def _sample_variance_components(self):
## Sample sigma2_u
first = self.p
for i in range(0, self.c):
prior = self.priors_sigma2u[i]
qi = self.qi[i] # size of random vector ui
df = prior["df"] + qi
# print 'num_fixed: '+str(self.num_fixed)+' num_locations: '+ str(self.num_locations)
# print 'theta:'+str(np.shape(self.theta))
ui = self.theta[first : first + qi, 0]
first += qi
# print np.shape(u)
# print np.shape(self.invG)
try:
scale1 = (ui.T * self.Ginvs[i] * ui)[0, 0]
# print 'u.T * invG * u = ' + str(scale0)
scale = (scale1 + prior["df"] * prior["scale"]) / df
# print 'scale = ' + str(scale) + ' scalebig = ' + str(scalebig)
self.sigma2_u[i, 0] = gt.rchisquare_inv_scaled(df, scale)
# print 'sigma_b: '+str(x)+ ' df='+str(df)+' scale='+str(scale)
except Exception, e:
print "Error while sampling sigma2_u"
print "df=" + str(df) + " scale=" + str(scale)
print e
raise e
def _sample_variance_components(self):
## Sample sigma2_b
df = self.prior_b['df'] + self.num_random
#print 'num_fixed: '+str(self.num_fixed)+' num_locations: '+ str(self.num_locations)
#print 'theta:'+str(np.shape(self.theta))
u = self.theta[self.num_fixed:self.num_locations,0]
#print np.shape(u)
#print np.shape(self.invG)
try:
scale1 = (u.T * self.invG * u)[0,0]
#print 'u.T * invG * u = ' + str(scale0)
scale = (scale1 + self.prior_b['df']*self.prior_b['scale']) / df
#print 'scale = ' + str(scale) + ' scalebig = ' + str(scalebig)
self.utinvu = scale1
self.u_df = df
self.u_scale = scale
self.theta[self.sigma2_b_idx,0] = gt.rchisquare_inv_scaled(df,scale)
#print 'sigma_b: '+str(x)+ ' df='+str(df)+' scale='+str(scale)
except Exception, e:
print "Error while sampling sigma2_b"
print "df="+str(df)+" scale="+str(scale)
print e
raise e
print "Error while sampling sigma2_b"
print "df="+str(df)+" scale="+str(scale)
print e
raise e
## Sample sigma2_e
#print 'sigma_e index: '+ str(self.sigma2_e_idx)
try:
df = self.prior_e['df'] + self.num_y
e = self.X*self.theta[0:self.num_fixed,0] + self.Z*self.theta[self.num_fixed:self.num_locations]
e = self.y - e
scale = ((e.T * e)[0,0] + self.prior_e['df']*self.prior_e['scale']) / df
self.eeT = (e.T * e)[0,0]
self.e_df = df
self.e_scale = scale
#print 'df '+ str(df) + ' scale ' + str(scale)
self.theta[self.sigma2_e_idx] = gt.rchisquare_inv_scaled(df,scale)
#print 'sigma_e: '+str(x)+ ' sample_e'+str(self.sample)
except Exception, e:
print "Error while sampling sigma2_e"
print "df="+str(df)+" scale="+scale
print e
raise e
# Mixed Model Equations
def _compute_mm_equations(self):
# C beta_hat = r
# C dimension = (self.num_fixed+self.num_random)^2
# we trust in C
p = self.num_fixed
pq = self.num_locations
self.mmeq_C = np.asmatrix(np.zeros(pq*pq).reshape(pq,pq))
# print 'sigma_b: '+str(x)+ ' df='+str(df)+' scale='+str(scale)
except Exception, e:
print "Error while sampling sigma2_u"
print "df=" + str(df) + " scale=" + str(scale)
print e
raise e
## Sample sigma2_e
# print 'sigma_e index: '+ str(self.sigma2_e_idx)
prior = self.priors_sigma2e[0]
try:
df = prior["df"] + self.n
e = self.X * self.theta[0 : self.p, 0] + self.Z * self.theta[self.p :]
e = self.y - e
scale = ((e.T * e)[0, 0] + prior["df"] * prior["scale"]) / df
# print 'df '+ str(df) + ' scale ' + str(scale)
self.sigma2_e[0, 0] = gt.rchisquare_inv_scaled(df, scale)
# print 'sigma_e: '+str(x)+ ' sample_e'+str(self.sample)
except Exception, e:
print "Error while sampling sigma2_e"
print "df=" + str(df) + " scale=" + str(scale)
print e
raise e
def plot(self):
# n = len(params)
num_samples = self.num_samples
plt.figure(1)
# print "NUM SAMPLES="+str(num_samples)
x = range(0, num_samples)
