def _prepare_simulate(self):
# Priors
# scaled inverted chi-square. Tuple (v,s2) = (degrees freedom,scale)
self.prior_b = gt.create_chisquare_si(1.0,0.5)
self.prior_e = gt.create_chisquare_si(1.0,0.5)
# Parameters - theta
self.theta = np.asmatrix(np.zeros(self.num_parameters).reshape(self.num_parameters,1))
self.loc_hat = np.asmatrix(np.zeros(self.num_locations).reshape(self.num_locations,1))
# used for Garcia-Cortes & Sorensen method
self.loc_star = np.asmatrix(np.zeros(self.num_locations).reshape(self.num_locations,1))
self.y_star = np.asmatrix(np.zeros(self.num_y).reshape(self.num_y,1))
self.loc_tilde = np.asmatrix(np.zeros(self.num_locations).reshape(self.num_locations,1))
## Aliases
self.B = np.matrix(self.theta[0:self.num_fixed,0],copy=False)
self.B_hat = np.matrix(self.loc_hat[0:self.num_fixed,0],copy=False)
# self.B = np.asmatrix(np.zeros(self.num_fixed).reshape(self.num_fixed,1))
self.b = np.matrix(self.theta[self.num_fixed:self.num_locations,0],copy=False)
self.b_hat = np.matrix(self.loc_hat[self.num_fixed:,0],copy=False)
# self.b = np.asmatrix(np.zeros(self.num_random).reshape(self.num_random,1))
## Initial Values
# B and b initialised to 0 - is this correct for B?
self.B[0,0] = 10
self.B[1,0] = 1.5
### Variance components
# one sigma for each random effect
self.theta[self.sigma2_b_idx,0] = 0.5
# one sigma for each random effect
self.theta[self.sigma2_e_idx,0] = 0.5
# Restore buffer
self.restore_initialize()
# other initializations
self._compute_mm_equations()
def set_default_priors(self):
default_gpriors = [gt.create_chisquare_si(1.0, 0.5) for i in range(0, self.c)]
default_rpriors = [gt.create_chisquare_si(1.0, 0.5)]
# Set priors to defaults scaled inverse chi-square. User can change them later.
assert self.set_priors(Gpriors=default_gpriors, Rpriors=default_rpriors)
