def find_CI(quartile, variable, critical, search = "left", grid_left = None, grid_right = None, precision = 0.001):
'''
We take {regions} points in our domain and test every point for its likelihood value,
then we look at points where values of ll ratio criteria cross critical value of chi^2 distribution
It means, we should choose {regions} in such way that at least one point lays in the confidence interval
at the first step.
'''
regions = 20
solution = solutions[quartile]
ll_hat = -0.5 * commons.chi2(solution)
critical_val = chi2.ppf(critical, df=1)
other1 = variable_to_others[variable][0]
other2 = variable_to_others[variable][1]
variable_bounds = (grid_left if not grid_left is None else commons.bounds[quartile][variable_index[variable]][0],
grid_right if not grid_right is None else commons.bounds[quartile][variable_index[variable]][1])
grid = np.linspace(start = variable_bounds[0], stop = variable_bounds[1], num = regions)
other_bounds = (commons.bounds[quartile][variable_index[other1]], commons.bounds[quartile][variable_index[other2]])
ll_prev_criteria = None
#print("Critical value is {}".format(critical_val))
for i, v in enumerate(grid):
#print("Check {} = {}".format(variable, v))
ys = sp.Matrix([variable_ref[other1], variable_ref[other2]])
pll_func_sym = commons.chi2_sym.subs(variable_ref[variable], v)
pll_func = lambda args: lambdify(ys, pll_func_sym, 'numpy')(*args)[0, 0]
pll_func_jacob = lambda args: lambdify(ys, pll_func_sym.jacobian(ys), 'numpy')(*args)
result = opt.minimize(pll_func, [solution[variable_index[other1]], solution[variable_index[other2]]],
method='TNC',
jac=pll_func_jacob,
bounds=other_bounds)
pll_val = -.5 * result.fun
ll_criteria = 2 * (ll_hat - pll_val)
#print("\tProfile LL ratio value = {}".format(ll_criteria))
if not ll_prev_criteria is None:
l = grid[i - 1]
r = grid[i]
if ll_prev_criteria > critical_val and ll_criteria < critical_val and search == "left":
#print("Critical point lays between {} and {}\n".format(l, r))
if (r - l) < precision:
return l, r
else:
return find_CI(quartile, variable, critical,
search = search,
grid_left = l,
grid_right = r,
precision = precision)
elif ll_prev_criteria < critical and ll_criteria > critical and search == "right":
#print("Critical point lays between {} and {}\n".format(l, r))
if (r - l) < precision:
return l, r
else:
return find_CI(quartile, variable, critical,
search = search,
grid_left = l,
grid_right = r,
precision = precision)
ll_prev_criteria = ll_criteria
def find_CI(quartile,
variable,
critical,
search="left",
grid_left=None,
grid_right=None,
precision=0.001):
'''
We take {regions} points in our domain and test every point for its likelihood value,
then we look at points where values of ll ratio criteria cross critical value of chi^2 distribution
It means, we should choose {regions} in such way that at least one point lays in the confidence interval
at the first step.
'''
regions = 20
solution = solutions[quartile]
ll_hat = -0.5 * commons.chi2(solution)
critical_val = chi2.ppf(critical, df=1)
other1 = variable_to_others[variable][0]
other2 = variable_to_others[variable][1]
variable_bounds = (grid_left if not grid_left is None else
commons.bounds[quartile][variable_index[variable]][0],
grid_right if not grid_right is None else
commons.bounds[quartile][variable_index[variable]][1])
grid = np.linspace(start=variable_bounds[0],
stop=variable_bounds[1],
num=regions)
other_bounds = (commons.bounds[quartile][variable_index[other1]],
commons.bounds[quartile][variable_index[other2]])
ll_prev_criteria = None
#print("Critical value is {}".format(critical_val))
for i, v in enumerate(grid):
#print("Check {} = {}".format(variable, v))
ys = sp.Matrix([variable_ref[other1], variable_ref[other2]])
pll_func_sym = commons.chi2_sym.subs(variable_ref[variable], v)
pll_func = lambda args: lambdify(ys, pll_func_sym, 'numpy')(*args)[0, 0
]
pll_func_jacob = lambda args: lambdify(ys, pll_func_sym.jacobian(ys),
'numpy')(*args)
result = opt.minimize(pll_func, [
solution[variable_index[other1]], solution[variable_index[other2]]
],
method='TNC',
jac=pll_func_jacob,
bounds=other_bounds)
pll_val = -.5 * result.fun
ll_criteria = 2 * (ll_hat - pll_val)
#print("\tProfile LL ratio value = {}".format(ll_criteria))
if not ll_prev_criteria is None:
l = grid[i - 1]
r = grid[i]
if ll_prev_criteria > critical_val and ll_criteria < critical_val and search == "left":
#print("Critical point lays between {} and {}\n".format(l, r))
if (r - l) < precision:
return l, r
else:
return find_CI(quartile,
variable,
critical,
search=search,
grid_left=l,
grid_right=r,
precision=precision)
elif ll_prev_criteria < critical and ll_criteria > critical and search == "right":
#print("Critical point lays between {} and {}\n".format(l, r))
if (r - l) < precision:
return l, r
else:
return find_CI(quartile,
variable,
critical,
search=search,
grid_left=l,
grid_right=r,
precision=precision)
ll_prev_criteria = ll_criteria
import sympy as sp
import numpy as np
import math
import scipy.optimize as opt
import commons
from sympy.utilities.lambdify import lambdify, implemented_function
from scipy.stats import chi2, norm
solutions = {1: (67.45671972804791, 130.5332959537717, 0.0940606150464772),
4: (-112.54293893364066, -49.46663201608742, 0.09406008329558219)}
ll_hat1 = -0.5 * commons.chi2(solutions[1]) #Values of ll function in globally optimal points
ll_hat2 = -0.5 * commons.chi2(solutions[4])
#Technical stuff
variable_to_others = {"gamma": ("deltaB", "rB"), "deltaB": ("gamma", 'rB'), "rB": ("gamma", "deltaB")}
variable_index = {"gamma": 0, "deltaB": 1, "rB": 2}
variable_ref = {"gamma" : commons.gamma, "deltaB": commons.deltaB, "rB": commons.rB}
def find_CI(quartile, variable, critical, search = "left", grid_left = None, grid_right = None, precision = 0.001):
'''
We take {regions} points in our domain and test every point for its likelihood value,
then we look at points where values of ll ratio criteria cross critical value of chi^2 distribution
It means, we should choose {regions} in such way that at least one point lays in the confidence interval
at the first step.
'''
regions = 20
solution = solutions[quartile]
ll_hat = -0.5 * commons.chi2(solution)
import sympy as sp
import numpy as np
import math
import scipy.optimize as opt
import commons
from sympy.utilities.lambdify import lambdify, implemented_function
from scipy.stats import chi2, norm
solutions = {
1: (67.45671972804791, 130.5332959537717, 0.0940606150464772),
4: (-112.54293893364066, -49.46663201608742, 0.09406008329558219)
}
ll_hat1 = -0.5 * commons.chi2(
solutions[1]) #Values of ll function in globally optimal points
ll_hat2 = -0.5 * commons.chi2(solutions[4])
#Technical stuff
variable_to_others = {
"gamma": ("deltaB", "rB"),
"deltaB": ("gamma", 'rB'),
"rB": ("gamma", "deltaB")
}
variable_index = {"gamma": 0, "deltaB": 1, "rB": 2}
variable_ref = {
"gamma": commons.gamma,
"deltaB": commons.deltaB,
"rB": commons.rB
}
