Talk:Hypergamy: Difference between revisions

Talk:Hypergamy (edit)

925 bytes added , 24 September 2021

398

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 [TBD]
+== Modeling Hypergamy through programming ==
+Using Hinge data as a strating point, one can demonstrate the basic regression of hypergamy. Rhode's coefficient at 1.08 and C-coefficient at 9.607 are better curve-fit than pareto's index being 1.398.
+<code>
+from numpy import exp
+def rhode(x,b): return x*(b-1)/(b-x)
+def chotikapanich(x,b): return (exp(b*x)-1)/(exp(b)-1)
+def pareto(x,b): return 1-(1-x)**(1-1/b)
+import matplotlib.pyplot as plt
+from scipy.optimize import curve_fit
+from numpy import array
+xdata = array([0,0.5,0.9,0.95,0.99,1])
+ydata = array([0,0.043,0.42,0.589,0.836,1])
+def demo(func):
+  plt.plot(xdata, ydata, 'b-', label='data')
+  popt, pcov = curve_fit(func, xdata, ydata, bounds=(0, 1000))
+  plt.plot(xdata, func(xdata, *popt), 'r-',
+          label='fit: b=%5.3f' % tuple(popt))
+  plt.xlabel('x')
+  plt.ylabel('y')
+  plt.legend()
+  plt.show()
+</code>
+demo(rhode)
+demo(chotikapanich)
+demo(pareto)