import pylab as pl import pymc as mc import models import graphics # make model vars = models.nonlinear() #vars['beta'].value = [10, -9, 15] # carefully choosen initial value, for demonstration purposes only m = mc.MCMC(vars) m.sample(iter=20000, burn=10000, thin=10) # display results pl.figure(figsize=(12,9)) graphics.plot_2005_data() graphics.plot_nonlinear_model(m) pl.savefig('../tex/ex2.png')
# make and fit model twice vars = models.nonlinear() vars['gamma'].value = .95 vars['beta'].value = [ 10, -10, 15 ] # carefully choosen initial value, for demonstration purposes only m1 = mc.MCMC(vars) m1.sample(iter=20000, burn=10000, thin=10) vars = models.nonlinear() vars['gamma'].value = .95 vars['beta'].value = [8, -8, 10] # as above m2 = mc.MCMC(vars) m2.sample(iter=20000, burn=10000, thin=10) # display results pl.figure(figsize=(12, 9)) graphics.plot_2005_data() graphics.plot_nonlinear_model(m1, color='g', label='Replicate 1') pl.axis([.8, .99, 1., 3.]) pl.savefig('../tex/ex3_a.png') graphics.plot_nonlinear_model(m2, color='b', label='Replicate 2') pl.axis([.8, .99, 1., 3.]) pl.savefig('../tex/ex3_b.png') # explore model convergence mc.Matplot.plot(m1, path='../tex/ex3')
import pylab as pl import pymc as mc import models import graphics # make model vars = models.nonlinear() #vars['beta'].value = [10, -9, 15] # carefully choosen initial value, for demonstration purposes only m = mc.MCMC(vars) m.sample(iter=20000, burn=10000, thin=10) # display results pl.figure(figsize=(12, 9)) graphics.plot_2005_data() graphics.plot_nonlinear_model(m) pl.savefig('../tex/ex2.png')
import graphics # make and fit model twice vars = models.nonlinear() vars['gamma'].value = .95 vars['beta'].value = [10, -10, 15] # carefully choosen initial value, for demonstration purposes only m1 = mc.MCMC(vars) m1.sample(iter=20000, burn=10000, thin=10) vars = models.nonlinear() vars['gamma'].value = .95 vars['beta'].value = [8, -8, 10] # as above m2 = mc.MCMC(vars) m2.sample(iter=20000, burn=10000, thin=10) # display results pl.figure(figsize=(12,9)) graphics.plot_2005_data() graphics.plot_nonlinear_model(m1, color='g', label='Replicate 1') pl.axis([.8, .99, 1., 3.]) pl.savefig('../tex/ex3_a.png') graphics.plot_nonlinear_model(m2, color='b', label='Replicate 2') pl.axis([.8, .99, 1., 3.]) pl.savefig('../tex/ex3_b.png') # explore model convergence mc.Matplot.plot(m1, path='../tex/ex3')