def test_beta(self,
b0_vals,
param_nums,
ftol=10**-5,
maxiter=30,
print_weights=1):
"""
Returns the profile log likelihood for regression parameters
'param_num' at 'b0_vals.'
Parameters
----------
b0_vals: list
The value of parameters to be tested
param_num: list
Which parameters to be tested
maxiter: int, optional
How many iterations to use in the EM algorithm. Default is 30
ftol: float, optional
The function tolerance for the EM optimization.
Default is 10''**''-5
print_weights: bool
If true, returns the weights tate maximize the profile
log likelihood. Default is False
Returns
-------
test_results: tuple
The log-likelihood and p-pvalue of the test.
Notes
----
The function will warn if the EM reaches the maxiter. However, when
optimizing over nuisance parameters, it is possible to reach a
maximum number of inner iterations for a specific value for the
nuisance parameters while the resultsof the function are still valid.
This usually occurs when the optimization over the nuisance parameters
selects paramater values that yield a log-likihood ratio close to
infinity.
Examples
-------
import statsmodels.api as sm
import numpy as np
# Test parameter is .05 in one regressor no intercept model
data=sm.datasets.heart.load()
y = np.log10(data.endog)
x = data.exog
cens = data.censors
model = sm.emplike.emplikeAFT(y, x, cens)
res=model.test_beta([0], [0])
>>>res
>>>(1.4657739632606308, 0.22601365256959183)
#Test slope is 0 in model with intercept
data=sm.datasets.heart.load()
y = np.log10(data.endog)
x = data.exog
cens = data.censors
model = sm.emplike.emplikeAFT(y, sm.add_constant(x, prepend=1), cens)
res=model.test_beta([0], [1])
>>>res
>>>(4.623487775078047, 0.031537049752572731)
"""
censors = self.model.censors
endog = self.model.endog
exog = self.model.exog
uncensored = (censors == 1).flatten()
censored = (censors == 0).flatten()
uncens_endog = endog[uncensored]
uncens_exog = exog[uncensored, :]
reg_model = OLS(uncens_endog, uncens_exog).fit()
llr, pval, new_weights = reg_model.el_test(
b0_vals, param_nums, return_weights=True) # Needs to be changed
km = self.model._make_km(endog, censors).flatten() # when merged
uncens_nobs = self.model.uncens_nobs
F = np.asarray(new_weights).reshape(uncens_nobs)
# Step 0 ^
params = self.params()
survidx = np.where(censors == 0)
survidx = survidx[0] - np.arange(len(survidx[0]))
numcensbelow = np.int_(np.cumsum(1 - censors))
if len(param_nums) == len(params):
llr = self._EM_test([],
F=F,
params=params,
param_nums=param_nums,
b0_vals=b0_vals,
survidx=survidx,
uncens_nobs=uncens_nobs,
numcensbelow=numcensbelow,
km=km,
uncensored=uncensored,
censored=censored,
ftol=ftol,
maxiter=25)
return llr, chi2.sf(llr, self.model.nvar)
else:
x0 = np.delete(params, param_nums)
try:
res = optimize.fmin(
self._EM_test,
x0,
(params, param_nums, b0_vals, F, survidx, uncens_nobs,
numcensbelow, km, uncensored, censored, maxiter, ftol),
full_output=1,
disp=0)
llr = res[1]
return llr, chi2.sf(llr, len(param_nums))
except np.linalg.linalg.LinAlgError:
return np.inf, 0
def test_beta(self, b0_vals, param_nums, ftol=10 ** - 5, maxiter=30,
print_weights=1):
"""
Returns the profile log likelihood for regression parameters
'param_num' at 'b0_vals.'
Parameters
----------
b0_vals: list
The value of parameters to be tested
param_num: list
Which parameters to be tested
maxiter: int, optional
How many iterations to use in the EM algorithm. Default is 30
ftol: float, optional
The function tolerance for the EM optimization.
Default is 10''**''-5
print_weights: bool
If true, returns the weights tate maximize the profile
log likelihood. Default is False
Returns
-------
test_results: tuple
The log-likelihood and p-pvalue of the test.
Notes
----
The function will warn if the EM reaches the maxiter. However, when
optimizing over nuisance parameters, it is possible to reach a
maximum number of inner iterations for a specific value for the
nuisance parameters while the resultsof the function are still valid.
This usually occurs when the optimization over the nuisance parameters
selects paramater values that yield a log-likihood ratio close to
infinity.
Examples
-------
import statsmodels.api as sm
import numpy as np
# Test parameter is .05 in one regressor no intercept model
data=sm.datasets.heart.load()
y = np.log10(data.endog)
x = data.exog
cens = data.censors
model = sm.emplike.emplikeAFT(y, x, cens)
res=model.test_beta([0], [0])
>>>res
>>>(1.4657739632606308, 0.22601365256959183)
#Test slope is 0 in model with intercept
data=sm.datasets.heart.load()
y = np.log10(data.endog)
x = data.exog
cens = data.censors
model = sm.emplike.emplikeAFT(y, sm.add_constant(x, prepend=1), cens)
res=model.test_beta([0], [1])
>>>res
>>>(4.623487775078047, 0.031537049752572731)
"""
censors = self.model.censors
endog = self.model.endog
exog = self.model.exog
uncensored = (censors == 1).flatten()
censored = (censors == 0).flatten()
uncens_endog = endog[uncensored]
uncens_exog = exog[uncensored, :]
reg_model = OLS(uncens_endog, uncens_exog).fit()
llr, pval, new_weights = reg_model.el_test(b0_vals, param_nums, return_weights=True) # Needs to be changed
km = self.model._make_km(endog, censors).flatten() # when merged
uncens_nobs = self.model.uncens_nobs
F = np.asarray(new_weights).reshape(uncens_nobs)
# Step 0 ^
params = self.params()
survidx = np.where(censors == 0)
survidx = survidx[0] - np.arange(len(survidx[0]))
numcensbelow = np.int_(np.cumsum(1 - censors))
if len(param_nums) == len(params):
llr = self._EM_test([], F=F, params=params,
param_nums=param_nums,
b0_vals=b0_vals, survidx=survidx,
uncens_nobs=uncens_nobs,
numcensbelow=numcensbelow, km=km,
uncensored=uncensored, censored=censored,
ftol=ftol, maxiter=25)
return llr, chi2.sf(llr, self.model.nvar)
else:
x0 = np.delete(params, param_nums)
try:
res = optimize.fmin(self._EM_test, x0,
(params, param_nums, b0_vals, F, survidx,
uncens_nobs, numcensbelow, km, uncensored,
censored, maxiter, ftol), full_output=1,
disp = 0)
llr = res[1]
return llr, chi2.sf(llr, len(param_nums))
except np.linalg.linalg.LinAlgError:
return np.inf, 0
