangraham/netrics/blob/master/Notebooks/Nyakatoke_Example.npz?raw=true' >>>> urllib.urlretrieve(url, download + "Nyakatoke_Example.npz") >>>> # Open dataset >>>> NyakatokeTestDataset = np.load(download + "Nyakatoke_Example.npz") >>>> # Extract adjacency matrix >>>> D = NyakatokeTestDataset['D'] >>>> # Initialize list of dyad-specific covariates as elements >>>> # W = [W0, W1, W2,...WK-1] >>>> W =  >>>> # Initialize list with covariate labels >>>> cov_names =  >>>> # Construct list of regressor matrices and corresponding variable names >>>> for matrix in NyakatokeTestDataset.files: >>>> if matrix != 'D': >>>> W.append(NyakatokeTestDataset[matrix]) >>>> cov_names.append(matrix) >>>> # Apply tetrad logit procedure to dataset >>>> [beta_TL, vcov_beta_TL, tetrad_frac_TL, success] = \ netrics.tetrad_logit(D, W, dtcon=None, silent=False, W_names=cov_names)