"""
Bayesian inference for generalized linear mixed models.
Currently only families without additional scale or shape parameters
are supported (binomial and Poisson).
Two estimation approaches are supported: Laplace approximation
(maximum a posteriori), and variational Bayes (mean field
approximation to the posterior).
All realizations of random effects are required to be mutually
independent in this implementation.
The `exog_vc` matrix is the design matrix for the random effects.
Every column of `exog_vc` corresponds to an independent realization of
a random effect. These random effects have mean zero and an unknown
standard deviation. The standard deviation parameters are constrained
to be equal within subsets of the columns. These subsets are specified
through the parameer `ident` when not using formulas. When formulas
are used, the columns of `exog_vc` derived from a common formula are
constrained to have the same standard deviation.
In many applications, `exog_vc` will be sparse. A sparse matrix may
be passed when constructing a model class. If a dense matrix is
passed, it will be converted internally to a sparse matrix. There
currently is no way to avoid creating a temporary dense version of
`exog_vc` when using formulas.
Model and parameterization
--------------------------
The joint density of data and parameters factors as:
p(y | vc, fep) p(vc | vcp) p(vcp) p(fe)
The terms p(vcp) and p(fe) are prior distributions that are taken to
be Gaussian (the vcp parameters are log standard deviations so the
variance parameters have log-normal distributions). The random
effects distribution p(vc | vcp) is independent Gaussian (random
effect realizations are independent within and between values of the
`ident` array). The model p(y | vc, fep) is based on the specific GLM
being fit.
"""
from __future__ import division
import numpy as np
from scipy.optimize import minimize
from scipy import sparse
import statsmodels.base.model as base
from statsmodels.iolib import summary2
from statsmodels.genmod import families
import pandas as pd
import warnings
import patsy
# Gauss-Legendre weights
glw = [[0.2955242247147529, -0.1488743389816312],
[0.2955242247147529, 0.1488743389816312],
[0.2692667193099963, -0.4333953941292472],
[0.2692667193099963, 0.4333953941292472],
[0.2190863625159820, -0.6794095682990244],
[0.2190863625159820, 0.6794095682990244],
[0.1494513491505806, -0.8650633666889845],
[0.1494513491505806, 0.8650633666889845],
[0.0666713443086881, -0.9739065285171717],
[0.0666713443086881, 0.9739065285171717]]
_init_doc = r"""
Fit a generalized linear mixed model using Bayesian methods.
The class implements the Laplace approximation to the posterior
distribution (`fit_map`) and a variational Bayes approximation to
the posterior (`fit_vb`). See the two fit method docstrings for
more information about the fitting approaches.
Parameters
----------
endog : array-like
Vector of response values.
exog : array-like
Array of covariates for the fixed effects part of the mean
structure.
exog_vc : array-like
Array of covariates for the random part of the model. A
scipy.sparse array may be provided, or else the passed
array will be converted to sparse internally.
ident : array-like
Array of labels showing which random terms (columns of
`exog_vc`) have a common variance.
vc_p : float
Prior standard deviation for variance component parameters
(the prior standard deviation of log(s) is vc_p, where s is
the standard deviation of a random effect).
fe_p : float
Prior standard deviation for fixed effects parameters.
family : statsmodels.genmod.families instance
The GLM family.
fep_names : list of strings
The names of the fixed effects parameters (corresponding to
columns of exog). If None, default names are constructed.
vcp_names : list of strings
The names of the variance component parameters (corresponding
to distinct labels in ident). If None, default names are
constructed.
vc_names : list of strings
The names of the random effect realizations.
Returns
-------
MixedGLMResults object
Notes
-----
There are three types of values in the posterior distribution:
fixed effects parameters (fep), corresponding to the columns of
`exog`, random effects realizations (vc), corresponding to the
columns of `exog_vc`, and the standard deviations of the random
effects realizations (vcp), corresponding to the unique labels in
`ident`.
All random effects are modeled as being independent Gaussian
values (given the variance parameters). Every column of `exog_vc`
has a distinct realized random effect that is used to form the
linear predictors. The elements of `ident` determine the distinct
random effect variance parameters. Two random effect realizations
that have the same value in `ident` are constrained to have the
same variance. When fitting with a formula, `ident` is
constructed internally (each element of `vc_formulas` yields a
distinct label in `ident`).
The random effect standard deviation parameters (vcp) have
log-normal prior distributions with mean 0 and standard deviation
`vcp_p`.
Note that for some families, e.g. Binomial, the posterior mode may
be difficult to find numerically if `vcp_p` is set to too large of
a value. Setting `vcp_p` to 0.5 seems to work well.
The prior for the fixed effects parameters is Gaussian with mean 0
and standard deviation `fe_p`.
Examples
--------{example}
References
----------
Introduction to generalized linear mixed models:
https://stats.idre.ucla.edu/other/mult-pkg/introduction-to-generalized-linear-mixed-models
SAS documentation:
https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_intromix_a0000000215.htm
An assessment of estimation methods for generalized linear mixed
models with binary outcomes
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866838/
"""
_logit_example = """
A binomial (logistic) random effects model with random intercepts
for villages and random slopes for each year within each village:
>>> data['year_cen'] = data['Year'] - data.Year.mean()
>>> random = ['0 + C(Village)', '0 + C(Village)*year_cen']
>>> model = BinomialBayesMixedGLM.from_formula('y ~ year_cen',
random, data)
>>> result = model.fit()
"""
_poisson_example = """
A Poisson random effects model with random intercepts for villages
and random slopes for each year within each village:
>>> data['year_cen'] = data['Year'] - data.Year.mean()
>>> random = ['0 + C(Village)', '0 + C(Village)*year_cen']
>>> model = PoissonBayesMixedGLM.from_formula('y ~ year_cen',
random, data)
>>> result = model.fit()
"""
class _BayesMixedGLM(base.Model):
def __init__(self, endog, exog, exog_vc=None, ident=None, family=None,
vcp_p=1, fe_p=2, fep_names=None,
vcp_names=None, vc_names=None, **kwargs):
if len(ident) != exog_vc.shape[1]:
msg = "len(ident) should match the number of columns of exog_vc"
raise ValueError(msg)
# Get the fixed effects parameter names
if fep_names is None:
if hasattr(exog, "columns"):
fep_names = exog.columns.tolist()
else:
fep_names = ["FE_%d" % (k + 1)
for k in range(exog.shape[1])]
# Get the variance parameter names
if vcp_names is None:
vcp_names = ["VC_%d" % (k + 1)
for k in range(int(max(ident)) + 1)]
else:
if len(vcp_names) != len(set(ident)):
msg = "The lengths of vcp_names and ident should be the same"
raise ValueError(msg)
endog = np.asarray(endog)
exog = np.asarray(exog)
if not sparse.issparse(exog_vc):
exog_vc = sparse.csr_matrix(exog_vc)
ident = ident.astype(np.int)
vcp_p = float(vcp_p)
fe_p = float(fe_p)
# Number of fixed effects parameters
if exog is None:
k_fep = 0
else:
k_fep = exog.shape[1]
# Number of variance component structure parameters and
# variance component realizations.
if exog_vc is None:
k_vc = 0
k_vcp = 0
else:
k_vc = exog_vc.shape[1]
k_vcp = max(ident) + 1
# power would be better but not available in older scipy
exog_vc2 = exog_vc.multiply(exog_vc)
super(_BayesMixedGLM, self).__init__(endog, exog, **kwargs)
self.exog_vc = exog_vc
self.exog_vc2 = exog_vc2
self.ident = ident
self.family = family
self.k_fep = k_fep
self.k_vc = k_vc
self.k_vcp = k_vcp
self.fep_names = fep_names
self.vcp_names = vcp_names
self.vc_names = vc_names
self.fe_p = fe_p
self.vcp_p = vcp_p
def _unpack(self, vec):
ii = 0
# Fixed effects parameters
fep = vec[:ii+self.k_fep]
ii += self.k_fep
# Variance component structure parameters (standard
# deviations). These are on the log scale. The standard
# deviation for random effect j is exp(vcp[ident[j]]).
vcp = vec[ii:ii+self.k_vcp]
ii += self.k_vcp
# Random effect realizations
vc = vec[ii:]
return fep, vcp, vc
def logposterior(self, params):
"""
The overall log-density: log p(y, fe, vc, vcp).
This differs by an additive constant from the log posterior
log p(fe, vc, vcp | y).
"""
fep, vcp, vc = self._unpack(params)
# Contributions from p(y | vc)
lp = 0
if self.k_fep > 0:
lp += np.dot(self.exog, fep)
if self.k_vc > 0:
lp += self.exog_vc.dot(vc)
mu = self.family.link.inverse(lp)
ll = self.family.loglike(self.endog, mu)
if self.k_vc > 0:
# Contribution from p(vc | vcp)
vcp0 = vcp[self.ident]
s = np.exp(vcp0)
ll -= 0.5 * np.sum(vc**2 / s**2) + np.sum(vcp0)
# Prior for vc parameters
ll -= 0.5 * np.sum(vcp**2 / self.vcp_p**2)
# Contributions from p(fep)
if self.k_fep > 0:
ll -= 0.5 * np.sum(fep**2 / self.fe_p**2)
return ll
def logposterior_grad(self, params):
"""
The gradient of the log posterior.
"""
fep, vcp, vc = self._unpack(params)
lp = 0
if self.k_fep > 0:
lp += np.dot(self.exog, fep)
if self.k_vc > 0:
lp += self.exog_vc.dot(vc)
mu = self.family.link.inverse(lp)
score_factor = (self.endog - mu) / self.family.link.deriv(mu)
score_factor /= self.family.variance(mu)
te = [None, None, None]
# Contributions from p(y | x, z, vc)
if self.k_fep > 0:
te[0] = np.dot(score_factor, self.exog)
if self.k_vc > 0:
te[2] = self.exog_vc.transpose().dot(score_factor)
if self.k_vc > 0:
# Contributions from p(vc | vcp)
# vcp0 = vcp[self.ident]
# s = np.exp(vcp0)
# ll -= 0.5 * np.sum(vc**2 / s**2) + np.sum(vcp0)
vcp0 = vcp[self.ident]
s = np.exp(vcp0)
u = vc**2 / s**2 - 1
te[1] = np.bincount(self.ident, weights=u)
te[2] -= vc / s**2
# Contributions from p(vcp)
# ll -= 0.5 * np.sum(vcp**2 / self.vcp_p**2)
te[1] -= vcp / self.vcp_p**2
# Contributions from p(fep)
if self.k_fep > 0:
te[0] -= fep / self.fe_p**2
te = [x for x in te if x is not None]
return np.concatenate(te)
def _get_start(self):
start_fep = np.zeros(self.k_fep)
start_vcp = np.ones(self.k_vcp)
start_vc = np.random.normal(size=self.k_vc)
start = np.concatenate((start_fep, start_vcp, start_vc))
return start
@classmethod
def from_formula(cls, formula, vc_formulas, data, family=None,
vcp_p=1, fe_p=2):
"""
Fit a BayesMixedGLM using a formula.
Parameters
----------
formula : string
Formula for the endog and fixed effects terms (use ~ to separate
dependent and independent expressions).
vc_formulas : dictionary
vc_formulas[name] is a one-sided formula that creates one
collection of random effects with a common variance
prameter. If using a categorical expression to produce
variance components, note that generally `0 + ...` should
be used so that an intercept is not included.
data : data frame
The data to which the formulas are applied.
family : genmod.families instance
A GLM family.
vcp_p : float
The prior standard deviation for the logarithms of the standard
deviations of the random effects.
fe_p : float
The prior standard deviation for the fixed effects parameters.
"""
ident = []
exog_vc = []
vcp_names = []
j = 0
for na, fml in vc_formulas.items():
mat = patsy.dmatrix(fml, data, return_type='dataframe')
exog_vc.append(mat)
vcp_names.append(na)
ident.append(j * np.ones(mat.shape[1]))
j += 1
exog_vc = pd.concat(exog_vc, axis=1)
vc_names = exog_vc.columns.tolist()
ident = np.concatenate(ident)
model = super(_BayesMixedGLM, cls).from_formula(
formula, data=data, family=family, subset=None,
exog_vc=exog_vc, ident=ident, vc_names=vc_names,
vcp_names=vcp_names, fe_p=fe_p, vcp_p=vcp_p)
return model
def fit_map(self, method="BFGS", minim_opts=None):
"""
Construct the Laplace approximation to the posterior
distribution.
Parameters
----------
method : string
Optimization method for finding the posterior mode.
minim_opts : dict-like
Options passed to scipy.minimize.
Returns
-------
BayesMixedGLMResults instance.
"""
def fun(params):
return -self.logposterior(params)
def grad(params):
return -self.logposterior_grad(params)
start = self._get_start()
r = minimize(fun, start, method=method, jac=grad, options=minim_opts)
if not r.success:
msg = ("Laplace fitting did not converge, |gradient|=%.6f" %
np.sqrt(np.sum(r.jac**2)))
warnings.warn(msg)
from statsmodels.tools.numdiff import approx_fprime
hess = approx_fprime(r.x, grad)
hess_inv = np.linalg.inv(hess)
return BayesMixedGLMResults(self, r.x, hess_inv, optim_retvals=r)
class _VariationalBayesMixedGLM(object):
"""
A mixin providing generic (not family-specific) methods for
variational Bayes mean field fitting.
"""
# Integration range (from -rng to +rng). The integrals are with
# respect to a standard Gaussian distribution so (-5, 5) will be
# sufficient in many cases.
rng = 5
verbose = False
# Returns the mean and variance of the linear predictor under the
# given distribution parameters.
def _lp_stats(self, fep_mean, fep_sd, vc_mean, vc_sd):
tm = np.dot(self.exog, fep_mean)
tv = np.dot(self.exog**2, fep_sd**2)
tm += self.exog_vc.dot(vc_mean)
tv += self.exog_vc2.dot(vc_sd**2)
return tm, tv
def vb_elbo_base(self, h, tm, fep_mean, vcp_mean, vc_mean,
fep_sd, vcp_sd, vc_sd):
"""
Returns the evidence lower bound (ELBO) for the model.
This function calculates the family-specific ELBO function
based on information provided from a subclass.
Parameters
----------
h : function mapping 1d vector to 1d vector
The contribution of the model to the ELBO function can be
expressed as y_i*lp_i + Eh_i(z), where y_i and lp_i are
the response and linear predictor for observation i, and z
is a standard normal rangom variable. This formulation
can be achieved for any GLM with a canonical link
function.
"""
# p(y | vc) contributions
iv = 0
for w in glw:
z = self.rng * w[1]
iv += w[0] * h(z) * np.exp(-z**2 / 2)
iv /= np.sqrt(2*np.pi)
iv *= self.rng
iv += self.endog * tm
iv = iv.sum()
# p(vc | vcp) * p(vcp) * p(fep) contributions
iv += self._elbo_common(fep_mean, fep_sd, vcp_mean, vcp_sd,
vc_mean, vc_sd)
r = (iv + np.sum(np.log(fep_sd)) + np.sum(np.log(vcp_sd)) +
np.sum(np.log(vc_sd)))
return r
def vb_elbo_grad_base(self, h, tm, tv, fep_mean, vcp_mean, vc_mean,
fep_sd, vcp_sd, vc_sd):
"""
Return the gradient of the ELBO function.
See vb_elbo_base for parameters.
"""
fep_mean_grad = 0.
fep_sd_grad = 0.
vcp_mean_grad = 0.
vcp_sd_grad = 0.
vc_mean_grad = 0.
vc_sd_grad = 0.
# p(y | vc) contributions
for w in glw:
z = self.rng * w[1]
u = h(z) * np.exp(-z**2 / 2) / np.sqrt(2*np.pi)
r = u / np.sqrt(tv)
fep_mean_grad += w[0] * np.dot(u, self.exog)
vc_mean_grad += w[0] * self.exog_vc.transpose().dot(u)
fep_sd_grad += w[0] * z * np.dot(r, self.exog**2 * fep_sd)
v = self.exog_vc2.multiply(vc_sd).transpose().dot(r)
v = np.squeeze(np.asarray(v))
vc_sd_grad += w[0] * z * v
fep_mean_grad *= self.rng
vc_mean_grad *= self.rng
fep_sd_grad *= self.rng
vc_sd_grad *= self.rng
fep_mean_grad += np.dot(self.endog, self.exog)
vc_mean_grad += self.exog_vc.transpose().dot(self.endog)
(fep_mean_grad_i, fep_sd_grad_i, vcp_mean_grad_i, vcp_sd_grad_i,
vc_mean_grad_i, vc_sd_grad_i) = self._elbo_grad_common(
fep_mean, fep_sd, vcp_mean, vcp_sd, vc_mean, vc_sd)
fep_mean_grad += fep_mean_grad_i
fep_sd_grad += fep_sd_grad_i
vcp_mean_grad += vcp_mean_grad_i
vcp_sd_grad += vcp_sd_grad_i
vc_mean_grad += vc_mean_grad_i
vc_sd_grad += vc_sd_grad_i
fep_sd_grad += 1 / fep_sd
vcp_sd_grad += 1 / vcp_sd
vc_sd_grad += 1 / vc_sd
mean_grad = np.concatenate((fep_mean_grad, vcp_mean_grad,
vc_mean_grad))
sd_grad = np.concatenate((fep_sd_grad, vcp_sd_grad, vc_sd_grad))
if self.verbose:
print("|G|=%f" % np.sqrt(np.sum(mean_grad**2) +
np.sum(sd_grad**2)))
return mean_grad, sd_grad
def fit_vb(self, mean=None, sd=None, fit_method="BFGS", minim_opts=None,
verbose=False):
"""
Fit a model using the variational Bayes mean field approximation.
Parameters
----------
mean : array-like
Starting value for VB mean vector
sd : array-like
Starting value for VB standard deviation vector
fit_method : string
Algorithm for scipy.minimize
minim_opts : dict-like
Options passed to scipy.minimize
verbose : bool
If True, print the gradient norm to the screen each time
it is calculated.
Notes
-----
The goal is to find a factored Gaussian approximation
q1*q2*... to the posterior distribution, approximately
minimizing the KL divergence from the factored approximation
to the actual posterior. The KL divergence, or ELBO function
has the form
E* log p(y, fe, vcp, vc) - E* log q
where E* is expectation with respect to the product of qj.
References
----------
Blei, Kucukelbir, McAuliffe (2017). Variational Inference: A
review for Statisticians
https://arxiv.org/pdf/1601.00670.pdf
"""
self.verbose = verbose
n = self.k_fep + self.k_vcp + self.k_vc
ml = self.k_fep + self.k_vcp + self.k_vc
if mean is None:
m = np.zeros(n)
else:
if len(mean) != ml:
raise ValueError("mean has incorrect length, %d != %d" %
(len(mean), ml))
m = mean.copy()
if sd is None:
s = -0.5 + 0.1 * np.random.normal(size=n)
else:
if len(sd) != ml:
raise ValueError("sd has incorrect length, %d != %d" %
(len(sd), ml))
# s is parameterized on the log-scale internally when
# optimizing the ELBO function (this is transparent to the
# caller)
s = np.log(sd)
# Don't allow the variance parameter starting mean values to
# be too small.
i1, i2 = self.k_fep, self.k_fep + self.k_vcp
m[i1:i2] = np.where(m[i1:i2] < -1, -1, m[i1:i2])
# Don't allow the posterior standard deviation starting values
# to be too small.
s = np.where(s < -1, -1, s)
def elbo(x):
n = len(x) // 2
return -self.vb_elbo(x[:n], np.exp(x[n:]))
def elbo_grad(x):
n = len(x) // 2
gm, gs = self.vb_elbo_grad(x[:n], np.exp(x[n:]))
gs *= np.exp(x[n:])
return -np.concatenate((gm, gs))
start = np.concatenate((m, s))
mm = minimize(elbo, start, jac=elbo_grad, method=fit_method,
options=minim_opts)
if not mm.success:
warnings.warn("VB fitting did not converge")
n = len(mm.x) // 2
return BayesMixedGLMResults(self, mm.x[0:n], np.exp(2*mm.x[n:]), mm)
# Handle terms in the ELBO that are common to all models.
def _elbo_common(self, fep_mean, fep_sd, vcp_mean, vcp_sd, vc_mean, vc_sd):
iv = 0
# p(vc | vcp) contributions
m = vcp_mean[self.ident]
s = vcp_sd[self.ident]
iv -= np.sum((vc_mean**2 + vc_sd**2) * np.exp(2*(s**2 - m))) / 2
iv -= np.sum(m)
# p(vcp) contributions
iv -= 0.5 * (vcp_mean**2 + vcp_sd**2).sum() / self.vcp_p**2
# p(b) contributions
iv -= 0.5 * (fep_mean**2 + fep_sd**2).sum() / self.fe_p**2
return iv
def _elbo_grad_common(self, fep_mean, fep_sd, vcp_mean, vcp_sd,
vc_mean, vc_sd):
# p(vc | vcp) contributions
m = vcp_mean[self.ident]
s = vcp_sd[self.ident]
u = vc_mean**2 + vc_sd**2
ve = np.exp(2*(s**2 - m))
dm = u * ve - 1
ds = -2 * u * ve * s
vcp_mean_grad = np.bincount(self.ident, weights=dm)
vcp_sd_grad = np.bincount(self.ident, weights=ds)
vc_mean_grad = -vc_mean.copy() * ve
vc_sd_grad = -vc_sd.copy() * ve
# p(vcp) contributions
vcp_mean_grad -= vcp_mean / self.vcp_p**2
vcp_sd_grad -= vcp_sd / self.vcp_p**2
# p(b) contributions
fep_mean_grad = -fep_mean.copy() / self.fe_p**2
fep_sd_grad = -fep_sd.copy() / self.fe_p**2
return (fep_mean_grad, fep_sd_grad, vcp_mean_grad, vcp_sd_grad,
vc_mean_grad, vc_sd_grad)
[docs]class BayesMixedGLMResults(object):
"""
Attributes
----------
fe_mean : array-like
Posterior mean of the fixed effects coefficients.
fe_sd : array-like
Posterior standard deviation of the fixed effects coefficients
vcp_mean : array-like
Posterior mean of the logged variance component standard
deviations.
vcp_sd : array-like
Posterior standard deviation of the logged variance component
standard deviations.
vc_mean : array-like
Posterior mean of the random coefficients
vc_sd : array-like
Posterior standard deviation of the random coefficients
"""
def __init__(self, model, params, cov_params,
optim_retvals=None):
self.model = model
self.params = params
self.cov_params = cov_params
self.optim_retvals = optim_retvals
self.fe_mean, self.vcp_mean, self.vc_mean = (
model._unpack(params))
if cov_params.ndim == 2:
cp = np.diag(cov_params)
else:
cp = cov_params
self.fe_sd, self.vcp_sd, self.vc_sd = model._unpack(cp)
self.fe_sd = np.sqrt(self.fe_sd)
self.vcp_sd = np.sqrt(self.vcp_sd)
self.vc_sd = np.sqrt(self.vc_sd)
[docs] def summary(self):
df = pd.DataFrame()
m = self.model.k_fep + self.model.k_vcp
df["Type"] = (["F" for k in range(self.model.k_fep)] +
["R" for k in range(self.model.k_vcp)])
df["Post. Mean"] = self.params[0:m]
if self.cov_params.ndim == 2:
v = np.diag(self.cov_params)[0:m]
df["Post. SD"] = np.sqrt(v)
else:
df["Post. SD"] = np.sqrt(self.cov_params[0:m])
# Convert variance parameters to natural scale
df["VC"] = np.exp(df["Post. Mean"])
df["VC (LB)"] = np.exp(df["Post. Mean"] - 2*df["Post. SD"])
df["VC (UB)"] = np.exp(df["Post. Mean"] + 2*df["Post. SD"])
df["VC"] = ["%.3f" % x for x in df.VC]
df["VC (LB)"] = ["%.3f" % x for x in df["VC (LB)"]]
df["VC (UB)"] = ["%.3f" % x for x in df["VC (UB)"]]
df.loc[df.index < self.model.k_fep, "VC"] = ""
df.loc[df.index < self.model.k_fep, "VC (LB)"] = ""
df.loc[df.index < self.model.k_fep, "VC (UB)"] = ""
df.index = self.model.fep_names + self.model.vcp_names
summ = summary2.Summary()
summ.add_title(self.model.family.__class__.__name__ +
" Mixed GLM Results")
summ.add_df(df)
return summ
[docs] def random_effects(self, term=None):
"""
Posterior mean and standard deviation of random effects.
Parameters
----------
term : int or None
If None, results for all random effects are returned. If
an integer, returns results for a given set of random
effects. The value of `term` refers to an element of the
`ident` vector, or to a position in the `vc_formulas`
list.
Returns
-------
Data frame of posterior means and posterior standard
deviations of random effects.
"""
z = self.vc_mean
s = self.vc_sd
na = self.model.vc_names
if term is not None:
termix = self.model.vcp_names.index(term)
ii = np.flatnonzero(self.model.ident == termix)
z = z[ii]
s = s[ii]
na = [na[i] for i in ii]
x = pd.DataFrame({"Mean": z, "SD": s})
if na is not None:
x.index = na
return x
[docs]class BinomialBayesMixedGLM(_VariationalBayesMixedGLM, _BayesMixedGLM):
__doc__ = _init_doc.format(example=_logit_example)
def __init__(self, endog, exog, exog_vc, ident, vcp_p=1,
fe_p=2, fep_names=None, vcp_names=None,
vc_names=None):
super(BinomialBayesMixedGLM, self).__init__(
endog, exog, exog_vc=exog_vc,
ident=ident, vcp_p=vcp_p, fe_p=fe_p,
family=families.Binomial(),
fep_names=fep_names, vcp_names=vcp_names,
vc_names=vc_names)
[docs] def vb_elbo(self, vb_mean, vb_sd):
"""
Returns the evidence lower bound (ELBO) for the model.
"""
fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean)
fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd)
tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd)
def h(z):
return -np.log(1 + np.exp(tm + np.sqrt(tv)*z))
return self.vb_elbo_base(
h, tm, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd)
[docs] def vb_elbo_grad(self, vb_mean, vb_sd):
"""
Returns the gradient of the model's evidence lower bound (ELBO).
"""
fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean)
fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd)
tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd)
def h(z):
u = tm + np.sqrt(tv)*z
x = np.zeros_like(u)
ii = np.flatnonzero(u > 0)
uu = u[ii]
x[ii] = 1 / (1 + np.exp(-uu))
ii = np.flatnonzero(u <= 0)
uu = u[ii]
x[ii] = np.exp(uu) / (1 + np.exp(uu))
return -x
return self.vb_elbo_grad_base(
h, tm, tv, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd)
[docs]class PoissonBayesMixedGLM(_VariationalBayesMixedGLM, _BayesMixedGLM):
__doc__ = _init_doc.format(example=_poisson_example)
def __init__(self, endog, exog, exog_vc, ident, vcp_p=1,
fe_p=2, fep_names=None, vcp_names=None):
super(PoissonBayesMixedGLM, self).__init__(
endog=endog, exog=exog, exog_vc=exog_vc,
ident=ident, vcp_p=vcp_p, fe_p=fe_p,
family=families.Poisson(),
fep_names=fep_names, vcp_names=vcp_names)
[docs] def vb_elbo(self, vb_mean, vb_sd):
"""
Returns the evidence lower bound (ELBO) for the model.
"""
fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean)
fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd)
tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd)
def h(z):
return -np.exp(tm + np.sqrt(tv)*z)
return self.vb_elbo_base(
h, tm, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd)
[docs] def vb_elbo_grad(self, vb_mean, vb_sd):
"""
Returns the gradient of the model's evidence lower bound (ELBO).
"""
fep_mean, vcp_mean, vc_mean = self._unpack(vb_mean)
fep_sd, vcp_sd, vc_sd = self._unpack(vb_sd)
tm, tv = self._lp_stats(fep_mean, fep_sd, vc_mean, vc_sd)
def h(z):
y = -np.exp(tm + np.sqrt(tv)*z)
return y
return self.vb_elbo_grad_base(
h, tm, tv, fep_mean, vcp_mean, vc_mean, fep_sd, vcp_sd, vc_sd)
