Source code for statsmodels.discrete.conditional_models
"""
Conditional logistic, Poisson, and multinomial logit regression
"""
import numpy as np
import statsmodels.base.model as base
import statsmodels.regression.linear_model as lm
import statsmodels.base.wrapper as wrap
from statsmodels.discrete.discrete_model import (MultinomialResults,
MultinomialResultsWrapper)
import collections
import warnings
import itertools
class _ConditionalModel(base.LikelihoodModel):
def __init__(self, endog, exog, missing='none', **kwargs):
if "groups" not in kwargs:
raise ValueError("'groups' is a required argument")
groups = kwargs["groups"]
if groups.size != endog.size:
msg = "'endog' and 'groups' should have the same dimensions"
raise ValueError(msg)
if exog.shape[0] != endog.size:
msg = "The leading dimension of 'exog' should equal the length of 'endog'"
raise ValueError(msg)
super().__init__(
endog, exog, missing=missing, **kwargs)
if self.data.const_idx is not None:
msg = ("Conditional models should not have an intercept in the " +
"design matrix")
raise ValueError(msg)
exog = self.exog
self.k_params = exog.shape[1]
# Get the row indices for each group
row_ix = {}
for i, g in enumerate(groups):
if g not in row_ix:
row_ix[g] = []
row_ix[g].append(i)
# Split the data into groups and remove groups with no variation
endog, exog = np.asarray(endog), np.asarray(exog)
offset = kwargs.get("offset")
self._endog_grp = []
self._exog_grp = []
self._groupsize = []
if offset is not None:
offset = np.asarray(offset)
self._offset_grp = []
self._offset = []
self._sumy = []
self.nobs = 0
drops = [0, 0]
for g, ix in row_ix.items():
y = endog[ix].flat
if np.std(y) == 0:
drops[0] += 1
drops[1] += len(y)
continue
self.nobs += len(y)
self._endog_grp.append(y)
if offset is not None:
self._offset_grp.append(offset[ix])
self._groupsize.append(len(y))
self._exog_grp.append(exog[ix, :])
self._sumy.append(np.sum(y))
if drops[0] > 0:
msg = ("Dropped %d groups and %d observations for having " +
"no within-group variance") % tuple(drops)
warnings.warn(msg)
# This can be pre-computed
if offset is not None:
self._endofs = []
for k, ofs in enumerate(self._offset_grp):
self._endofs.append(np.dot(self._endog_grp[k], ofs))
# Number of groups
self._n_groups = len(self._endog_grp)
# These are the sufficient statistics
self._xy = []
self._n1 = []
for g in range(self._n_groups):
self._xy.append(np.dot(self._endog_grp[g], self._exog_grp[g]))
self._n1.append(np.sum(self._endog_grp[g]))
def hessian(self, params):
from statsmodels.tools.numdiff import approx_fprime
hess = approx_fprime(params, self.score)
hess = np.atleast_2d(hess)
return hess
def fit(self,
start_params=None,
method='BFGS',
maxiter=100,
full_output=True,
disp=False,
fargs=(),
callback=None,
retall=False,
skip_hessian=False,
**kwargs):
rslt = super().fit(
start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
skip_hessian=skip_hessian)
crslt = ConditionalResults(self, rslt.params, rslt.cov_params(), 1)
crslt.method = method
crslt.nobs = self.nobs
crslt.n_groups = self._n_groups
crslt._group_stats = [
"%d" % min(self._groupsize),
"%d" % max(self._groupsize),
"%.1f" % np.mean(self._groupsize)
]
rslt = ConditionalResultsWrapper(crslt)
return rslt
def fit_regularized(self,
method="elastic_net",
alpha=0.,
start_params=None,
refit=False,
**kwargs):
"""
Return a regularized fit to a linear regression model.
Parameters
----------
method : {'elastic_net'}
Only the `elastic_net` approach is currently implemented.
alpha : scalar or array_like
The penalty weight. If a scalar, the same penalty weight
applies to all variables in the model. If a vector, it
must have the same length as `params`, and contains a
penalty weight for each coefficient.
start_params : array_like
Starting values for `params`.
refit : bool
If True, the model is refit using only the variables that
have non-zero coefficients in the regularized fit. The
refitted model is not regularized.
**kwargs
Additional keyword argument that are used when fitting the model.
Returns
-------
Results
A results instance.
"""
from statsmodels.base.elastic_net import fit_elasticnet
if method != "elastic_net":
raise ValueError("method for fit_regularized must be elastic_net")
defaults = {"maxiter": 50, "L1_wt": 1, "cnvrg_tol": 1e-10,
"zero_tol": 1e-10}
defaults.update(kwargs)
return fit_elasticnet(self, method=method,
alpha=alpha,
start_params=start_params,
refit=refit,
**defaults)
# Override to allow groups to be passed as a variable name.
@classmethod
def from_formula(cls,
formula,
data,
subset=None,
drop_cols=None,
*args,
**kwargs):
try:
groups = kwargs["groups"]
del kwargs["groups"]
except KeyError:
raise ValueError("'groups' is a required argument")
if isinstance(groups, str):
groups = data[groups]
if "0+" not in formula.replace(" ", ""):
warnings.warn("Conditional models should not include an intercept")
model = super().from_formula(
formula, data=data, groups=groups, *args, **kwargs)
return model
[docs]
class ConditionalLogit(_ConditionalModel):
"""
Fit a conditional logistic regression model to grouped data.
Every group is implicitly given an intercept, but the model is fit using
a conditional likelihood in which the intercepts are not present. Thus,
intercept estimates are not given, but the other parameter estimates can
be interpreted as being adjusted for any group-level confounders.
Parameters
----------
endog : array_like
The response variable, must contain only 0 and 1.
exog : array_like
The array of covariates. Do not include an intercept
in this array.
groups : array_like
Codes defining the groups. This is a required keyword parameter.
"""
def __init__(self, endog, exog, missing='none', **kwargs):
super().__init__(
endog, exog, missing=missing, **kwargs)
if np.any(np.unique(self.endog) != np.r_[0, 1]):
msg = "endog must be coded as 0, 1"
raise ValueError(msg)
self.K = self.exog.shape[1]
# i.e. self.k_params, for compatibility with MNLogit
[docs]
def loglike(self, params):
ll = 0
for g in range(len(self._endog_grp)):
ll += self.loglike_grp(g, params)
return ll
[docs]
def score(self, params):
score = 0
for g in range(self._n_groups):
score += self.score_grp(g, params)
return score
def _denom(self, grp, params, ofs=None):
if ofs is None:
ofs = 0
exb = np.exp(np.dot(self._exog_grp[grp], params) + ofs)
# In the recursions, f may be called multiple times with the
# same arguments, so we memoize the results.
memo = {}
def f(t, k):
if t < k:
return 0
if k == 0:
return 1
try:
return memo[(t, k)]
except KeyError:
pass
v = f(t - 1, k) + f(t - 1, k - 1) * exb[t - 1]
memo[(t, k)] = v
return v
return f(self._groupsize[grp], self._n1[grp])
def _denom_grad(self, grp, params, ofs=None):
if ofs is None:
ofs = 0
ex = self._exog_grp[grp]
exb = np.exp(np.dot(ex, params) + ofs)
# s may be called multiple times in the recursions with the
# same arguments, so memoize the results.
memo = {}
def s(t, k):
if t < k:
return 0, np.zeros(self.k_params)
if k == 0:
return 1, 0
try:
return memo[(t, k)]
except KeyError:
pass
h = exb[t - 1]
a, b = s(t - 1, k)
c, e = s(t - 1, k - 1)
d = c * h * ex[t - 1, :]
u, v = a + c * h, b + d + e * h
memo[(t, k)] = (u, v)
return u, v
return s(self._groupsize[grp], self._n1[grp])
[docs]
def loglike_grp(self, grp, params):
ofs = None
if hasattr(self, 'offset'):
ofs = self._offset_grp[grp]
llg = np.dot(self._xy[grp], params)
if ofs is not None:
llg += self._endofs[grp]
llg -= np.log(self._denom(grp, params, ofs))
return llg
[docs]
def score_grp(self, grp, params):
ofs = 0
if hasattr(self, 'offset'):
ofs = self._offset_grp[grp]
d, h = self._denom_grad(grp, params, ofs)
return self._xy[grp] - h / d
[docs]
class ConditionalPoisson(_ConditionalModel):
"""
Fit a conditional Poisson regression model to grouped data.
Every group is implicitly given an intercept, but the model is fit using
a conditional likelihood in which the intercepts are not present. Thus,
intercept estimates are not given, but the other parameter estimates can
be interpreted as being adjusted for any group-level confounders.
Parameters
----------
endog : array_like
The response variable
exog : array_like
The covariates
groups : array_like
Codes defining the groups. This is a required keyword parameter.
"""
[docs]
def loglike(self, params):
ofs = None
if hasattr(self, 'offset'):
ofs = self._offset_grp
ll = 0.0
for i in range(len(self._endog_grp)):
xb = np.dot(self._exog_grp[i], params)
if ofs is not None:
xb += ofs[i]
exb = np.exp(xb)
y = self._endog_grp[i]
ll += np.dot(y, xb)
s = exb.sum()
ll -= self._sumy[i] * np.log(s)
return ll
[docs]
def score(self, params):
ofs = None
if hasattr(self, 'offset'):
ofs = self._offset_grp
score = 0.0
for i in range(len(self._endog_grp)):
x = self._exog_grp[i]
xb = np.dot(x, params)
if ofs is not None:
xb += ofs[i]
exb = np.exp(xb)
s = exb.sum()
y = self._endog_grp[i]
score += np.dot(y, x)
score -= self._sumy[i] * np.dot(exb, x) / s
return score
[docs]
class ConditionalResults(base.LikelihoodModelResults):
def __init__(self, model, params, normalized_cov_params, scale):
super().__init__(
model,
params,
normalized_cov_params=normalized_cov_params,
scale=scale)
[docs]
def summary(self, yname=None, xname=None, title=None, alpha=.05):
"""
Summarize the fitted model.
Parameters
----------
yname : str, optional
Default is `y`
xname : list[str], optional
Names for the exogenous variables, default is "var_xx".
Must match the number of parameters in the model
title : str, optional
Title for the top table. If not None, then this replaces the
default title
alpha : float
Significance level for the confidence intervals
Returns
-------
smry : Summary instance
This holds the summary tables and text, which can be printed or
converted to various output formats.
See Also
--------
statsmodels.iolib.summary.Summary : class to hold summary
results
"""
top_left = [
('Dep. Variable:', None),
('Model:', None),
('Log-Likelihood:', None),
('Method:', [self.method]),
('Date:', None),
('Time:', None),
]
top_right = [
('No. Observations:', None),
('No. groups:', [self.n_groups]),
('Min group size:', [self._group_stats[0]]),
('Max group size:', [self._group_stats[1]]),
('Mean group size:', [self._group_stats[2]]),
]
if title is None:
title = "Conditional Logit Model Regression Results"
# create summary tables
from statsmodels.iolib.summary import Summary
smry = Summary()
smry.add_table_2cols(
self,
gleft=top_left,
gright=top_right, # [],
yname=yname,
xname=xname,
title=title)
smry.add_table_params(
self, yname=yname, xname=xname, alpha=alpha, use_t=self.use_t)
return smry
[docs]
class ConditionalMNLogit(_ConditionalModel):
"""
Fit a conditional multinomial logit model to grouped data.
Parameters
----------
endog : array_like
The dependent variable, must be integer-valued, coded
0, 1, ..., c-1, where c is the number of response
categories.
exog : array_like
The independent variables.
groups : array_like
Codes defining the groups. This is a required keyword parameter.
Notes
-----
Equivalent to femlogit in Stata.
References
----------
Gary Chamberlain (1980). Analysis of covariance with qualitative
data. The Review of Economic Studies. Vol. 47, No. 1, pp. 225-238.
"""
def __init__(self, endog, exog, missing='none', **kwargs):
super().__init__(
endog, exog, missing=missing, **kwargs)
# endog must be integers
self.endog = self.endog.astype(int)
self.k_cat = self.endog.max() + 1
self.df_model = (self.k_cat - 1) * self.exog.shape[1]
self.df_resid = self.nobs - self.df_model
self._ynames_map = {j: str(j) for j in range(self.k_cat)}
self.J = self.k_cat # Unfortunate name, needed for results
self.K = self.exog.shape[1] # for compatibility with MNLogit
if self.endog.min() < 0:
msg = "endog may not contain negative values"
raise ValueError(msg)
grx = collections.defaultdict(list)
for k, v in enumerate(self.groups):
grx[v].append(k)
self._group_labels = list(grx.keys())
self._group_labels.sort()
self._grp_ix = [grx[k] for k in self._group_labels]
[docs]
def fit(self,
start_params=None,
method='BFGS',
maxiter=100,
full_output=True,
disp=False,
fargs=(),
callback=None,
retall=False,
skip_hessian=False,
**kwargs):
if start_params is None:
q = self.exog.shape[1]
c = self.k_cat - 1
start_params = np.random.normal(size=q * c)
# Do not call super(...).fit because it cannot handle the 2d-params.
rslt = base.LikelihoodModel.fit(
self,
start_params=start_params,
method=method,
maxiter=maxiter,
full_output=full_output,
disp=disp,
skip_hessian=skip_hessian)
rslt.params = rslt.params.reshape((self.exog.shape[1], -1))
rslt = MultinomialResults(self, rslt)
# Not clear what the null likelihood should be, there is no intercept
# so the null model is not clearly defined. This is needed for summary
# to work.
rslt.set_null_options(llnull=np.nan)
return MultinomialResultsWrapper(rslt)
[docs]
def loglike(self, params):
q = self.exog.shape[1]
c = self.k_cat - 1
pmat = params.reshape((q, c))
pmat = np.concatenate((np.zeros((q, 1)), pmat), axis=1)
lpr = np.dot(self.exog, pmat)
ll = 0.0
for ii in self._grp_ix:
x = lpr[ii, :]
jj = np.arange(x.shape[0], dtype=int)
y = self.endog[ii]
denom = 0.0
for p in itertools.permutations(y):
denom += np.exp(x[(jj, p)].sum())
ll += x[(jj, y)].sum() - np.log(denom)
return ll
[docs]
def score(self, params):
q = self.exog.shape[1]
c = self.k_cat - 1
pmat = params.reshape((q, c))
pmat = np.concatenate((np.zeros((q, 1)), pmat), axis=1)
lpr = np.dot(self.exog, pmat)
grad = np.zeros((q, c))
for ii in self._grp_ix:
x = lpr[ii, :]
jj = np.arange(x.shape[0], dtype=int)
y = self.endog[ii]
denom = 0.0
denomg = np.zeros((q, c))
for p in itertools.permutations(y):
v = np.exp(x[(jj, p)].sum())
denom += v
for i, r in enumerate(p):
if r != 0:
denomg[:, r - 1] += v * self.exog[ii[i], :]
for i, r in enumerate(y):
if r != 0:
grad[:, r - 1] += self.exog[ii[i], :]
grad -= denomg / denom
return grad.flatten()
class ConditionalResultsWrapper(lm.RegressionResultsWrapper):
pass
wrap.populate_wrapper(ConditionalResultsWrapper, ConditionalResults)
Last update:
Oct 03, 2024