"""
Linear mixed effects models are regression models for dependent data.
They can be used to estimate regression relationships involving both
means and variances.
These models are also known as multilevel linear models, and
hierarchical linear models.
The MixedLM class fits linear mixed effects models to data, and
provides support for some common post-estimation tasks. This is a
group-based implementation that is most efficient for models in which
the data can be partitioned into independent groups. Some models with
crossed effects can be handled by specifying a model with a single
group.
The data are partitioned into disjoint groups. The probability model
for group i is:
Y = X*beta + Z*gamma + epsilon
where
* n_i is the number of observations in group i
* Y is a n_i dimensional response vector (called endog in MixedLM)
* X is a n_i x k_fe dimensional design matrix for the fixed effects
(called exog in MixedLM)
* beta is a k_fe-dimensional vector of fixed effects parameters
(called fe_params in MixedLM)
* Z is a design matrix for the random effects with n_i rows (called
exog_re in MixedLM). The number of columns in Z can vary by group
as discussed below.
* gamma is a random vector with mean 0. The covariance matrix for the
first `k_re` elements of `gamma` (called cov_re in MixedLM) is
common to all groups. The remaining elements of `gamma` are
variance components as discussed in more detail below. Each group
receives its own independent realization of gamma.
* epsilon is a n_i dimensional vector of iid normal
errors with mean 0 and variance sigma^2; the epsilon
values are independent both within and between groups
Y, X and Z must be entirely observed. beta, Psi, and sigma^2 are
estimated using ML or REML estimation, and gamma and epsilon are
random so define the probability model.
The marginal mean structure is E[Y | X, Z] = X*beta. If only the mean
structure is of interest, GEE is an alternative to using linear mixed
models.
Two types of random effects are supported. Standard random effects
are correlated with each other in arbitrary ways. Every group has the
same number (`k_re`) of standard random effects, with the same joint
distribution (but with independent realizations across the groups).
Variance components are uncorrelated with each other, and with the
standard random effects. Each variance component has mean zero, and
all realizations of a given variance component have the same variance
parameter. The number of realized variance components per variance
parameter can differ across the groups.
The primary reference for the implementation details is:
MJ Lindstrom, DM Bates (1988). "Newton Raphson and EM algorithms for
linear mixed effects models for repeated measures data". Journal of
the American Statistical Association. Volume 83, Issue 404, pages
1014-1022.
See also this more recent document:
http://econ.ucsb.edu/~doug/245a/Papers/Mixed%20Effects%20Implement.pdf
All the likelihood, gradient, and Hessian calculations closely follow
Lindstrom and Bates 1988, adapted to support variance components.
The following two documents are written more from the perspective of
users:
http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf
http://lme4.r-forge.r-project.org/slides/2009-07-07-Rennes/3Longitudinal-4.pdf
Notation:
* `cov_re` is the random effects covariance matrix (referred to above
as Psi) and `scale` is the (scalar) error variance. For a single
group, the marginal covariance matrix of endog given exog is scale*I
+ Z * cov_re * Z', where Z is the design matrix for the random
effects in one group.
* `vcomp` is a vector of variance parameters. The length of `vcomp`
is determined by the number of keys in either the `exog_vc` argument
to ``MixedLM``, or the `vc_formula` argument when using formulas to
fit a model.
Notes:
1. Three different parameterizations are used in different places.
The regression slopes (usually called `fe_params`) are identical in
all three parameterizations, but the variance parameters differ. The
parameterizations are:
* The "user parameterization" in which cov(endog) = scale*I + Z *
cov_re * Z', as described above. This is the main parameterization
visible to the user.
* The "profile parameterization" in which cov(endog) = I +
Z * cov_re1 * Z'. This is the parameterization of the profile
likelihood that is maximized to produce parameter estimates.
(see Lindstrom and Bates for details). The "user" cov_re is
equal to the "profile" cov_re1 times the scale.
* The "square root parameterization" in which we work with the Cholesky
factor of cov_re1 instead of cov_re directly. This is hidden from the
user.
All three parameterizations can be packed into a vector by
(optionally) concatenating `fe_params` together with the lower
triangle or Cholesky square root of the dependence structure, followed
by the variance parameters for the variance components. The are
stored as square roots if (and only if) the random effects covariance
matrix is stored as its Choleky factor. Note that when unpacking, it
is important to either square or reflect the dependence structure
depending on which parameterization is being used.
Two score methods are implemented. One takes the score with respect
to the elements of the random effects covariance matrix (used for
inference once the MLE is reached), and the other takes the score with
respect to the parameters of the Choleky square root of the random
effects covariance matrix (used for optimization).
The numerical optimization uses GLS to avoid explicitly optimizing
over the fixed effects parameters. The likelihood that is optimized
is profiled over both the scale parameter (a scalar) and the fixed
effects parameters (if any). As a result of this profiling, it is
difficult and unnecessary to calculate the Hessian of the profiled log
likelihood function, so that calculation is not implemented here.
Therefore, optimization methods requiring the Hessian matrix such as
the Newton-Raphson algorithm cannot be used for model fitting.
"""
import numpy as np
import statsmodels.base.model as base
from statsmodels.tools.decorators import cache_readonly
from statsmodels.tools import data as data_tools
from scipy.stats.distributions import norm
from scipy import sparse
import pandas as pd
import patsy
from collections import OrderedDict
import warnings
from statsmodels.tools.sm_exceptions import ConvergenceWarning
from statsmodels.base._penalties import Penalty
def _dot(x, y):
"""
Returns the dot product of the arrays, works for sparse and dense.
"""
if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
return np.dot(x, y)
elif sparse.issparse(x):
return x.dot(y)
elif sparse.issparse(y):
return y.T.dot(x.T).T
# From numpy, adapted to work with sparse and dense arrays.
def _multi_dot_three(A, B, C):
"""
Find best ordering for three arrays and do the multiplication.
Doing in manually instead of using dynamic programing is
approximately 15 times faster.
"""
# cost1 = cost((AB)C)
cost1 = (A.shape[0] * A.shape[1] * B.shape[1] + # (AB)
A.shape[0] * B.shape[1] * C.shape[1]) # (--)C
# cost2 = cost((AB)C)
cost2 = (B.shape[0] * B.shape[1] * C.shape[1] + # (BC)
A.shape[0] * A.shape[1] * C.shape[1]) # A(--)
if cost1 < cost2:
return _dot(_dot(A, B), C)
else:
return _dot(A, _dot(B, C))
def _dotsum(x, y):
"""
Returns sum(x * y), where '*' is the pointwise product, computed
efficiently for dense and sparse matrices.
"""
if sparse.issparse(x):
return x.multiply(y).sum()
else:
# This way usually avoids allocating a temporary.
return np.dot(x.ravel(), y.ravel())
class VCSpec(object):
"""
Define the variance component structure of a multilevel model.
An instance of the class contains three attributes:
- names : names[k] is the name of variance component k.
- mats : mats[k][i] is the design matrix for group index
i in variance component k.
- colnames : colnames[k][i] is the list of column names for
mats[k][i].
The groups in colnames and mats must be in sorted order.
"""
def __init__(self, names, colnames, mats):
self.names = names
self.colnames = colnames
self.mats = mats
def _get_exog_re_names(self, exog_re):
"""
Passes through if given a list of names. Otherwise, gets pandas names
or creates some generic variable names as needed.
"""
if self.k_re == 0:
return []
if isinstance(exog_re, pd.DataFrame):
return exog_re.columns.tolist()
elif isinstance(exog_re, pd.Series) and exog_re.name is not None:
return [exog_re.name]
elif isinstance(exog_re, list):
return exog_re
# Default names
defnames = ["x_re{0:1d}".format(k + 1) for k in range(exog_re.shape[1])]
return defnames
class MixedLMParams(object):
"""
This class represents a parameter state for a mixed linear model.
Parameters
----------
k_fe : int
The number of covariates with fixed effects.
k_re : int
The number of covariates with random coefficients (excluding
variance components).
k_vc : int
The number of variance components parameters.
Notes
-----
This object represents the parameter state for the model in which
the scale parameter has been profiled out.
"""
def __init__(self, k_fe, k_re, k_vc):
self.k_fe = k_fe
self.k_re = k_re
self.k_re2 = k_re * (k_re + 1) // 2
self.k_vc = k_vc
self.k_tot = self.k_fe + self.k_re2 + self.k_vc
self._ix = np.tril_indices(self.k_re)
def from_packed(params, k_fe, k_re, use_sqrt, has_fe):
"""
Create a MixedLMParams object from packed parameter vector.
Parameters
----------
params : array_like
The mode parameters packed into a single vector.
k_fe : int
The number of covariates with fixed effects
k_re : int
The number of covariates with random effects (excluding
variance components).
use_sqrt : bool
If True, the random effects covariance matrix is provided
as its Cholesky factor, otherwise the lower triangle of
the covariance matrix is stored.
has_fe : bool
If True, `params` contains fixed effects parameters.
Otherwise, the fixed effects parameters are set to zero.
Returns
-------
A MixedLMParams object.
"""
k_re2 = int(k_re * (k_re + 1) / 2)
# The number of covariance parameters.
if has_fe:
k_vc = len(params) - k_fe - k_re2
else:
k_vc = len(params) - k_re2
pa = MixedLMParams(k_fe, k_re, k_vc)
cov_re = np.zeros((k_re, k_re))
ix = pa._ix
if has_fe:
pa.fe_params = params[0:k_fe]
cov_re[ix] = params[k_fe:k_fe+k_re2]
else:
pa.fe_params = np.zeros(k_fe)
cov_re[ix] = params[0:k_re2]
if use_sqrt:
cov_re = np.dot(cov_re, cov_re.T)
else:
cov_re = (cov_re + cov_re.T) - np.diag(np.diag(cov_re))
pa.cov_re = cov_re
if k_vc > 0:
if use_sqrt:
pa.vcomp = params[-k_vc:]**2
else:
pa.vcomp = params[-k_vc:]
else:
pa.vcomp = np.array([])
return pa
from_packed = staticmethod(from_packed)
def from_components(fe_params=None, cov_re=None, cov_re_sqrt=None,
vcomp=None):
"""
Create a MixedLMParams object from each parameter component.
Parameters
----------
fe_params : array_like
The fixed effects parameter (a 1-dimensional array). If
None, there are no fixed effects.
cov_re : array_like
The random effects covariance matrix (a square, symmetric
2-dimensional array).
cov_re_sqrt : array_like
The Cholesky (lower triangular) square root of the random
effects covariance matrix.
vcomp : array_like
The variance component parameters. If None, there are no
variance components.
Returns
-------
A MixedLMParams object.
"""
if vcomp is None:
vcomp = np.empty(0)
if fe_params is None:
fe_params = np.empty(0)
if cov_re is None and cov_re_sqrt is None:
cov_re = np.empty((0, 0))
k_fe = len(fe_params)
k_vc = len(vcomp)
k_re = cov_re.shape[0] if cov_re is not None else cov_re_sqrt.shape[0]
pa = MixedLMParams(k_fe, k_re, k_vc)
pa.fe_params = fe_params
if cov_re_sqrt is not None:
pa.cov_re = np.dot(cov_re_sqrt, cov_re_sqrt.T)
elif cov_re is not None:
pa.cov_re = cov_re
pa.vcomp = vcomp
return pa
from_components = staticmethod(from_components)
def copy(self):
"""
Returns a copy of the object.
"""
obj = MixedLMParams(self.k_fe, self.k_re, self.k_vc)
obj.fe_params = self.fe_params.copy()
obj.cov_re = self.cov_re.copy()
obj.vcomp = self.vcomp.copy()
return obj
def get_packed(self, use_sqrt, has_fe=False):
"""
Return the model parameters packed into a single vector.
Parameters
----------
use_sqrt : bool
If True, the Cholesky square root of `cov_re` is
included in the packed result. Otherwise the
lower triangle of `cov_re` is included.
has_fe : bool
If True, the fixed effects parameters are included
in the packed result, otherwise they are omitted.
"""
if self.k_re > 0:
if use_sqrt:
L = np.linalg.cholesky(self.cov_re)
cpa = L[self._ix]
else:
cpa = self.cov_re[self._ix]
else:
cpa = np.zeros(0)
if use_sqrt:
vcomp = np.sqrt(self.vcomp)
else:
vcomp = self.vcomp
if has_fe:
pa = np.concatenate((self.fe_params, cpa, vcomp))
else:
pa = np.concatenate((cpa, vcomp))
return pa
def _smw_solver(s, A, AtA, Qi, di):
r"""
Returns a solver for the linear system:
.. math::
(sI + ABA^\prime) y = x
The returned function f satisfies f(x) = y as defined above.
B and its inverse matrix are block diagonal. The upper left block
of :math:`B^{-1}` is Qi and its lower right block is diag(di).
Parameters
----------
s : scalar
See above for usage
A : ndarray
p x q matrix, in general q << p, may be sparse.
AtA : square ndarray
:math:`A^\prime A`, a q x q matrix.
Qi : square symmetric ndarray
The matrix `B` is q x q, where q = r + d. `B` consists of a r
x r diagonal block whose inverse is `Qi`, and a d x d diagonal
block, whose inverse is diag(di).
di : 1d array_like
See documentation for Qi.
Returns
-------
A function for solving a linear system, as documented above.
Notes
-----
Uses Sherman-Morrison-Woodbury identity:
https://en.wikipedia.org/wiki/Woodbury_matrix_identity
"""
# Use SMW identity
qmat = AtA / s
if sparse.issparse(qmat):
qmat = qmat.todense()
m = Qi.shape[0]
qmat[0:m, 0:m] += Qi
d = qmat.shape[0]
qmat.flat[m*(d+1)::d+1] += di
if sparse.issparse(A):
qmati = sparse.linalg.spsolve(sparse.csc_matrix(qmat), A.T)
else:
qmati = np.linalg.solve(qmat, A.T)
if sparse.issparse(A):
def solver(rhs):
ql = qmati.dot(rhs)
ql = A.dot(ql)
return rhs / s - ql / s**2
else:
def solver(rhs):
ql = np.dot(qmati, rhs)
ql = np.dot(A, ql)
return rhs / s - ql / s**2
return solver
def _smw_logdet(s, A, AtA, Qi, di, B_logdet):
r"""
Returns the log determinant of
.. math::
sI + ABA^\prime
Uses the matrix determinant lemma to accelerate the calculation.
B is assumed to be positive definite, and s > 0, therefore the
determinant is positive.
Parameters
----------
s : positive scalar
See above for usage
A : ndarray
p x q matrix, in general q << p.
AtA : square ndarray
:math:`A^\prime A`, a q x q matrix.
Qi : square symmetric ndarray
The matrix `B` is q x q, where q = r + d. `B` consists of a r
x r diagonal block whose inverse is `Qi`, and a d x d diagonal
block, whose inverse is diag(di).
di : 1d array_like
See documentation for Qi.
B_logdet : real
The log determinant of B
Returns
-------
The log determinant of s*I + A*B*A'.
Notes
-----
Uses the matrix determinant lemma:
https://en.wikipedia.org/wiki/Matrix_determinant_lemma
"""
p = A.shape[0]
ld = p * np.log(s)
qmat = AtA / s
m = Qi.shape[0]
qmat[0:m, 0:m] += Qi
d = qmat.shape[0]
qmat.flat[m*(d+1)::d+1] += di
_, ld1 = np.linalg.slogdet(qmat)
return B_logdet + ld + ld1
def _convert_vc(exog_vc):
vc_names = []
vc_colnames = []
vc_mats = []
# Get the groups in sorted order
groups = set([])
for k, v in exog_vc.items():
groups |= set(v.keys())
groups = list(groups)
groups.sort()
for k, v in exog_vc.items():
vc_names.append(k)
colnames, mats = [], []
for g in groups:
try:
colnames.append(v[g].columns)
except AttributeError:
colnames.append([str(j) for j in range(v[g].shape[1])])
mats.append(v[g])
vc_colnames.append(colnames)
vc_mats.append(mats)
ii = np.argsort(vc_names)
vc_names = [vc_names[i] for i in ii]
vc_colnames = [vc_colnames[i] for i in ii]
vc_mats = [vc_mats[i] for i in ii]
return VCSpec(vc_names, vc_colnames, vc_mats)
class MixedLM(base.LikelihoodModel):
"""
Linear Mixed Effects Model
Parameters
----------
endog : 1d array_like
The dependent variable
exog : 2d array_like
A matrix of covariates used to determine the
mean structure (the "fixed effects" covariates).
groups : 1d array_like
A vector of labels determining the groups -- data from
different groups are independent
exog_re : 2d array_like
A matrix of covariates used to determine the variance and
covariance structure (the "random effects" covariates). If
None, defaults to a random intercept for each group.
exog_vc : VCSpec instance or dict-like (deprecated)
A VCSPec instance defines the structure of the variance
components in the model. Alternatively, see notes below
for a dictionary-based format. The dictionary format is
deprecated and may be removed at some point in the future.
use_sqrt : bool
If True, optimization is carried out using the lower
triangle of the square root of the random effects
covariance matrix, otherwise it is carried out using the
lower triangle of the random effects covariance matrix.
missing : str
The approach to missing data handling
Notes
-----
If `exog_vc` is not a `VCSpec` instance, then it must be a
dictionary of dictionaries. Specifically, `exog_vc[a][g]` is a
matrix whose columns are linearly combined using independent
random coefficients. This random term then contributes to the
variance structure of the data for group `g`. The random
coefficients all have mean zero, and have the same variance. The
matrix must be `m x k`, where `m` is the number of observations in
group `g`. The number of columns may differ among the top-level
groups.
The covariates in `exog`, `exog_re` and `exog_vc` may (but need
not) partially or wholly overlap.
`use_sqrt` should almost always be set to True. The main use case
for use_sqrt=False is when complicated patterns of fixed values in
the covariance structure are set (using the `free` argument to
`fit`) that cannot be expressed in terms of the Cholesky factor L.
Examples
--------
A basic mixed model with fixed effects for the columns of
``exog`` and a random intercept for each distinct value of
``group``:
>>> model = sm.MixedLM(endog, exog, groups)
>>> result = model.fit()
A mixed model with fixed effects for the columns of ``exog`` and
correlated random coefficients for the columns of ``exog_re``:
>>> model = sm.MixedLM(endog, exog, groups, exog_re=exog_re)
>>> result = model.fit()
A mixed model with fixed effects for the columns of ``exog`` and
independent random coefficients for the columns of ``exog_re``:
>>> free = MixedLMParams.from_components(
fe_params=np.ones(exog.shape[1]),
cov_re=np.eye(exog_re.shape[1]))
>>> model = sm.MixedLM(endog, exog, groups, exog_re=exog_re)
>>> result = model.fit(free=free)
A different way to specify independent random coefficients for the
columns of ``exog_re``. In this example ``groups`` must be a
Pandas Series with compatible indexing with ``exog_re``, and
``exog_re`` has two columns.
>>> g = pd.groupby(groups, by=groups).groups
>>> vc = {}
>>> vc['1'] = {k : exog_re.loc[g[k], 0] for k in g}
>>> vc['2'] = {k : exog_re.loc[g[k], 1] for k in g}
>>> model = sm.MixedLM(endog, exog, groups, vcomp=vc)
>>> result = model.fit()
"""
def __init__(self, endog, exog, groups, exog_re=None,
exog_vc=None, use_sqrt=True, missing='none',
**kwargs):
_allowed_kwargs = ["missing_idx", "design_info", "formula"]
for x in kwargs.keys():
if x not in _allowed_kwargs:
raise ValueError(
"argument %s not permitted for MixedLM initialization" % x)
self.use_sqrt = use_sqrt
# Some defaults
self.reml = True
self.fe_pen = None
self.re_pen = None
if isinstance(exog_vc, dict):
warnings.warn("Using deprecated variance components format")
# Convert from old to new representation
exog_vc = _convert_vc(exog_vc)
if exog_vc is not None:
self.k_vc = len(exog_vc.names)
self.exog_vc = exog_vc
else:
self.k_vc = 0
self.exog_vc = VCSpec([], [], [])
# If there is one covariate, it may be passed in as a column
# vector, convert these to 2d arrays.
# TODO: Can this be moved up in the class hierarchy?
# yes, it should be done up the hierarchy
if (exog is not None and
data_tools._is_using_ndarray_type(exog, None) and
exog.ndim == 1):
exog = exog[:, None]
if (exog_re is not None and
data_tools._is_using_ndarray_type(exog_re, None) and
exog_re.ndim == 1):
exog_re = exog_re[:, None]
# Calling super creates self.endog, etc. as ndarrays and the
# original exog, endog, etc. are self.data.endog, etc.
super(MixedLM, self).__init__(endog, exog, groups=groups,
exog_re=exog_re, missing=missing,
**kwargs)
self._init_keys.extend(["use_sqrt", "exog_vc"])
# Number of fixed effects parameters
self.k_fe = exog.shape[1]
if exog_re is None and len(self.exog_vc.names) == 0:
# Default random effects structure (random intercepts).
self.k_re = 1
self.k_re2 = 1
self.exog_re = np.ones((len(endog), 1), dtype=np.float64)
self.data.exog_re = self.exog_re
names = ['Group Var']
self.data.param_names = self.exog_names + names
self.data.exog_re_names = names
self.data.exog_re_names_full = names
elif exog_re is not None:
# Process exog_re the same way that exog is handled
# upstream
# TODO: this is wrong and should be handled upstream wholly
self.data.exog_re = exog_re
self.exog_re = np.asarray(exog_re)
if self.exog_re.ndim == 1:
self.exog_re = self.exog_re[:, None]
# Model dimensions
# Number of random effect covariates
self.k_re = self.exog_re.shape[1]
# Number of covariance parameters
self.k_re2 = self.k_re * (self.k_re + 1) // 2
else:
# All random effects are variance components
self.k_re = 0
self.k_re2 = 0
if not self.data._param_names:
# HACK: could have been set in from_formula already
# needs refactor
(param_names, exog_re_names,
exog_re_names_full) = self._make_param_names(exog_re)
self.data.param_names = param_names
self.data.exog_re_names = exog_re_names
self.data.exog_re_names_full = exog_re_names_full
self.k_params = self.k_fe + self.k_re2
# Convert the data to the internal representation, which is a
# list of arrays, corresponding to the groups.
group_labels = list(set(groups))
group_labels.sort()
row_indices = dict((s, []) for s in group_labels)
for i, g in enumerate(groups):
row_indices[g].append(i)
self.row_indices = row_indices
self.group_labels = group_labels
self.n_groups = len(self.group_labels)
# Split the data by groups
self.endog_li = self.group_list(self.endog)
self.exog_li = self.group_list(self.exog)
self.exog_re_li = self.group_list(self.exog_re)
# Precompute this.
if self.exog_re is None:
self.exog_re2_li = None
else:
self.exog_re2_li = [np.dot(x.T, x) for x in self.exog_re_li]
# The total number of observations, summed over all groups
self.nobs = len(self.endog)
self.n_totobs = self.nobs
# Set the fixed effects parameter names
if self.exog_names is None:
self.exog_names = ["FE%d" % (k + 1) for k in
range(self.exog.shape[1])]
# Precompute this
self._aex_r = []
self._aex_r2 = []
for i in range(self.n_groups):
a = self._augment_exog(i)
self._aex_r.append(a)
# This matrix is not very sparse so convert it to dense.
ma = _dot(a.T, a)
if sparse.issparse(ma):
ma = ma.todense()
self._aex_r2.append(ma)
# Precompute this
self._lin, self._quad = self._reparam()
def _make_param_names(self, exog_re):
"""
Returns the full parameter names list, just the exogenous random
effects variables, and the exogenous random effects variables with
the interaction terms.
"""
exog_names = list(self.exog_names)
exog_re_names = _get_exog_re_names(self, exog_re)
param_names = []
jj = self.k_fe
for i in range(len(exog_re_names)):
for j in range(i + 1):
if i == j:
param_names.append(exog_re_names[i] + " Var")
else:
param_names.append(exog_re_names[j] + " x " +
exog_re_names[i] + " Cov")
jj += 1
vc_names = [x + " Var" for x in self.exog_vc.names]
return exog_names + param_names + vc_names, exog_re_names, param_names
@classmethod
def from_formula(cls, formula, data, re_formula=None, vc_formula=None,
subset=None, use_sparse=False, missing='none', *args,
**kwargs):
"""
Create a Model from a formula and dataframe.
Parameters
----------
formula : str or generic Formula object
The formula specifying the model
data : array_like
The data for the model. See Notes.
re_formula : str
A one-sided formula defining the variance structure of the
model. The default gives a random intercept for each
group.
vc_formula : dict-like
Formulas describing variance components. `vc_formula[vc]` is
the formula for the component with variance parameter named
`vc`. The formula is processed into a matrix, and the columns
of this matrix are linearly combined with independent random
coefficients having mean zero and a common variance.
subset : array_like
An array-like object of booleans, integers, or index
values that indicate the subset of df to use in the
model. Assumes df is a `pandas.DataFrame`
missing : str
Either 'none' or 'drop'
args : extra arguments
These are passed to the model
kwargs : extra keyword arguments
These are passed to the model with one exception. The
``eval_env`` keyword is passed to patsy. It can be either a
:class:`patsy:patsy.EvalEnvironment` object or an integer
indicating the depth of the namespace to use. For example, the
default ``eval_env=0`` uses the calling namespace. If you wish
to use a "clean" environment set ``eval_env=-1``.
Returns
-------
model : Model instance
Notes
-----
`data` must define __getitem__ with the keys in the formula
terms args and kwargs are passed on to the model
instantiation. E.g., a numpy structured or rec array, a
dictionary, or a pandas DataFrame.
If the variance component is intended to produce random
intercepts for disjoint subsets of a group, specified by
string labels or a categorical data value, always use '0 +' in
the formula so that no overall intercept is included.
If the variance components specify random slopes and you do
not also want a random group-level intercept in the model,
then use '0 +' in the formula to exclude the intercept.
The variance components formulas are processed separately for
each group. If a variable is categorical the results will not
be affected by whether the group labels are distinct or
re-used over the top-level groups.
Examples
--------
Suppose we have data from an educational study with students
nested in classrooms nested in schools. The students take a
test, and we want to relate the test scores to the students'
ages, while accounting for the effects of classrooms and
schools. The school will be the top-level group, and the
classroom is a nested group that is specified as a variance
component. Note that the schools may have different number of
classrooms, and the classroom labels may (but need not be)
different across the schools.
>>> vc = {'classroom': '0 + C(classroom)'}
>>> MixedLM.from_formula('test_score ~ age', vc_formula=vc, \
re_formula='1', groups='school', data=data)
Now suppose we also have a previous test score called
'pretest'. If we want the relationship between pretest
scores and the current test to vary by classroom, we can
specify a random slope for the pretest score
>>> vc = {'classroom': '0 + C(classroom)', 'pretest': '0 + pretest'}
>>> MixedLM.from_formula('test_score ~ age + pretest', vc_formula=vc, \
re_formula='1', groups='school', data=data)
The following model is almost equivalent to the previous one,
but here the classroom random intercept and pretest slope may
be correlated.
>>> vc = {'classroom': '0 + C(classroom)'}
>>> MixedLM.from_formula('test_score ~ age + pretest', vc_formula=vc, \
re_formula='1 + pretest', groups='school', \
data=data)
"""
if "groups" not in kwargs.keys():
raise AttributeError("'groups' is a required keyword argument " +
"in MixedLM.from_formula")
groups = kwargs["groups"]
# If `groups` is a variable name, retrieve the data for the
# groups variable.
group_name = "Group"
if isinstance(groups, str):
group_name = groups
groups = np.asarray(data[groups])
else:
groups = np.asarray(groups)
del kwargs["groups"]
# Bypass all upstream missing data handling to properly handle
# variance components
if missing == 'drop':
data, groups = _handle_missing(data, groups, formula, re_formula,
vc_formula)
missing = 'none'
if re_formula is not None:
if re_formula.strip() == "1":
# Work around Patsy bug, fixed by 0.3.
exog_re = np.ones((data.shape[0], 1))
exog_re_names = [group_name]
else:
eval_env = kwargs.get('eval_env', None)
if eval_env is None:
eval_env = 1
elif eval_env == -1:
from patsy import EvalEnvironment
eval_env = EvalEnvironment({})
exog_re = patsy.dmatrix(re_formula, data, eval_env=eval_env)
exog_re_names = exog_re.design_info.column_names
exog_re_names = [x.replace("Intercept", group_name)
for x in exog_re_names]
exog_re = np.asarray(exog_re)
if exog_re.ndim == 1:
exog_re = exog_re[:, None]
else:
exog_re = None
if vc_formula is None:
exog_re_names = [group_name]
else:
exog_re_names = []
if vc_formula is not None:
eval_env = kwargs.get('eval_env', None)
if eval_env is None:
eval_env = 1
elif eval_env == -1:
from patsy import EvalEnvironment
eval_env = EvalEnvironment({})
vc_mats = []
vc_colnames = []
vc_names = []
gb = data.groupby(groups)
kylist = sorted(gb.groups.keys())
vcf = sorted(vc_formula.keys())
for vc_name in vcf:
md = patsy.ModelDesc.from_formula(vc_formula[vc_name])
vc_names.append(vc_name)
evc_mats, evc_colnames = [], []
for group_ix, group in enumerate(kylist):
ii = gb.groups[group]
mat = patsy.dmatrix(
md,
data.loc[ii, :],
eval_env=eval_env,
return_type='dataframe')
evc_colnames.append(mat.columns.tolist())
if use_sparse:
evc_mats.append(sparse.csr_matrix(mat))
else:
evc_mats.append(np.asarray(mat))
vc_mats.append(evc_mats)
vc_colnames.append(evc_colnames)
exog_vc = VCSpec(vc_names, vc_colnames, vc_mats)
else:
exog_vc = VCSpec([], [], [])
kwargs["subset"] = None
kwargs["exog_re"] = exog_re
kwargs["exog_vc"] = exog_vc
kwargs["groups"] = groups
mod = super(MixedLM, cls).from_formula(
formula, data, *args, **kwargs)
# expand re names to account for pairs of RE
(param_names,
exog_re_names,
exog_re_names_full) = mod._make_param_names(exog_re_names)
mod.data.param_names = param_names
mod.data.exog_re_names = exog_re_names
mod.data.exog_re_names_full = exog_re_names_full
if vc_formula is not None:
mod.data.vcomp_names = mod.exog_vc.names
return mod
def predict(self, params, exog=None):
"""
Return predicted values from a design matrix.
Parameters
----------
params : array_like
Parameters of a mixed linear model. Can be either a
MixedLMParams instance, or a vector containing the packed
model parameters in which the fixed effects parameters are
at the beginning of the vector, or a vector containing
only the fixed effects parameters.
exog : array_like, optional
Design / exogenous data for the fixed effects. Model exog
is used if None.
Returns
-------
An array of fitted values. Note that these predicted values
only reflect the fixed effects mean structure of the model.
"""
if exog is None:
exog = self.exog
if isinstance(params, MixedLMParams):
params = params.fe_params
else:
params = params[0:self.k_fe]
return np.dot(exog, params)
def group_list(self, array):
"""
Returns `array` split into subarrays corresponding to the
grouping structure.
"""
if array is None:
return None
if array.ndim == 1:
return [np.array(array[self.row_indices[k]])
for k in self.group_labels]
else:
return [np.array(array[self.row_indices[k], :])
for k in self.group_labels]
def fit_regularized(self, start_params=None, method='l1', alpha=0,
ceps=1e-4, ptol=1e-6, maxit=200, **fit_kwargs):
"""
Fit a model in which the fixed effects parameters are
penalized. The dependence parameters are held fixed at their
estimated values in the unpenalized model.
Parameters
----------
method : str of Penalty object
Method for regularization. If a string, must be 'l1'.
alpha : array_like
Scalar or vector of penalty weights. If a scalar, the
same weight is applied to all coefficients; if a vector,
it contains a weight for each coefficient. If method is a
Penalty object, the weights are scaled by alpha. For L1
regularization, the weights are used directly.
ceps : positive real scalar
Fixed effects parameters smaller than this value
in magnitude are treated as being zero.
ptol : positive real scalar
Convergence occurs when the sup norm difference
between successive values of `fe_params` is less than
`ptol`.
maxit : int
The maximum number of iterations.
fit_kwargs : keywords
Additional keyword arguments passed to fit.
Returns
-------
A MixedLMResults instance containing the results.
Notes
-----
The covariance structure is not updated as the fixed effects
parameters are varied.
The algorithm used here for L1 regularization is a"shooting"
or cyclic coordinate descent algorithm.
If method is 'l1', then `fe_pen` and `cov_pen` are used to
obtain the covariance structure, but are ignored during the
L1-penalized fitting.
References
----------
Friedman, J. H., Hastie, T. and Tibshirani, R. Regularized
Paths for Generalized Linear Models via Coordinate
Descent. Journal of Statistical Software, 33(1) (2008)
http://www.jstatsoft.org/v33/i01/paper
http://statweb.stanford.edu/~tibs/stat315a/Supplements/fuse.pdf
"""
if isinstance(method, str) and (method.lower() != 'l1'):
raise ValueError("Invalid regularization method")
# If method is a smooth penalty just optimize directly.
if isinstance(method, Penalty):
# Scale the penalty weights by alpha
method.alpha = alpha
fit_kwargs.update({"fe_pen": method})
return self.fit(**fit_kwargs)
if np.isscalar(alpha):
alpha = alpha * np.ones(self.k_fe, dtype=np.float64)
# Fit the unpenalized model to get the dependence structure.
mdf = self.fit(**fit_kwargs)
fe_params = mdf.fe_params
cov_re = mdf.cov_re
vcomp = mdf.vcomp
scale = mdf.scale
try:
cov_re_inv = np.linalg.inv(cov_re)
except np.linalg.LinAlgError:
cov_re_inv = None
for itr in range(maxit):
fe_params_s = fe_params.copy()
for j in range(self.k_fe):
if abs(fe_params[j]) < ceps:
continue
# The residuals
fe_params[j] = 0.
expval = np.dot(self.exog, fe_params)
resid_all = self.endog - expval
# The loss function has the form
# a*x^2 + b*x + pwt*|x|
a, b = 0., 0.
for group_ix, group in enumerate(self.group_labels):
vc_var = self._expand_vcomp(vcomp, group_ix)
exog = self.exog_li[group_ix]
ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix]
resid = resid_all[self.row_indices[group]]
solver = _smw_solver(scale, ex_r, ex2_r, cov_re_inv,
1 / vc_var)
x = exog[:, j]
u = solver(x)
a += np.dot(u, x)
b -= 2 * np.dot(u, resid)
pwt1 = alpha[j]
if b > pwt1:
fe_params[j] = -(b - pwt1) / (2 * a)
elif b < -pwt1:
fe_params[j] = -(b + pwt1) / (2 * a)
if np.abs(fe_params_s - fe_params).max() < ptol:
break
# Replace the fixed effects estimates with their penalized
# values, leave the dependence parameters in their unpenalized
# state.
params_prof = mdf.params.copy()
params_prof[0:self.k_fe] = fe_params
scale = self.get_scale(fe_params, mdf.cov_re_unscaled, mdf.vcomp)
# Get the Hessian including only the nonzero fixed effects,
# then blow back up to the full size after inverting.
hess = self.hessian(params_prof)
pcov = np.nan * np.ones_like(hess)
ii = np.abs(params_prof) > ceps
ii[self.k_fe:] = True
ii = np.flatnonzero(ii)
hess1 = hess[ii, :][:, ii]
pcov[np.ix_(ii, ii)] = np.linalg.inv(-hess1)
params_object = MixedLMParams.from_components(fe_params, cov_re=cov_re)
results = MixedLMResults(self, params_prof, pcov / scale)
results.params_object = params_object
results.fe_params = fe_params
results.cov_re = cov_re
results.scale = scale
results.cov_re_unscaled = mdf.cov_re_unscaled
results.method = mdf.method
results.converged = True
results.cov_pen = self.cov_pen
results.k_fe = self.k_fe
results.k_re = self.k_re
results.k_re2 = self.k_re2
results.k_vc = self.k_vc
return MixedLMResultsWrapper(results)
def get_fe_params(self, cov_re, vcomp):
"""
Use GLS to update the fixed effects parameter estimates.
Parameters
----------
cov_re : array_like
The covariance matrix of the random effects.
Returns
-------
The GLS estimates of the fixed effects parameters.
"""
if self.k_fe == 0:
return np.array([])
if self.k_re == 0:
cov_re_inv = np.empty((0, 0))
else:
cov_re_inv = np.linalg.inv(cov_re)
# Cache these quantities that do not change.
if not hasattr(self, "_endex_li"):
self._endex_li = []
for group_ix, _ in enumerate(self.group_labels):
mat = np.concatenate(
(self.exog_li[group_ix],
self.endog_li[group_ix][:, None]), axis=1)
self._endex_li.append(mat)
xtxy = 0.
for group_ix, group in enumerate(self.group_labels):
vc_var = self._expand_vcomp(vcomp, group_ix)
exog = self.exog_li[group_ix]
ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix]
solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var)
u = solver(self._endex_li[group_ix])
xtxy += np.dot(exog.T, u)
fe_params = np.linalg.solve(xtxy[:, 0:-1], xtxy[:, -1])
return fe_params
def _reparam(self):
"""
Returns parameters of the map converting parameters from the
form used in optimization to the form returned to the user.
Returns
-------
lin : list-like
Linear terms of the map
quad : list-like
Quadratic terms of the map
Notes
-----
If P are the standard form parameters and R are the
transformed parameters (i.e. with the Cholesky square root
covariance and square root transformed variance components),
then P[i] = lin[i] * R + R' * quad[i] * R
"""
k_fe, k_re, k_re2, k_vc = self.k_fe, self.k_re, self.k_re2, self.k_vc
k_tot = k_fe + k_re2 + k_vc
ix = np.tril_indices(self.k_re)
lin = []
for k in range(k_fe):
e = np.zeros(k_tot)
e[k] = 1
lin.append(e)
for k in range(k_re2):
lin.append(np.zeros(k_tot))
for k in range(k_vc):
lin.append(np.zeros(k_tot))
quad = []
# Quadratic terms for fixed effects.
for k in range(k_tot):
quad.append(np.zeros((k_tot, k_tot)))
# Quadratic terms for random effects covariance.
ii = np.tril_indices(k_re)
ix = [(a, b) for a, b in zip(ii[0], ii[1])]
for i1 in range(k_re2):
for i2 in range(k_re2):
ix1 = ix[i1]
ix2 = ix[i2]
if (ix1[1] == ix2[1]) and (ix1[0] <= ix2[0]):
ii = (ix2[0], ix1[0])
k = ix.index(ii)
quad[k_fe+k][k_fe+i2, k_fe+i1] += 1
for k in range(k_tot):
quad[k] = 0.5*(quad[k] + quad[k].T)
# Quadratic terms for variance components.
km = k_fe + k_re2
for k in range(km, km+k_vc):
quad[k][k, k] = 1
return lin, quad
def _expand_vcomp(self, vcomp, group_ix):
"""
Replicate variance parameters to match a group's design.
Parameters
----------
vcomp : array_like
The variance parameters for the variance components.
group_ix : int
The group index
Returns an expanded version of vcomp, in which each variance
parameter is copied as many times as there are independent
realizations of the variance component in the given group.
"""
if len(vcomp) == 0:
return np.empty(0)
vc_var = []
for j in range(len(self.exog_vc.names)):
d = self.exog_vc.mats[j][group_ix].shape[1]
vc_var.append(vcomp[j] * np.ones(d))
if len(vc_var) > 0:
return np.concatenate(vc_var)
else:
# Cannot reach here?
return np.empty(0)
def _augment_exog(self, group_ix):
"""
Concatenate the columns for variance components to the columns
for other random effects to obtain a single random effects
exog matrix for a given group.
"""
ex_r = self.exog_re_li[group_ix] if self.k_re > 0 else None
if self.k_vc == 0:
return ex_r
ex = [ex_r] if self.k_re > 0 else []
any_sparse = False
for j, _ in enumerate(self.exog_vc.names):
ex.append(self.exog_vc.mats[j][group_ix])
any_sparse |= sparse.issparse(ex[-1])
if any_sparse:
for j, x in enumerate(ex):
if not sparse.issparse(x):
ex[j] = sparse.csr_matrix(x)
ex = sparse.hstack(ex)
ex = sparse.csr_matrix(ex)
else:
ex = np.concatenate(ex, axis=1)
return ex
def loglike(self, params, profile_fe=True):
"""
Evaluate the (profile) log-likelihood of the linear mixed
effects model.
Parameters
----------
params : MixedLMParams, or array_like.
The parameter value. If array-like, must be a packed
parameter vector containing only the covariance
parameters.
profile_fe : bool
If True, replace the provided value of `fe_params` with
the GLS estimates.
Returns
-------
The log-likelihood value at `params`.
Notes
-----
The scale parameter `scale` is always profiled out of the
log-likelihood. In addition, if `profile_fe` is true the
fixed effects parameters are also profiled out.
"""
if type(params) is not MixedLMParams:
params = MixedLMParams.from_packed(params, self.k_fe,
self.k_re, self.use_sqrt,
has_fe=False)
cov_re = params.cov_re
vcomp = params.vcomp
# Move to the profile set
if profile_fe:
fe_params = self.get_fe_params(cov_re, vcomp)
else:
fe_params = params.fe_params
if self.k_re > 0:
try:
cov_re_inv = np.linalg.inv(cov_re)
except np.linalg.LinAlgError:
cov_re_inv = None
_, cov_re_logdet = np.linalg.slogdet(cov_re)
else:
cov_re_inv = np.zeros((0, 0))
cov_re_logdet = 0
# The residuals
expval = np.dot(self.exog, fe_params)
resid_all = self.endog - expval
likeval = 0.
# Handle the covariance penalty
if (self.cov_pen is not None) and (self.k_re > 0):
likeval -= self.cov_pen.func(cov_re, cov_re_inv)
# Handle the fixed effects penalty
if (self.fe_pen is not None):
likeval -= self.fe_pen.func(fe_params)
xvx, qf = 0., 0.
for group_ix, group in enumerate(self.group_labels):
vc_var = self._expand_vcomp(vcomp, group_ix)
cov_aug_logdet = cov_re_logdet + np.sum(np.log(vc_var))
exog = self.exog_li[group_ix]
ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix]
solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var)
resid = resid_all[self.row_indices[group]]
# Part 1 of the log likelihood (for both ML and REML)
ld = _smw_logdet(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var,
cov_aug_logdet)
likeval -= ld / 2.
# Part 2 of the log likelihood (for both ML and REML)
u = solver(resid)
qf += np.dot(resid, u)
# Adjustment for REML
if self.reml:
mat = solver(exog)
xvx += np.dot(exog.T, mat)
if self.reml:
likeval -= (self.n_totobs - self.k_fe) * np.log(qf) / 2.
_, ld = np.linalg.slogdet(xvx)
likeval -= ld / 2.
likeval -= (self.n_totobs - self.k_fe) * np.log(2 * np.pi) / 2.
likeval += ((self.n_totobs - self.k_fe) *
np.log(self.n_totobs - self.k_fe) / 2.)
likeval -= (self.n_totobs - self.k_fe) / 2.
else:
likeval -= self.n_totobs * np.log(qf) / 2.
likeval -= self.n_totobs * np.log(2 * np.pi) / 2.
likeval += self.n_totobs * np.log(self.n_totobs) / 2.
likeval -= self.n_totobs / 2.
return likeval
def _gen_dV_dPar(self, ex_r, solver, group_ix, max_ix=None):
"""
A generator that yields the element-wise derivative of the
marginal covariance matrix with respect to the random effects
variance and covariance parameters.
ex_r : array_like
The random effects design matrix
solver : function
A function that given x returns V^{-1}x, where V
is the group's marginal covariance matrix.
group_ix : int
The group index
max_ix : {int, None}
If not None, the generator ends when this index
is reached.
"""
axr = solver(ex_r)
# Regular random effects
jj = 0
for j1 in range(self.k_re):
for j2 in range(j1 + 1):
if max_ix is not None and jj > max_ix:
return
# Need 2d
mat_l, mat_r = ex_r[:, j1:j1+1], ex_r[:, j2:j2+1]
vsl, vsr = axr[:, j1:j1+1], axr[:, j2:j2+1]
yield jj, mat_l, mat_r, vsl, vsr, j1 == j2
jj += 1
# Variance components
for j, _ in enumerate(self.exog_vc.names):
if max_ix is not None and jj > max_ix:
return
mat = self.exog_vc.mats[j][group_ix]
axmat = solver(mat)
yield jj, mat, mat, axmat, axmat, True
jj += 1
def score(self, params, profile_fe=True):
"""
Returns the score vector of the profile log-likelihood.
Notes
-----
The score vector that is returned is computed with respect to
the parameterization defined by this model instance's
`use_sqrt` attribute.
"""
if type(params) is not MixedLMParams:
params = MixedLMParams.from_packed(
params, self.k_fe, self.k_re, self.use_sqrt,
has_fe=False)
if profile_fe:
params.fe_params = self.get_fe_params(params.cov_re, params.vcomp)
if self.use_sqrt:
score_fe, score_re, score_vc = self.score_sqrt(
params, calc_fe=not profile_fe)
else:
score_fe, score_re, score_vc = self.score_full(
params, calc_fe=not profile_fe)
if self._freepat is not None:
score_fe *= self._freepat.fe_params
score_re *= self._freepat.cov_re[self._freepat._ix]
score_vc *= self._freepat.vcomp
if profile_fe:
return np.concatenate((score_re, score_vc))
else:
return np.concatenate((score_fe, score_re, score_vc))
def score_full(self, params, calc_fe):
"""
Returns the score with respect to untransformed parameters.
Calculates the score vector for the profiled log-likelihood of
the mixed effects model with respect to the parameterization
in which the random effects covariance matrix is represented
in its full form (not using the Cholesky factor).
Parameters
----------
params : MixedLMParams or array_like
The parameter at which the score function is evaluated.
If array-like, must contain the packed random effects
parameters (cov_re and vcomp) without fe_params.
calc_fe : bool
If True, calculate the score vector for the fixed effects
parameters. If False, this vector is not calculated, and
a vector of zeros is returned in its place.
Returns
-------
score_fe : array_like
The score vector with respect to the fixed effects
parameters.
score_re : array_like
The score vector with respect to the random effects
parameters (excluding variance components parameters).
score_vc : array_like
The score vector with respect to variance components
parameters.
Notes
-----
`score_re` is taken with respect to the parameterization in
which `cov_re` is represented through its lower triangle
(without taking the Cholesky square root).
"""
fe_params = params.fe_params
cov_re = params.cov_re
vcomp = params.vcomp
try:
cov_re_inv = np.linalg.inv(cov_re)
except np.linalg.LinAlgError:
cov_re_inv = None
score_fe = np.zeros(self.k_fe)
score_re = np.zeros(self.k_re2)
score_vc = np.zeros(self.k_vc)
# Handle the covariance penalty.
if self.cov_pen is not None:
score_re -= self.cov_pen.deriv(cov_re, cov_re_inv)
# Handle the fixed effects penalty.
if calc_fe and (self.fe_pen is not None):
score_fe -= self.fe_pen.deriv(fe_params)
# resid' V^{-1} resid, summed over the groups (a scalar)
rvir = 0.
# exog' V^{-1} resid, summed over the groups (a k_fe
# dimensional vector)
xtvir = 0.
# exog' V^{_1} exog, summed over the groups (a k_fe x k_fe
# matrix)
xtvix = 0.
# V^{-1} exog' dV/dQ_jj exog V^{-1}, where Q_jj is the jj^th
# covariance parameter.
xtax = [0., ] * (self.k_re2 + self.k_vc)
# Temporary related to the gradient of log |V|
dlv = np.zeros(self.k_re2 + self.k_vc)
# resid' V^{-1} dV/dQ_jj V^{-1} resid (a scalar)
rvavr = np.zeros(self.k_re2 + self.k_vc)
for group_ix, group in enumerate(self.group_labels):
vc_var = self._expand_vcomp(vcomp, group_ix)
exog = self.exog_li[group_ix]
ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix]
solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var)
# The residuals
resid = self.endog_li[group_ix]
if self.k_fe > 0:
expval = np.dot(exog, fe_params)
resid = resid - expval
if self.reml:
viexog = solver(exog)
xtvix += np.dot(exog.T, viexog)
# Contributions to the covariance parameter gradient
vir = solver(resid)
for (jj, matl, matr, vsl, vsr, sym) in\
self._gen_dV_dPar(ex_r, solver, group_ix):
dlv[jj] = _dotsum(matr, vsl)
if not sym:
dlv[jj] += _dotsum(matl, vsr)
ul = _dot(vir, matl)
ur = ul.T if sym else _dot(matr.T, vir)
ulr = np.dot(ul, ur)
rvavr[jj] += ulr
if not sym:
rvavr[jj] += ulr.T
if self.reml:
ul = _dot(viexog.T, matl)
ur = ul.T if sym else _dot(matr.T, viexog)
ulr = np.dot(ul, ur)
xtax[jj] += ulr
if not sym:
xtax[jj] += ulr.T
# Contribution of log|V| to the covariance parameter
# gradient.
if self.k_re > 0:
score_re -= 0.5 * dlv[0:self.k_re2]
if self.k_vc > 0:
score_vc -= 0.5 * dlv[self.k_re2:]
rvir += np.dot(resid, vir)
if calc_fe:
xtvir += np.dot(exog.T, vir)
fac = self.n_totobs
if self.reml:
fac -= self.k_fe
if calc_fe and self.k_fe > 0:
score_fe += fac * xtvir / rvir
if self.k_re > 0:
score_re += 0.5 * fac * rvavr[0:self.k_re2] / rvir
if self.k_vc > 0:
score_vc += 0.5 * fac * rvavr[self.k_re2:] / rvir
if self.reml:
xtvixi = np.linalg.inv(xtvix)
for j in range(self.k_re2):
score_re[j] += 0.5 * _dotsum(xtvixi.T, xtax[j])
for j in range(self.k_vc):
score_vc[j] += 0.5 * _dotsum(xtvixi.T, xtax[self.k_re2 + j])
return score_fe, score_re, score_vc
def score_sqrt(self, params, calc_fe=True):
"""
Returns the score with respect to transformed parameters.
Calculates the score vector with respect to the
parameterization in which the random effects covariance matrix
is represented through its Cholesky square root.
Parameters
----------
params : MixedLMParams or array_like
The model parameters. If array-like must contain packed
parameters that are compatible with this model instance.
calc_fe : bool
If True, calculate the score vector for the fixed effects
parameters. If False, this vector is not calculated, and
a vector of zeros is returned in its place.
Returns
-------
score_fe : array_like
The score vector with respect to the fixed effects
parameters.
score_re : array_like
The score vector with respect to the random effects
parameters (excluding variance components parameters).
score_vc : array_like
The score vector with respect to variance components
parameters.
"""
score_fe, score_re, score_vc = self.score_full(params, calc_fe=calc_fe)
params_vec = params.get_packed(use_sqrt=True, has_fe=True)
score_full = np.concatenate((score_fe, score_re, score_vc))
scr = 0.
for i in range(len(params_vec)):
v = self._lin[i] + 2 * np.dot(self._quad[i], params_vec)
scr += score_full[i] * v
score_fe = scr[0:self.k_fe]
score_re = scr[self.k_fe:self.k_fe + self.k_re2]
score_vc = scr[self.k_fe + self.k_re2:]
return score_fe, score_re, score_vc
def hessian(self, params):
"""
Returns the model's Hessian matrix.
Calculates the Hessian matrix for the linear mixed effects
model with respect to the parameterization in which the
covariance matrix is represented directly (without square-root
transformation).
Parameters
----------
params : MixedLMParams or array_like
The model parameters at which the Hessian is calculated.
If array-like, must contain the packed parameters in a
form that is compatible with this model instance.
Returns
-------
hess : 2d ndarray
The Hessian matrix, evaluated at `params`.
"""
if type(params) is not MixedLMParams:
params = MixedLMParams.from_packed(params, self.k_fe, self.k_re,
use_sqrt=self.use_sqrt,
has_fe=True)
fe_params = params.fe_params
vcomp = params.vcomp
cov_re = params.cov_re
if self.k_re > 0:
cov_re_inv = np.linalg.inv(cov_re)
else:
cov_re_inv = np.empty((0, 0))
# Blocks for the fixed and random effects parameters.
hess_fe = 0.
hess_re = np.zeros((self.k_re2 + self.k_vc, self.k_re2 + self.k_vc))
hess_fere = np.zeros((self.k_re2 + self.k_vc, self.k_fe))
fac = self.n_totobs
if self.reml:
fac -= self.exog.shape[1]
rvir = 0.
xtvix = 0.
xtax = [0., ] * (self.k_re2 + self.k_vc)
m = self.k_re2 + self.k_vc
B = np.zeros(m)
D = np.zeros((m, m))
F = [[0.] * m for k in range(m)]
for group_ix, group in enumerate(self.group_labels):
vc_var = self._expand_vcomp(vcomp, group_ix)
exog = self.exog_li[group_ix]
ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix]
solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var)
# The residuals
resid = self.endog_li[group_ix]
if self.k_fe > 0:
expval = np.dot(exog, fe_params)
resid = resid - expval
viexog = solver(exog)
xtvix += np.dot(exog.T, viexog)
vir = solver(resid)
rvir += np.dot(resid, vir)
for (jj1, matl1, matr1, vsl1, vsr1, sym1) in\
self._gen_dV_dPar(ex_r, solver, group_ix):
ul = _dot(viexog.T, matl1)
ur = _dot(matr1.T, vir)
hess_fere[jj1, :] += np.dot(ul, ur)
if not sym1:
ul = _dot(viexog.T, matr1)
ur = _dot(matl1.T, vir)
hess_fere[jj1, :] += np.dot(ul, ur)
if self.reml:
ul = _dot(viexog.T, matl1)
ur = ul if sym1 else np.dot(viexog.T, matr1)
ulr = _dot(ul, ur.T)
xtax[jj1] += ulr
if not sym1:
xtax[jj1] += ulr.T
ul = _dot(vir, matl1)
ur = ul if sym1 else _dot(vir, matr1)
B[jj1] += np.dot(ul, ur) * (1 if sym1 else 2)
# V^{-1} * dV/d_theta
E = [(vsl1, matr1)]
if not sym1:
E.append((vsr1, matl1))
for (jj2, matl2, matr2, vsl2, vsr2, sym2) in\
self._gen_dV_dPar(ex_r, solver, group_ix, jj1):
re = sum([_multi_dot_three(matr2.T, x[0], x[1].T)
for x in E])
vt = 2 * _dot(_multi_dot_three(vir[None, :], matl2, re),
vir[:, None])
if not sym2:
le = sum([_multi_dot_three(matl2.T, x[0], x[1].T)
for x in E])
vt += 2 * _dot(_multi_dot_three(
vir[None, :], matr2, le), vir[:, None])
D[jj1, jj2] += vt
if jj1 != jj2:
D[jj2, jj1] += vt
rt = _dotsum(vsl2, re.T) / 2
if not sym2:
rt += _dotsum(vsr2, le.T) / 2
hess_re[jj1, jj2] += rt
if jj1 != jj2:
hess_re[jj2, jj1] += rt
if self.reml:
ev = sum([_dot(x[0], _dot(x[1].T, viexog)) for x in E])
u1 = _dot(viexog.T, matl2)
u2 = _dot(matr2.T, ev)
um = np.dot(u1, u2)
F[jj1][jj2] += um + um.T
if not sym2:
u1 = np.dot(viexog.T, matr2)
u2 = np.dot(matl2.T, ev)
um = np.dot(u1, u2)
F[jj1][jj2] += um + um.T
hess_fe -= fac * xtvix / rvir
hess_re = hess_re - 0.5 * fac * (D/rvir - np.outer(B, B) / rvir**2)
hess_fere = -fac * hess_fere / rvir
if self.reml:
QL = [np.linalg.solve(xtvix, x) for x in xtax]
for j1 in range(self.k_re2 + self.k_vc):
for j2 in range(j1 + 1):
a = _dotsum(QL[j1].T, QL[j2])
a -= np.trace(np.linalg.solve(xtvix, F[j1][j2]))
a *= 0.5
hess_re[j1, j2] += a
if j1 > j2:
hess_re[j2, j1] += a
# Put the blocks together to get the Hessian.
m = self.k_fe + self.k_re2 + self.k_vc
hess = np.zeros((m, m))
hess[0:self.k_fe, 0:self.k_fe] = hess_fe
hess[0:self.k_fe, self.k_fe:] = hess_fere.T
hess[self.k_fe:, 0:self.k_fe] = hess_fere
hess[self.k_fe:, self.k_fe:] = hess_re
return hess
def get_scale(self, fe_params, cov_re, vcomp):
"""
Returns the estimated error variance based on given estimates
of the slopes and random effects covariance matrix.
Parameters
----------
fe_params : array_like
The regression slope estimates
cov_re : 2d array_like
Estimate of the random effects covariance matrix
vcomp : array_like
Estimate of the variance components
Returns
-------
scale : float
The estimated error variance.
"""
try:
cov_re_inv = np.linalg.inv(cov_re)
except np.linalg.LinAlgError:
cov_re_inv = None
qf = 0.
for group_ix, group in enumerate(self.group_labels):
vc_var = self._expand_vcomp(vcomp, group_ix)
exog = self.exog_li[group_ix]
ex_r, ex2_r = self._aex_r[group_ix], self._aex_r2[group_ix]
solver = _smw_solver(1., ex_r, ex2_r, cov_re_inv, 1 / vc_var)
# The residuals
resid = self.endog_li[group_ix]
if self.k_fe > 0:
expval = np.dot(exog, fe_params)
resid = resid - expval
mat = solver(resid)
qf += np.dot(resid, mat)
if self.reml:
qf /= (self.n_totobs - self.k_fe)
else:
qf /= self.n_totobs
return qf
def fit(self, start_params=None, reml=True, niter_sa=0,
do_cg=True, fe_pen=None, cov_pen=None, free=None,
full_output=False, method=None, **kwargs):
"""
Fit a linear mixed model to the data.
Parameters
----------
start_params : array_like or MixedLMParams
Starting values for the profile log-likelihood. If not a
`MixedLMParams` instance, this should be an array
containing the packed parameters for the profile
log-likelihood, including the fixed effects
parameters.
reml : bool
If true, fit according to the REML likelihood, else
fit the standard likelihood using ML.
niter_sa : int
Currently this argument is ignored and has no effect
on the results.
cov_pen : CovariancePenalty object
A penalty for the random effects covariance matrix
do_cg : bool, defaults to True
If False, the optimization is skipped and a results
object at the given (or default) starting values is
returned.
fe_pen : Penalty object
A penalty on the fixed effects
free : MixedLMParams object
If not `None`, this is a mask that allows parameters to be
held fixed at specified values. A 1 indicates that the
corresponding parameter is estimated, a 0 indicates that
it is fixed at its starting value. Setting the `cov_re`
component to the identity matrix fits a model with
independent random effects. Note that some optimization
methods do not respect this constraint (bfgs and lbfgs both
work).
full_output : bool
If true, attach iteration history to results
method : str
Optimization method. Can be a scipy.optimize method name,
or a list of such names to be tried in sequence.
Returns
-------
A MixedLMResults instance.
"""
_allowed_kwargs = ['gtol', 'maxiter', 'eps', 'maxcor', 'ftol',
'tol', 'disp', 'maxls']
for x in kwargs.keys():
if x not in _allowed_kwargs:
warnings.warn("Argument %s not used by MixedLM.fit" % x)
if method is None:
method = ['bfgs', 'lbfgs', 'cg']
elif isinstance(method, str):
method = [method]
for meth in method:
if meth.lower() in ["newton", "ncg"]:
raise ValueError(
"method %s not available for MixedLM" % meth)
self.reml = reml
self.cov_pen = cov_pen
self.fe_pen = fe_pen
self._freepat = free
if full_output:
hist = []
else:
hist = None
if start_params is None:
params = MixedLMParams(self.k_fe, self.k_re, self.k_vc)
params.fe_params = np.zeros(self.k_fe)
params.cov_re = np.eye(self.k_re)
params.vcomp = np.ones(self.k_vc)
else:
if isinstance(start_params, MixedLMParams):
params = start_params
else:
# It's a packed array
if len(start_params) == self.k_fe + self.k_re2 + self.k_vc:
params = MixedLMParams.from_packed(
start_params, self.k_fe, self.k_re, self.use_sqrt,
has_fe=True)
elif len(start_params) == self.k_re2 + self.k_vc:
params = MixedLMParams.from_packed(
start_params, self.k_fe, self.k_re, self.use_sqrt,
has_fe=False)
else:
raise ValueError("invalid start_params")
if do_cg:
kwargs["retall"] = hist is not None
if "disp" not in kwargs:
kwargs["disp"] = False
packed = params.get_packed(use_sqrt=self.use_sqrt, has_fe=False)
if niter_sa > 0:
warnings.warn("niter_sa is currently ignored")
# Try optimizing one or more times
for j in range(len(method)):
rslt = super(MixedLM, self).fit(start_params=packed,
skip_hessian=True,
method=method[j],
**kwargs)
if rslt.mle_retvals['converged']:
break
packed = rslt.params
if j + 1 < len(method):
next_method = method[j + 1]
warnings.warn(
"Retrying MixedLM optimization with %s" % next_method,
ConvergenceWarning)
else:
msg = ("MixedLM optimization failed, " +
"trying a different optimizer may help.")
warnings.warn(msg, ConvergenceWarning)
# The optimization succeeded
params = np.atleast_1d(rslt.params)
if hist is not None:
hist.append(rslt.mle_retvals)
converged = rslt.mle_retvals['converged']
if not converged:
gn = self.score(rslt.params)
gn = np.sqrt(np.sum(gn**2))
msg = "Gradient optimization failed, |grad| = %f" % gn
warnings.warn(msg, ConvergenceWarning)
# Convert to the final parameterization (i.e. undo the square
# root transform of the covariance matrix, and the profiling
# over the error variance).
params = MixedLMParams.from_packed(
params, self.k_fe, self.k_re, use_sqrt=self.use_sqrt, has_fe=False)
cov_re_unscaled = params.cov_re
vcomp_unscaled = params.vcomp
fe_params = self.get_fe_params(cov_re_unscaled, vcomp_unscaled)
params.fe_params = fe_params
scale = self.get_scale(fe_params, cov_re_unscaled, vcomp_unscaled)
cov_re = scale * cov_re_unscaled
vcomp = scale * vcomp_unscaled
f1 = (self.k_re > 0) and (np.min(np.abs(np.diag(cov_re))) < 0.01)
f2 = (self.k_vc > 0) and (np.min(np.abs(vcomp)) < 0.01)
if f1 or f2:
msg = "The MLE may be on the boundary of the parameter space."
warnings.warn(msg, ConvergenceWarning)
# Compute the Hessian at the MLE. Note that this is the
# Hessian with respect to the random effects covariance matrix
# (not its square root). It is used for obtaining standard
# errors, not for optimization.
hess = self.hessian(params)
hess_diag = np.diag(hess)
if free is not None:
pcov = np.zeros_like(hess)
pat = self._freepat.get_packed(use_sqrt=False, has_fe=True)
ii = np.flatnonzero(pat)
hess_diag = hess_diag[ii]
if len(ii) > 0:
hess1 = hess[np.ix_(ii, ii)]
pcov[np.ix_(ii, ii)] = np.linalg.inv(-hess1)
else:
pcov = np.linalg.inv(-hess)
if np.any(hess_diag >= 0):
msg = ("The Hessian matrix at the estimated parameter values " +
"is not positive definite.")
warnings.warn(msg, ConvergenceWarning)
# Prepare a results class instance
params_packed = params.get_packed(use_sqrt=False, has_fe=True)
results = MixedLMResults(self, params_packed, pcov / scale)
results.params_object = params
results.fe_params = fe_params
results.cov_re = cov_re
results.vcomp = vcomp
results.scale = scale
results.cov_re_unscaled = cov_re_unscaled
results.method = "REML" if self.reml else "ML"
results.converged = converged
results.hist = hist
results.reml = self.reml
results.cov_pen = self.cov_pen
results.k_fe = self.k_fe
results.k_re = self.k_re
results.k_re2 = self.k_re2
results.k_vc = self.k_vc
results.use_sqrt = self.use_sqrt
results.freepat = self._freepat
return MixedLMResultsWrapper(results)
def get_distribution(self, params, scale, exog):
return _mixedlm_distribution(self, params, scale, exog)
class _mixedlm_distribution(object):
"""
A private class for simulating data from a given mixed linear model.
Parameters
----------
model : MixedLM instance
A mixed linear model
params : array_like
A parameter vector defining a mixed linear model. See
notes for more information.
scale : scalar
The unexplained variance
exog : array_like
An array of fixed effect covariates. If None, model.exog
is used.
Notes
-----
The params array is a vector containing fixed effects parameters,
random effects parameters, and variance component parameters, in
that order. The lower triangle of the random effects covariance
matrix is stored. The random effects and variance components
parameters are divided by the scale parameter.
This class is used in Mediation, and possibly elsewhere.
"""
def __init__(self, model, params, scale, exog):
self.model = model
self.exog = exog if exog is not None else model.exog
po = MixedLMParams.from_packed(
params, model.k_fe, model.k_re, False, True)
self.fe_params = po.fe_params
self.cov_re = scale * po.cov_re
self.vcomp = scale * po.vcomp
self.scale = scale
group_idx = np.zeros(model.nobs, dtype=np.int)
for k, g in enumerate(model.group_labels):
group_idx[model.row_indices[g]] = k
self.group_idx = group_idx
def rvs(self, n):
"""
Return a vector of simulated values from a mixed linear
model.
The parameter n is ignored, but required by the interface
"""
model = self.model
# Fixed effects
y = np.dot(self.exog, self.fe_params)
# Random effects
u = np.random.normal(size=(model.n_groups, model.k_re))
u = np.dot(u, np.linalg.cholesky(self.cov_re).T)
y += (u[self.group_idx, :] * model.exog_re).sum(1)
# Variance components
for j, _ in enumerate(model.exog_vc.names):
ex = model.exog_vc.mats[j]
v = self.vcomp[j]
for i, g in enumerate(model.group_labels):
exg = ex[i]
ii = model.row_indices[g]
u = np.random.normal(size=exg.shape[1])
y[ii] += np.sqrt(v) * np.dot(exg, u)
# Residual variance
y += np.sqrt(self.scale) * np.random.normal(size=len(y))
return y
class MixedLMResults(base.LikelihoodModelResults, base.ResultMixin):
'''
Class to contain results of fitting a linear mixed effects model.
MixedLMResults inherits from statsmodels.LikelihoodModelResults
Parameters
----------
See statsmodels.LikelihoodModelResults
Attributes
----------
model : class instance
Pointer to MixedLM model instance that called fit.
normalized_cov_params : array
The sampling covariance matrix of the estimates
params : array
A packed parameter vector for the profile parameterization.
The first `k_fe` elements are the estimated fixed effects
coefficients. The remaining elements are the estimated
variance parameters. The variance parameters are all divided
by `scale` and are not the variance parameters shown
in the summary.
fe_params : array
The fitted fixed-effects coefficients
cov_re : array
The fitted random-effects covariance matrix
bse_fe : array
The standard errors of the fitted fixed effects coefficients
bse_re : array
The standard errors of the fitted random effects covariance
matrix and variance components. The first `k_re * (k_re + 1)`
parameters are the standard errors for the lower triangle of
`cov_re`, the remaining elements are the standard errors for
the variance components.
See Also
--------
statsmodels.LikelihoodModelResults
'''
def __init__(self, model, params, cov_params):
super(MixedLMResults, self).__init__(model, params,
normalized_cov_params=cov_params)
self.nobs = self.model.nobs
self.df_resid = self.nobs - np.linalg.matrix_rank(self.model.exog)
@cache_readonly
def fittedvalues(self):
"""
Returns the fitted values for the model.
The fitted values reflect the mean structure specified by the
fixed effects and the predicted random effects.
"""
fit = np.dot(self.model.exog, self.fe_params)
re = self.random_effects
for group_ix, group in enumerate(self.model.group_labels):
ix = self.model.row_indices[group]
mat = []
if self.model.exog_re_li is not None:
mat.append(self.model.exog_re_li[group_ix])
for j in range(self.k_vc):
mat.append(self.model.exog_vc.mats[j][group_ix])
mat = np.concatenate(mat, axis=1)
fit[ix] += np.dot(mat, re[group])
return fit
@cache_readonly
def resid(self):
"""
Returns the residuals for the model.
The residuals reflect the mean structure specified by the
fixed effects and the predicted random effects.
"""
return self.model.endog - self.fittedvalues
@cache_readonly
def bse_fe(self):
"""
Returns the standard errors of the fixed effect regression
coefficients.
"""
p = self.model.exog.shape[1]
return np.sqrt(np.diag(self.cov_params())[0:p])
@cache_readonly
def bse_re(self):
"""
Returns the standard errors of the variance parameters.
The first `k_re x (k_re + 1)` elements of the returned array
are the standard errors of the lower triangle of `cov_re`.
The remaining elements are the standard errors of the variance
components.
Note that the sampling distribution of variance parameters is
strongly skewed unless the sample size is large, so these
standard errors may not give meaningful confidence intervals
or p-values if used in the usual way.
"""
p = self.model.exog.shape[1]
return np.sqrt(self.scale * np.diag(self.cov_params())[p:])
def _expand_re_names(self, group_ix):
names = list(self.model.data.exog_re_names)
for j, v in enumerate(self.model.exog_vc.names):
vg = self.model.exog_vc.colnames[j][group_ix]
na = ["%s[%s]" % (v, s) for s in vg]
names.extend(na)
return names
@cache_readonly
def random_effects(self):
"""
The conditional means of random effects given the data.
Returns
-------
random_effects : dict
A dictionary mapping the distinct `group` values to the
conditional means of the random effects for the group
given the data.
"""
try:
cov_re_inv = np.linalg.inv(self.cov_re)
except np.linalg.LinAlgError:
raise ValueError("Cannot predict random effects from " +
"singular covariance structure.")
vcomp = self.vcomp
k_re = self.k_re
ranef_dict = {}
for group_ix, group in enumerate(self.model.group_labels):
endog = self.model.endog_li[group_ix]
exog = self.model.exog_li[group_ix]
ex_r = self.model._aex_r[group_ix]
ex2_r = self.model._aex_r2[group_ix]
vc_var = self.model._expand_vcomp(vcomp, group_ix)
# Get the residuals relative to fixed effects
resid = endog
if self.k_fe > 0:
expval = np.dot(exog, self.fe_params)
resid = resid - expval
solver = _smw_solver(self.scale, ex_r, ex2_r, cov_re_inv,
1 / vc_var)
vir = solver(resid)
xtvir = _dot(ex_r.T, vir)
xtvir[0:k_re] = np.dot(self.cov_re, xtvir[0:k_re])
xtvir[k_re:] *= vc_var
ranef_dict[group] = pd.Series(
xtvir, index=self._expand_re_names(group_ix))
return ranef_dict
@cache_readonly
def random_effects_cov(self):
"""
Returns the conditional covariance matrix of the random
effects for each group given the data.
Returns
-------
random_effects_cov : dict
A dictionary mapping the distinct values of the `group`
variable to the conditional covariance matrix of the
random effects given the data.
"""
try:
cov_re_inv = np.linalg.inv(self.cov_re)
except np.linalg.LinAlgError:
cov_re_inv = None
vcomp = self.vcomp
ranef_dict = {}
for group_ix in range(self.model.n_groups):
ex_r = self.model._aex_r[group_ix]
ex2_r = self.model._aex_r2[group_ix]
label = self.model.group_labels[group_ix]
vc_var = self.model._expand_vcomp(vcomp, group_ix)
solver = _smw_solver(self.scale, ex_r, ex2_r, cov_re_inv,
1 / vc_var)
n = ex_r.shape[0]
m = self.cov_re.shape[0]
mat1 = np.empty((n, m + len(vc_var)))
mat1[:, 0:m] = np.dot(ex_r[:, 0:m], self.cov_re)
mat1[:, m:] = np.dot(ex_r[:, m:], np.diag(vc_var))
mat2 = solver(mat1)
mat2 = np.dot(mat1.T, mat2)
v = -mat2
v[0:m, 0:m] += self.cov_re
ix = np.arange(m, v.shape[0])
v[ix, ix] += vc_var
na = self._expand_re_names(group_ix)
v = pd.DataFrame(v, index=na, columns=na)
ranef_dict[label] = v
return ranef_dict
# Need to override since t-tests are only used for fixed effects
# parameters.
[docs] def t_test(self, r_matrix, scale=None, use_t=None):
"""
Compute a t-test for a each linear hypothesis of the form Rb = q
Parameters
----------
r_matrix : array_like
If an array is given, a p x k 2d array or length k 1d
array specifying the linear restrictions. It is assumed
that the linear combination is equal to zero.
scale : float, optional
An optional `scale` to use. Default is the scale specified
by the model fit.
use_t : bool, optional
If use_t is None, then the default of the model is used.
If use_t is True, then the p-values are based on the t
distribution.
If use_t is False, then the p-values are based on the normal
distribution.
Returns
-------
res : ContrastResults instance
The results for the test are attributes of this results instance.
The available results have the same elements as the parameter table
in `summary()`.
"""
if scale is not None:
import warnings
warnings.warn('scale is has no effect and is deprecated. It will'
'be removed in the next version.',
DeprecationWarning)
if r_matrix.shape[1] != self.k_fe:
raise ValueError("r_matrix for t-test should have %d columns"
% self.k_fe)
d = self.k_re2 + self.k_vc
z0 = np.zeros((r_matrix.shape[0], d))
r_matrix = np.concatenate((r_matrix, z0), axis=1)
tst_rslt = super(MixedLMResults, self).t_test(r_matrix, use_t=use_t)
return tst_rslt
[docs] def summary(self, yname=None, xname_fe=None, xname_re=None,
title=None, alpha=.05):
"""
Summarize the mixed model regression results.
Parameters
----------
yname : str, optional
Default is `y`
xname_fe : list[str], optional
Fixed effects covariate names
xname_re : list[str], optional
Random effects covariate names
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.summary2.Summary : class to hold summary results
"""
from statsmodels.iolib import summary2
smry = summary2.Summary()
info = OrderedDict()
info["Model:"] = "MixedLM"
if yname is None:
yname = self.model.endog_names
param_names = self.model.data.param_names[:]
k_fe_params = len(self.fe_params)
k_re_params = len(param_names) - len(self.fe_params)
if xname_fe is not None:
if len(xname_fe) != k_fe_params:
msg = "xname_fe should be a list of length %d" % k_fe_params
raise ValueError(msg)
param_names[:k_fe_params] = xname_fe
if xname_re is not None:
if len(xname_re) != k_re_params:
msg = "xname_re should be a list of length %d" % k_re_params
raise ValueError(msg)
param_names[k_fe_params:] = xname_re
info["No. Observations:"] = str(self.model.n_totobs)
info["No. Groups:"] = str(self.model.n_groups)
gs = np.array([len(x) for x in self.model.endog_li])
info["Min. group size:"] = "%.0f" % min(gs)
info["Max. group size:"] = "%.0f" % max(gs)
info["Mean group size:"] = "%.1f" % np.mean(gs)
info["Dependent Variable:"] = yname
info["Method:"] = self.method
info["Scale:"] = self.scale
info["Log-Likelihood:"] = self.llf
info["Converged:"] = "Yes" if self.converged else "No"
smry.add_dict(info)
smry.add_title("Mixed Linear Model Regression Results")
float_fmt = "%.3f"
sdf = np.nan * np.ones((self.k_fe + self.k_re2 + self.k_vc, 6))
# Coefficient estimates
sdf[0:self.k_fe, 0] = self.fe_params
# Standard errors
sdf[0:self.k_fe, 1] = np.sqrt(np.diag(self.cov_params()[0:self.k_fe]))
# Z-scores
sdf[0:self.k_fe, 2] = sdf[0:self.k_fe, 0] / sdf[0:self.k_fe, 1]
# p-values
sdf[0:self.k_fe, 3] = 2 * norm.cdf(-np.abs(sdf[0:self.k_fe, 2]))
# Confidence intervals
qm = -norm.ppf(alpha / 2)
sdf[0:self.k_fe, 4] = sdf[0:self.k_fe, 0] - qm * sdf[0:self.k_fe, 1]
sdf[0:self.k_fe, 5] = sdf[0:self.k_fe, 0] + qm * sdf[0:self.k_fe, 1]
# All random effects variances and covariances
jj = self.k_fe
for i in range(self.k_re):
for j in range(i + 1):
sdf[jj, 0] = self.cov_re[i, j]
sdf[jj, 1] = np.sqrt(self.scale) * self.bse[jj]
jj += 1
# Variance components
for i in range(self.k_vc):
sdf[jj, 0] = self.vcomp[i]
sdf[jj, 1] = np.sqrt(self.scale) * self.bse[jj]
jj += 1
sdf = pd.DataFrame(index=param_names, data=sdf)
sdf.columns = ['Coef.', 'Std.Err.', 'z', 'P>|z|',
'[' + str(alpha/2), str(1-alpha/2) + ']']
for col in sdf.columns:
sdf[col] = [float_fmt % x if np.isfinite(x) else ""
for x in sdf[col]]
smry.add_df(sdf, align='r')
return smry
@cache_readonly
def llf(self):
return self.model.loglike(self.params_object, profile_fe=False)
@cache_readonly
def aic(self):
"""Akaike information criterion"""
if self.reml:
return np.nan
if self.freepat is not None:
df = self.freepat.get_packed(use_sqrt=False, has_fe=True).sum() + 1
else:
df = self.params.size + 1
return -2 * (self.llf - df)
@cache_readonly
def bic(self):
"""Bayesian information criterion"""
if self.reml:
return np.nan
if self.freepat is not None:
df = self.freepat.get_packed(use_sqrt=False, has_fe=True).sum() + 1
else:
df = self.params.size + 1
return -2 * self.llf + np.log(self.nobs) * df
[docs] def profile_re(self, re_ix, vtype, num_low=5, dist_low=1., num_high=5,
dist_high=1.):
"""
Profile-likelihood inference for variance parameters.
Parameters
----------
re_ix : int
If vtype is `re`, this value is the index of the variance
parameter for which to construct a profile likelihood. If
`vtype` is 'vc' then `re_ix` is the name of the variance
parameter to be profiled.
vtype : str
Either 're' or 'vc', depending on whether the profile
analysis is for a random effect or a variance component.
num_low : int
The number of points at which to calculate the likelihood
below the MLE of the parameter of interest.
dist_low : float
The distance below the MLE of the parameter of interest to
begin calculating points on the profile likelihood.
num_high : int
The number of points at which to calculate the likelihood
above the MLE of the parameter of interest.
dist_high : float
The distance above the MLE of the parameter of interest to
begin calculating points on the profile likelihood.
Returns
-------
An array with two columns. The first column contains the
values to which the parameter of interest is constrained. The
second column contains the corresponding likelihood values.
Notes
-----
Only variance parameters can be profiled.
"""
pmodel = self.model
k_fe = pmodel.k_fe
k_re = pmodel.k_re
k_vc = pmodel.k_vc
endog, exog = pmodel.endog, pmodel.exog
# Need to permute the columns of the random effects design
# matrix so that the profiled variable is in the first column.
if vtype == 're':
ix = np.arange(k_re)
ix[0] = re_ix
ix[re_ix] = 0
exog_re = pmodel.exog_re.copy()[:, ix]
# Permute the covariance structure to match the permuted
# design matrix.
params = self.params_object.copy()
cov_re_unscaled = params.cov_re
cov_re_unscaled = cov_re_unscaled[np.ix_(ix, ix)]
params.cov_re = cov_re_unscaled
ru0 = cov_re_unscaled[0, 0]
# Convert dist_low and dist_high to the profile
# parameterization
cov_re = self.scale * cov_re_unscaled
low = (cov_re[0, 0] - dist_low) / self.scale
high = (cov_re[0, 0] + dist_high) / self.scale
elif vtype == 'vc':
re_ix = self.model.exog_vc.names.index(re_ix)
params = self.params_object.copy()
vcomp = self.vcomp
low = (vcomp[re_ix] - dist_low) / self.scale
high = (vcomp[re_ix] + dist_high) / self.scale
ru0 = vcomp[re_ix] / self.scale
# Define the sequence of values to which the parameter of
# interest will be constrained.
if low <= 0:
raise ValueError("dist_low is too large and would result in a "
"negative variance. Try a smaller value.")
left = np.linspace(low, ru0, num_low + 1)
right = np.linspace(ru0, high, num_high+1)[1:]
rvalues = np.concatenate((left, right))
# Indicators of which parameters are free and fixed.
free = MixedLMParams(k_fe, k_re, k_vc)
if self.freepat is None:
free.fe_params = np.ones(k_fe)
vcomp = np.ones(k_vc)
mat = np.ones((k_re, k_re))
else:
# If a freepat already has been specified, we add the
# constraint to it.
free.fe_params = self.freepat.fe_params
vcomp = self.freepat.vcomp
mat = self.freepat.cov_re
if vtype == 're':
mat = mat[np.ix_(ix, ix)]
if vtype == 're':
mat[0, 0] = 0
else:
vcomp[re_ix] = 0
free.cov_re = mat
free.vcomp = vcomp
klass = self.model.__class__
init_kwargs = pmodel._get_init_kwds()
if vtype == 're':
init_kwargs['exog_re'] = exog_re
likev = []
for x in rvalues:
model = klass(endog, exog, **init_kwargs)
if vtype == 're':
cov_re = params.cov_re.copy()
cov_re[0, 0] = x
params.cov_re = cov_re
else:
params.vcomp[re_ix] = x
# TODO should use fit_kwargs
rslt = model.fit(start_params=params, free=free,
reml=self.reml, cov_pen=self.cov_pen)._results
likev.append([x * rslt.scale, rslt.llf])
likev = np.asarray(likev)
return likev
class MixedLMResultsWrapper(base.LikelihoodResultsWrapper):
_attrs = {'bse_re': ('generic_columns', 'exog_re_names_full'),
'fe_params': ('generic_columns', 'xnames'),
'bse_fe': ('generic_columns', 'xnames'),
'cov_re': ('generic_columns_2d', 'exog_re_names'),
'cov_re_unscaled': ('generic_columns_2d', 'exog_re_names'),
}
_upstream_attrs = base.LikelihoodResultsWrapper._wrap_attrs
_wrap_attrs = base.wrap.union_dicts(_attrs, _upstream_attrs)
_methods = {}
_upstream_methods = base.LikelihoodResultsWrapper._wrap_methods
_wrap_methods = base.wrap.union_dicts(_methods, _upstream_methods)
def _handle_missing(data, groups, formula, re_formula, vc_formula):
tokens = set([])
forms = [formula]
if re_formula is not None:
forms.append(re_formula)
if vc_formula is not None:
forms.extend(vc_formula.values())
import tokenize
from io import StringIO
from statsmodels.compat.python import asunicode
skiptoks = {"(", ")", "*", ":", "+", "-", "**", "/"}
for fml in forms:
# Unicode conversion is for Py2 compatability
rl = StringIO(fml)
def rlu():
line = rl.readline()
return asunicode(line, 'ascii')
g = tokenize.generate_tokens(rlu)
for tok in g:
if tok not in skiptoks:
tokens.add(tok.string)
tokens = sorted(tokens & set(data.columns))
data = data[tokens]
ii = pd.notnull(data).all(1)
if type(groups) != "str":
ii &= pd.notnull(groups)
return data.loc[ii, :], groups[np.asarray(ii)]