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나는 이항식과 pymc의 문제로 어려움을 겪고있다. 나는 그룹으로 나누어 진 표본을 가지고 있는데, MCMC를 사용하여 감수성 상태에서 감염까지의 전이 률을 평가하고 유사한 결과를 얻고 싶다. here플롯 팅 이항 MCMC
내가 스크립트를 컴파일에 나는이 메시지를 얻을 :
Traceback (most recent call last):
File "statisticMCMC_bin.py", line 23, in <module>
plot(mc.finalhcc.stats()['mean'],color='red',linewidth=2)
File "/Library/Python/2.7/site-packages/pymc-2.3a-py2.7-macosx-10.8-intel.egg/pymc/Node.py", line 265, in stats
return self.trace.stats(alpha=alpha, start=start, batches=batches,
AttributeError: 'Binomial' object has no attribute 'trace'
을 더 플롯이 생성되지 않습니다 ..... 나는 그것을 어떻게 해결할 수 있습니까?
import sys
import pickle
import pykov
import random
import scipy.integrate as spi
import numpy as np
import pylab as pl
import math as mt
import scipy.linalg as linear
import decimal
from pymc import *
import numpy as np
n = np.array([647,1814,8838,9949,1920])###initial population
originalHCC=np.array([0,197,302,776,927], dtype=float)
beta=Uniform('beta',0.001,1.0)####death rate
vectorp=np.array([beta,beta,beta,beta,beta]);
finalhcc = pymc.Binomial('finalhcc', n=n, p=vectorp, value=originalHCC, observed=True)
import numpy as np
from pymc import *
from pylab import *
import scipy as sc
#from pymc.Matplot import plot
from scipy.stats.mstats import mquantiles
import MCMC_bin as mod
reload(mod)
mc=MCMC(mod)
mc.use_step_method(AdaptiveMetropolis, [mod.beta])
mc.sample(iter=500000,burn=5000, thin=20,verbose=1)
n = np.array([647,1814,8838,9949,1920,39])
figure(1)
title('HCC with uncertainty')
plot(mc.originalHCC, 's', mec='black', color='black',alpha=0.9)
plot(mc.finalhcc.stats()['mean'],color='red',linewidth=2)
plot(mc.finalhcc.stats()['95% HPD interval'],color='red',linewidth=1,linestyle='dotted')
axis(0,6,0.9*min(mc.originalHCC),1.2*max(mc.originalHCC))
savefig('HCC.png')