Source code for statsmodels.genmod.cov_struct

"""
Covariance models and estimators for GEE.

Some details for the covariance calculations can be found in the Stata
docs:

http://www.stata.com/manuals13/xtxtgee.pdf
"""

from statsmodels.compat.python import iterkeys, itervalues, zip, range
from statsmodels.stats.correlation_tools import cov_nearest
import numpy as np
import pandas as pd
from scipy import linalg as spl
from collections import defaultdict
from statsmodels.tools.sm_exceptions import (ConvergenceWarning, OutputWarning,
                                             NotImplementedWarning)
import warnings


[docs]class CovStruct(object): """ Base class for correlation and covariance structures. An implementation of this class takes the residuals from a regression model that has been fit to grouped data, and uses them to estimate the within-group dependence structure of the random errors in the model. The current state of the covariance structure is represented through the value of the `dep_params` attribute. The default state of a newly-created instance should always be the identity correlation matrix. """ def __init__(self, cov_nearest_method="clipped"): # Parameters describing the dependency structure self.dep_params = None # Keep track of the number of times that the covariance was # adjusted. self.cov_adjust = [] # Method for projecting the covariance matrix if it is not # PSD. self.cov_nearest_method = cov_nearest_method
[docs] def initialize(self, model): """ Called by GEE, used by implementations that need additional setup prior to running `fit`. Parameters ---------- model : GEE class A reference to the parent GEE class instance. """ self.model = model
[docs] def update(self, params): """ Update the association parameter values based on the current regression coefficients. Parameters ---------- params : array-like Working values for the regression parameters. """ raise NotImplementedError
[docs] def covariance_matrix(self, endog_expval, index): """ Returns the working covariance or correlation matrix for a given cluster of data. Parameters ---------- endog_expval: array-like The expected values of endog for the cluster for which the covariance or correlation matrix will be returned index: integer The index of the cluster for which the covariane or correlation matrix will be returned Returns ------- M: matrix The covariance or correlation matrix of endog is_cor: bool True if M is a correlation matrix, False if M is a covariance matrix """ raise NotImplementedError
[docs] def covariance_matrix_solve(self, expval, index, stdev, rhs): """ Solves matrix equations of the form `covmat * soln = rhs` and returns the values of `soln`, where `covmat` is the covariance matrix represented by this class. Parameters ---------- expval: array-like The expected value of endog for each observed value in the group. index: integer The group index. stdev : array-like The standard deviation of endog for each observation in the group. rhs : list/tuple of array-like A set of right-hand sides; each defines a matrix equation to be solved. Returns ------- soln : list/tuple of array-like The solutions to the matrix equations. Notes ----- Returns None if the solver fails. Some dependence structures do not use `expval` and/or `index` to determine the correlation matrix. Some families (e.g. binomial) do not use the `stdev` parameter when forming the covariance matrix. If the covariance matrix is singular or not SPD, it is projected to the nearest such matrix. These projection events are recorded in the fit_history member of the GEE model. Systems of linear equations with the covariance matrix as the left hand side (LHS) are solved for different right hand sides (RHS); the LHS is only factorized once to save time. This is a default implementation, it can be reimplemented in subclasses to optimize the linear algebra according to the struture of the covariance matrix. """ vmat, is_cor = self.covariance_matrix(expval, index) if is_cor: vmat *= np.outer(stdev, stdev) # Factor the covariance matrix. If the factorization fails, # attempt to condition it into a factorizable matrix. threshold = 1e-2 success = False cov_adjust = 0 for itr in range(20): try: vco = spl.cho_factor(vmat) success = True break except np.linalg.LinAlgError: vmat = cov_nearest(vmat, method=self.cov_nearest_method, threshold=threshold) threshold *= 2 cov_adjust += 1 self.cov_adjust.append(cov_adjust) # Last resort if we still can't factor the covariance matrix. if not success: warnings.warn( "Unable to condition covariance matrix to an SPD " "matrix using cov_nearest", ConvergenceWarning) vmat = np.diag(np.diag(vmat)) vco = spl.cho_factor(vmat) soln = [spl.cho_solve(vco, x) for x in rhs] return soln
[docs] def summary(self): """ Returns a text summary of the current estimate of the dependence structure. """ raise NotImplementedError
[docs]class Independence(CovStruct): """ An independence working dependence structure. """ # Nothing to update
[docs] def update(self, params): return
[docs] def covariance_matrix(self, expval, index): dim = len(expval) return np.eye(dim, dtype=np.float64), True
[docs] def covariance_matrix_solve(self, expval, index, stdev, rhs): v = stdev ** 2 rslt = [] for x in rhs: if x.ndim == 1: rslt.append(x / v) else: rslt.append(x / v[:, None]) return rslt
update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
[docs] def summary(self): return ("Observations within a cluster are modeled " "as being independent.")
[docs]class Exchangeable(CovStruct): """ An exchangeable working dependence structure. """ def __init__(self): super(Exchangeable, self).__init__() # The correlation between any two values in the same cluster self.dep_params = 0.
[docs] def update(self, params): endog = self.model.endog_li nobs = self.model.nobs varfunc = self.model.family.variance cached_means = self.model.cached_means has_weights = self.model.weights is not None weights_li = self.model.weights residsq_sum, scale = 0, 0 fsum1, fsum2, n_pairs = 0., 0., 0. for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / stdev f = weights_li[i] if has_weights else 1. ssr = np.sum(resid * resid) scale += f * ssr fsum1 += f * len(endog[i]) residsq_sum += f * (resid.sum() ** 2 - ssr) / 2 ngrp = len(resid) npr = 0.5 * ngrp * (ngrp - 1) fsum2 += f * npr n_pairs += npr ddof = self.model.ddof_scale scale /= (fsum1 * (nobs - ddof) / float(nobs)) residsq_sum /= scale self.dep_params = residsq_sum / \ (fsum2 * (n_pairs - ddof) / float(n_pairs))
[docs] def covariance_matrix(self, expval, index): dim = len(expval) dp = self.dep_params * np.ones((dim, dim), dtype=np.float64) np.fill_diagonal(dp, 1) return dp, True
[docs] def covariance_matrix_solve(self, expval, index, stdev, rhs): k = len(expval) c = self.dep_params / (1. - self.dep_params) c /= 1. + self.dep_params * (k - 1) rslt = [] for x in rhs: if x.ndim == 1: x1 = x / stdev y = x1 / (1. - self.dep_params) y -= c * sum(x1) y /= stdev else: x1 = x / stdev[:, None] y = x1 / (1. - self.dep_params) y -= c * x1.sum(0) y /= stdev[:, None] rslt.append(y) return rslt
update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
[docs] def summary(self): return ("The correlation between two observations in the " + "same cluster is %.3f" % self.dep_params)
[docs]class Nested(CovStruct): """ A nested working dependence structure. A working dependence structure that captures a nested hierarchy of groups. Each level of grouping contributes to the random error structure of the model. When using this working covariance structure, `dep_data` of the GEE instance should contain a n_obs x k matrix of 0/1 indicators, corresponding to the k subgroups nested under the top-level `groups` of the GEE instance. These subgroups should be nested from left to right, so that two observations with the same value for column j of `dep_data` should also have the same value for all columns j' < j (this only applies to observations in the same top-level cluster given by the `groups` argument to GEE). Examples -------- Suppose our data are student test scores, and the students are in classrooms, nested in schools, nested in school districts. The school district is the highest level of grouping, so the school district id would be provided to GEE as `groups`, and the school and classroom id's would be provided to the Nested class as the `dep_data` argument, e.g. 0 0 # School 0, classroom 0, student 0 0 0 # School 0, classroom 0, student 1 0 1 # School 0, classroom 1, student 0 0 1 # School 0, classroom 1, student 1 1 0 # School 1, classroom 0, student 0 1 0 # School 1, classroom 0, student 1 1 1 # School 1, classroom 1, student 0 1 1 # School 1, classroom 1, student 1 Labels lower in the hierarchy are recycled, so that student 0 in classroom 0 is different fro student 0 in classroom 1, etc. Notes ----- The calculations for this dependence structure involve all pairs of observations within a group (that is, within the top level `group` structure passed to GEE). Large group sizes will result in slow iterations. The variance components are estimated using least squares regression of the products r*r', for standardized residuals r and r' in the same group, on a matrix of indicators defining which variance components are shared by r and r'. """
[docs] def initialize(self, model): """ Called on the first call to update `ilabels` is a list of n_i x n_i matrices containing integer labels that correspond to specific correlation parameters. Two elements of ilabels[i] with the same label share identical variance components. `designx` is a matrix, with each row containing dummy variables indicating which variance components are associated with the corresponding element of QY. """ super(Nested, self).initialize(model) if self.model.weights is not None: warnings.warn("weights not implemented for nested cov_struct, " "using unweighted covariance estimate", NotImplementedWarning) # A bit of processing of the nest data id_matrix = np.asarray(self.model.dep_data) if id_matrix.ndim == 1: id_matrix = id_matrix[:, None] self.id_matrix = id_matrix endog = self.model.endog_li designx, ilabels = [], [] # The number of layers of nesting n_nest = self.id_matrix.shape[1] for i in range(self.model.num_group): ngrp = len(endog[i]) glab = self.model.group_labels[i] rix = self.model.group_indices[glab] # Determine the number of common variance components # shared by each pair of observations. ix1, ix2 = np.tril_indices(ngrp, -1) ncm = (self.id_matrix[rix[ix1], :] == self.id_matrix[rix[ix2], :]).sum(1) # This is used to construct the working correlation # matrix. ilabel = np.zeros((ngrp, ngrp), dtype=np.int32) ilabel[(ix1, ix2)] = ncm + 1 ilabel[(ix2, ix1)] = ncm + 1 ilabels.append(ilabel) # This is used to estimate the variance components. dsx = np.zeros((len(ix1), n_nest + 1), dtype=np.float64) dsx[:, 0] = 1 for k in np.unique(ncm): ii = np.flatnonzero(ncm == k) dsx[ii, 1:k + 1] = 1 designx.append(dsx) self.designx = np.concatenate(designx, axis=0) self.ilabels = ilabels svd = np.linalg.svd(self.designx, 0) self.designx_u = svd[0] self.designx_s = svd[1] self.designx_v = svd[2].T
[docs] def update(self, params): endog = self.model.endog_li nobs = self.model.nobs dim = len(params) if self.designx is None: self._compute_design(self.model) cached_means = self.model.cached_means varfunc = self.model.family.variance dvmat = [] scale = 0. for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / stdev ix1, ix2 = np.tril_indices(len(resid), -1) dvmat.append(resid[ix1] * resid[ix2]) scale += np.sum(resid ** 2) dvmat = np.concatenate(dvmat) scale /= (nobs - dim) # Use least squares regression to estimate the variance # components vcomp_coeff = np.dot(self.designx_v, np.dot(self.designx_u.T, dvmat) / self.designx_s) self.vcomp_coeff = np.clip(vcomp_coeff, 0, np.inf) self.scale = scale self.dep_params = self.vcomp_coeff.copy()
[docs] def covariance_matrix(self, expval, index): dim = len(expval) # First iteration if self.dep_params is None: return np.eye(dim, dtype=np.float64), True ilabel = self.ilabels[index] c = np.r_[self.scale, np.cumsum(self.vcomp_coeff)] vmat = c[ilabel] vmat /= self.scale return vmat, True
update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
[docs] def summary(self): """ Returns a summary string describing the state of the dependence structure. """ dep_names = ["Groups"] if hasattr(self.model, "_dep_data_names"): dep_names.extend(self.model._dep_data_names) else: dep_names.extend(["Component %d:" % (k + 1) for k in range(len(self.vcomp_coeff) - 1)]) if hasattr(self.model, "_groups_name"): dep_names[0] = self.model._groups_name dep_names.append("Residual") vc = self.vcomp_coeff.tolist() vc.append(self.scale - np.sum(vc)) smry = pd.DataFrame({"Variance": vc}, index=dep_names) return smry
class Stationary(CovStruct): """ A stationary covariance structure. The correlation between two observations is an arbitrary function of the distance between them. Distances up to a given maximum value are included in the covariance model. Parameters ---------- max_lag : float The largest distance that is included in the covariance model. grid : bool If True, the index positions in the data (after dropping missing values) are used to define distances, and the `time` variable is ignored. """ def __init__(self, max_lag=1, grid=False): super(Stationary, self).__init__() self.max_lag = max_lag self.grid = grid self.dep_params = np.zeros(max_lag) def initialize(self, model): super(Stationary, self).initialize(model) # Time used as an index needs to be integer type. if not self.grid: time = self.model.time[:, 0].astype(np.int32) self.time = self.model.cluster_list(time) def update(self, params): if self.grid: self.update_grid(params) else: self.update_nogrid(params) def update_grid(self, params): endog = self.model.endog_li cached_means = self.model.cached_means varfunc = self.model.family.variance dep_params = np.zeros(self.max_lag + 1) for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / stdev dep_params[0] += np.sum(resid * resid) / len(resid) for j in range(1, self.max_lag + 1): dep_params[j] += np.sum(resid[0:-j] * resid[j:]) / len(resid[j:]) self.dep_params = dep_params[1:] / dep_params[0] def update_nogrid(self, params): endog = self.model.endog_li cached_means = self.model.cached_means varfunc = self.model.family.variance dep_params = np.zeros(self.max_lag + 1) dn = np.zeros(self.max_lag + 1) for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(varfunc(expval)) resid = (endog[i] - expval) / stdev j1, j2 = np.tril_indices(len(expval)) dx = np.abs(self.time[i][j1] - self.time[i][j2]) ii = np.flatnonzero(dx <= self.max_lag) j1 = j1[ii] j2 = j2[ii] dx = dx[ii] vs = np.bincount(dx, weights=resid[ j1] * resid[j2], minlength=self.max_lag + 1) vd = np.bincount(dx, minlength=self.max_lag + 1) ii = np.flatnonzero(vd > 0) if len(ii) > 0: dn[ii] += 1 dep_params[ii] += vs[ii] / vd[ii] i0 = np.flatnonzero(dn > 0) dep_params[i0] /= dn[i0] self.dep_params = dep_params[1:] / dep_params[0] def covariance_matrix(self, endog_expval, index): if self.grid: return self.covariance_matrix_grid(endog_expval, index) j1, j2 = np.tril_indices(len(endog_expval)) dx = np.abs(self.time[index][j1] - self.time[index][j2]) ii = np.flatnonzero((0 < dx) & (dx <= self.max_lag)) j1 = j1[ii] j2 = j2[ii] dx = dx[ii] cmat = np.eye(len(endog_expval)) cmat[j1, j2] = self.dep_params[dx - 1] cmat[j2, j1] = self.dep_params[dx - 1] return cmat, True def covariance_matrix_grid(self, endog_expval, index): from scipy.linalg import toeplitz r = np.zeros(len(endog_expval)) r[0] = 1 r[1:self.max_lag + 1] = self.dep_params return toeplitz(r), True def covariance_matrix_solve(self, expval, index, stdev, rhs): if not self.grid: return super(Stationary, self).covariance_matrix_solve( expval, index, stdev, rhs) from statsmodels.tools.linalg import stationary_solve r = np.zeros(len(expval)) r[0:self.max_lag] = self.dep_params return [stationary_solve(r, x) for x in rhs] update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__ def summary(self): return ("Stationary dependence parameters\n", self.dep_params)
[docs]class Autoregressive(CovStruct): """ A first-order autoregressive working dependence structure. The dependence is defined in terms of the `time` component of the parent GEE class, which defaults to the index position of each value within its cluster, based on the order of values in the input data set. Time represents a potentially multidimensional index from which distances between pairs of observations can be determined. The correlation between two observations in the same cluster is dep_params^distance, where `dep_params` contains the (scalar) autocorrelation parameter to be estimated, and `distance` is the distance between the two observations, calculated from their corresponding time values. `time` is stored as an n_obs x k matrix, where `k` represents the number of dimensions in the time index. The autocorrelation parameter is estimated using weighted nonlinear least squares, regressing each value within a cluster on each preceeding value in the same cluster. Parameters ---------- dist_func: function from R^k x R^k to R^+, optional A function that computes the distance between the two observations based on their `time` values. References ---------- B Rosner, A Munoz. Autoregressive modeling for the analysis of longitudinal data with unequally spaced examinations. Statistics in medicine. Vol 7, 59-71, 1988. """ def __init__(self, dist_func=None): super(Autoregressive, self).__init__() # The function for determining distances based on time if dist_func is None: self.dist_func = lambda x, y: np.abs(x - y).sum() else: self.dist_func = dist_func self.designx = None # The autocorrelation parameter self.dep_params = 0.
[docs] def update(self, params): if self.model.weights is not None: warnings.warn("weights not implemented for autoregressive " "cov_struct, using unweighted covariance estimate", NotImplementedWarning) endog = self.model.endog_li time = self.model.time_li # Only need to compute this once if self.designx is not None: designx = self.designx else: designx = [] for i in range(self.model.num_group): ngrp = len(endog[i]) if ngrp == 0: continue # Loop over pairs of observations within a cluster for j1 in range(ngrp): for j2 in range(j1): designx.append(self.dist_func(time[i][j1, :], time[i][j2, :])) designx = np.array(designx) self.designx = designx scale = self.model.estimate_scale() varfunc = self.model.family.variance cached_means = self.model.cached_means # Weights var = 1. - self.dep_params ** (2 * designx) var /= 1. - self.dep_params ** 2 wts = 1. / var wts /= wts.sum() residmat = [] for i in range(self.model.num_group): expval, _ = cached_means[i] stdev = np.sqrt(scale * varfunc(expval)) resid = (endog[i] - expval) / stdev ngrp = len(resid) for j1 in range(ngrp): for j2 in range(j1): residmat.append([resid[j1], resid[j2]]) residmat = np.array(residmat) # Need to minimize this def fitfunc(a): dif = residmat[:, 0] - (a ** designx) * residmat[:, 1] return np.dot(dif ** 2, wts) # Left bracket point b_lft, f_lft = 0., fitfunc(0.) # Center bracket point b_ctr, f_ctr = 0.5, fitfunc(0.5) while f_ctr > f_lft: b_ctr /= 2 f_ctr = fitfunc(b_ctr) if b_ctr < 1e-8: self.dep_params = 0 return # Right bracket point b_rgt, f_rgt = 0.75, fitfunc(0.75) while f_rgt < f_ctr: b_rgt = b_rgt + (1. - b_rgt) / 2 f_rgt = fitfunc(b_rgt) if b_rgt > 1. - 1e-6: raise ValueError( "Autoregressive: unable to find right bracket") from scipy.optimize import brent self.dep_params = brent(fitfunc, brack=[b_lft, b_ctr, b_rgt])
[docs] def covariance_matrix(self, endog_expval, index): ngrp = len(endog_expval) if self.dep_params == 0: return np.eye(ngrp, dtype=np.float64), True idx = np.arange(ngrp) cmat = self.dep_params ** np.abs(idx[:, None] - idx[None, :]) return cmat, True
[docs] def covariance_matrix_solve(self, expval, index, stdev, rhs): # The inverse of an AR(1) covariance matrix is tri-diagonal. k = len(expval) soln = [] # LHS has 1 column if k == 1: return [x / stdev ** 2 for x in rhs] # LHS has 2 columns if k == 2: mat = np.array([[1, -self.dep_params], [-self.dep_params, 1]]) mat /= (1. - self.dep_params ** 2) for x in rhs: if x.ndim == 1: x1 = x / stdev else: x1 = x / stdev[:, None] x1 = np.dot(mat, x1) if x.ndim == 1: x1 /= stdev else: x1 /= stdev[:, None] soln.append(x1) return soln # LHS has >= 3 columns: values c0, c1, c2 defined below give # the inverse. c0 is on the diagonal, except for the first # and last position. c1 is on the first and last position of # the diagonal. c2 is on the sub/super diagonal. c0 = (1. + self.dep_params ** 2) / (1. - self.dep_params ** 2) c1 = 1. / (1. - self.dep_params ** 2) c2 = -self.dep_params / (1. - self.dep_params ** 2) soln = [] for x in rhs: flatten = False if x.ndim == 1: x = x[:, None] flatten = True x1 = x / stdev[:, None] z0 = np.zeros((1, x.shape[1])) rhs1 = np.concatenate((x[1:, :], z0), axis=0) rhs2 = np.concatenate((z0, x[0:-1, :]), axis=0) y = c0 * x + c2 * rhs1 + c2 * rhs2 y[0, :] = c1 * x[0, :] + c2 * x[1, :] y[-1, :] = c1 * x[-1, :] + c2 * x[-2, :] y /= stdev[:, None] if flatten: y = np.squeeze(y) soln.append(y) return soln
update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__ covariance_matrix_solve.__doc__ = CovStruct.covariance_matrix_solve.__doc__
[docs] def summary(self): return ("Autoregressive(1) dependence parameter: %.3f\n" % self.dep_params)
class CategoricalCovStruct(CovStruct): """ Parent class for covariance structure for categorical data models. Attributes ---------- nlevel : int The number of distinct levels for the outcome variable. ibd : list A list whose i^th element ibd[i] is an array whose rows contain integer pairs (a,b), where endog_li[i][a:b] is the subvector of binary indicators derived from the same ordinal value. """ def initialize(self, model): super(CategoricalCovStruct, self).initialize(model) self.nlevel = len(model.endog_values) self._ncut = self.nlevel - 1 from numpy.lib.stride_tricks import as_strided b = np.dtype(np.int64).itemsize ibd = [] for v in model.endog_li: jj = np.arange(0, len(v) + 1, self._ncut, dtype=np.int64) jj = as_strided(jj, shape=(len(jj) - 1, 2), strides=(b, b)) ibd.append(jj) self.ibd = ibd
[docs]class GlobalOddsRatio(CategoricalCovStruct): """ Estimate the global odds ratio for a GEE with ordinal or nominal data. References ---------- PJ Heagerty and S Zeger. "Marginal Regression Models for Clustered Ordinal Measurements". Journal of the American Statistical Association Vol. 91, Issue 435 (1996). Thomas Lumley. Generalized Estimating Equations for Ordinal Data: A Note on Working Correlation Structures. Biometrics Vol. 52, No. 1 (Mar., 1996), pp. 354-361 http://www.jstor.org/stable/2533173 Notes ----- The following data structures are calculated in the class: 'ibd' is a list whose i^th element ibd[i] is a sequence of integer pairs (a,b), where endog_li[i][a:b] is the subvector of binary indicators derived from the same ordinal value. `cpp` is a dictionary where cpp[group] is a map from cut-point pairs (c,c') to the indices of all between-subject pairs derived from the given cut points. """ def __init__(self, endog_type): super(GlobalOddsRatio, self).__init__() self.endog_type = endog_type self.dep_params = 0.
[docs] def initialize(self, model): super(GlobalOddsRatio, self).initialize(model) if self.model.weights is not None: warnings.warn("weights not implemented for GlobalOddsRatio " "cov_struct, using unweighted covariance estimate", NotImplementedWarning) # Need to restrict to between-subject pairs cpp = [] for v in model.endog_li: # Number of subjects in this group m = int(len(v) / self._ncut) i1, i2 = np.tril_indices(m, -1) cpp1 = {} for k1 in range(self._ncut): for k2 in range(k1 + 1): jj = np.zeros((len(i1), 2), dtype=np.int64) jj[:, 0] = i1 * self._ncut + k1 jj[:, 1] = i2 * self._ncut + k2 cpp1[(k2, k1)] = jj cpp.append(cpp1) self.cpp = cpp # Initialize the dependence parameters self.crude_or = self.observed_crude_oddsratio() if self.model.update_dep: self.dep_params = self.crude_or
[docs] def pooled_odds_ratio(self, tables): """ Returns the pooled odds ratio for a list of 2x2 tables. The pooled odds ratio is the inverse variance weighted average of the sample odds ratios of the tables. """ if len(tables) == 0: return 1. # Get the sampled odds ratios and variances log_oddsratio, var = [], [] for table in tables: lor = np.log(table[1, 1]) + np.log(table[0, 0]) -\ np.log(table[0, 1]) - np.log(table[1, 0]) log_oddsratio.append(lor) var.append((1 / table.astype(np.float64)).sum()) # Calculate the inverse variance weighted average wts = [1 / v for v in var] wtsum = sum(wts) wts = [w / wtsum for w in wts] log_pooled_or = sum([w * e for w, e in zip(wts, log_oddsratio)]) return np.exp(log_pooled_or)
[docs] def covariance_matrix(self, expected_value, index): vmat = self.get_eyy(expected_value, index) vmat -= np.outer(expected_value, expected_value) return vmat, False
[docs] def observed_crude_oddsratio(self): """ To obtain the crude (global) odds ratio, first pool all binary indicators corresponding to a given pair of cut points (c,c'), then calculate the odds ratio for this 2x2 table. The crude odds ratio is the inverse variance weighted average of these odds ratios. Since the covariate effects are ignored, this OR will generally be greater than the stratified OR. """ cpp = self.cpp endog = self.model.endog_li # Storage for the contingency tables for each (c,c') tables = {} for ii in iterkeys(cpp[0]): tables[ii] = np.zeros((2, 2), dtype=np.float64) # Get the observed crude OR for i in range(len(endog)): # The observed joint values for the current cluster yvec = endog[i] endog_11 = np.outer(yvec, yvec) endog_10 = np.outer(yvec, 1. - yvec) endog_01 = np.outer(1. - yvec, yvec) endog_00 = np.outer(1. - yvec, 1. - yvec) cpp1 = cpp[i] for ky in iterkeys(cpp1): ix = cpp1[ky] tables[ky][1, 1] += endog_11[ix[:, 0], ix[:, 1]].sum() tables[ky][1, 0] += endog_10[ix[:, 0], ix[:, 1]].sum() tables[ky][0, 1] += endog_01[ix[:, 0], ix[:, 1]].sum() tables[ky][0, 0] += endog_00[ix[:, 0], ix[:, 1]].sum() return self.pooled_odds_ratio(list(itervalues(tables)))
[docs] def get_eyy(self, endog_expval, index): """ Returns a matrix V such that V[i,j] is the joint probability that endog[i] = 1 and endog[j] = 1, based on the marginal probabilities of endog and the global odds ratio `current_or`. """ current_or = self.dep_params ibd = self.ibd[index] # The between-observation joint probabilities if current_or == 1.0: vmat = np.outer(endog_expval, endog_expval) else: psum = endog_expval[:, None] + endog_expval[None, :] pprod = endog_expval[:, None] * endog_expval[None, :] pfac = np.sqrt((1. + psum * (current_or - 1.)) ** 2 + 4 * current_or * (1. - current_or) * pprod) vmat = 1. + psum * (current_or - 1.) - pfac vmat /= 2. * (current_or - 1) # Fix E[YY'] for elements that belong to same observation for bdl in ibd: evy = endog_expval[bdl[0]:bdl[1]] if self.endog_type == "ordinal": vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] =\ np.minimum.outer(evy, evy) else: vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] = np.diag(evy) return vmat
[docs] def update(self, params): """ Update the global odds ratio based on the current value of params. """ cpp = self.cpp cached_means = self.model.cached_means # This will happen if all the clusters have only # one observation if len(cpp[0]) == 0: return tables = {} for ii in cpp[0]: tables[ii] = np.zeros((2, 2), dtype=np.float64) for i in range(self.model.num_group): endog_expval, _ = cached_means[i] emat_11 = self.get_eyy(endog_expval, i) emat_10 = endog_expval[:, None] - emat_11 emat_01 = -emat_11 + endog_expval emat_00 = 1. - (emat_11 + emat_10 + emat_01) cpp1 = cpp[i] for ky in iterkeys(cpp1): ix = cpp1[ky] tables[ky][1, 1] += emat_11[ix[:, 0], ix[:, 1]].sum() tables[ky][1, 0] += emat_10[ix[:, 0], ix[:, 1]].sum() tables[ky][0, 1] += emat_01[ix[:, 0], ix[:, 1]].sum() tables[ky][0, 0] += emat_00[ix[:, 0], ix[:, 1]].sum() cor_expval = self.pooled_odds_ratio(list(itervalues(tables))) self.dep_params *= self.crude_or / cor_expval if not np.isfinite(self.dep_params): self.dep_params = 1. warnings.warn("dep_params became inf, resetting to 1", ConvergenceWarning)
update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__
[docs] def summary(self): return "Global odds ratio: %.3f\n" % self.dep_params
class OrdinalIndependence(CategoricalCovStruct): """ An independence covariance structure for ordinal models. The working covariance between indicators derived from different observations is zero. The working covariance between indicators derived form a common observation is determined from their current mean values. There are no parameters to estimate in this covariance structure. """ def covariance_matrix(self, expected_value, index): ibd = self.ibd[index] n = len(expected_value) vmat = np.zeros((n, n)) for bdl in ibd: ev = expected_value[bdl[0]:bdl[1]] vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] =\ np.minimum.outer(ev, ev) - np.outer(ev, ev) return vmat, False # Nothing to update def update(self, params): pass class NominalIndependence(CategoricalCovStruct): """ An independence covariance structure for nominal models. The working covariance between indicators derived from different observations is zero. The working covariance between indicators derived form a common observation is determined from their current mean values. There are no parameters to estimate in this covariance structure. """ def covariance_matrix(self, expected_value, index): ibd = self.ibd[index] n = len(expected_value) vmat = np.zeros((n, n)) for bdl in ibd: ev = expected_value[bdl[0]:bdl[1]] vmat[bdl[0]:bdl[1], bdl[0]:bdl[1]] =\ np.diag(ev) - np.outer(ev, ev) return vmat, False # Nothing to update def update(self, params): pass class Equivalence(CovStruct): """ A covariance structure defined in terms of equivalence classes. An 'equivalence class' is a set of pairs of observations such that the covariance of every pair within the equivalence class has a common value. Parameters ---------- pairs : dict-like A dictionary of dictionaries, where `pairs[group][label]` provides the indices of all pairs of observations in the group that have the same covariance value. Specifically, `pairs[group][label]` is a tuple `(j1, j2)`, where `j1` and `j2` are integer arrays of the same length. `j1[i], j2[i]` is one index pair that belongs to the `label` equivalence class. Only one triangle of each covariance matrix should be included. Positions where j1 and j2 have the same value are variance parameters. labels : array-like An array of labels such that every distinct pair of labels defines an equivalence class. Either `labels` or `pairs` must be provided. When the two labels in a pair are equal two equivalence classes are defined: one for the diagonal elements (corresponding to variances) and one for the off-diagonal elements (corresponding to covariances). return_cov : boolean If True, `covariance_matrix` returns an estimate of the covariance matrix, otherwise returns an estimate of the correlation matrix. Notes ----- Using `labels` to define the class is much easier than using `pairs`, but is less general. Any pair of values not contained in `pairs` will be assigned zero covariance. The index values in `pairs` are row indices into the `exog` matrix. They are not updated if missing data are present. When using this covariance structure, missing data should be removed before constructing the model. If using `labels`, after a model is defined using the covariance structure it is possible to remove a label pair from the second level of the `pairs` dictionary to force the corresponding covariance to be zero. Examples -------- The following sets up the `pairs` dictionary for a model with two groups, equal variance for all observations, and constant covariance for all pairs of observations within each group. >> pairs = {0: {}, 1: {}} >> pairs[0][0] = (np.r_[0, 1, 2], np.r_[0, 1, 2]) >> pairs[0][1] = np.tril_indices(3, -1) >> pairs[1][0] = (np.r_[3, 4, 5], np.r_[3, 4, 5]) >> pairs[1][2] = 3 + np.tril_indices(3, -1) """ def __init__(self, pairs=None, labels=None, return_cov=False): super(Equivalence, self).__init__() if (pairs is None) and (labels is None): raise ValueError( "Equivalence cov_struct requires either `pairs` or `labels`") if (pairs is not None) and (labels is not None): raise ValueError( "Equivalence cov_struct accepts only one of `pairs` " "and `labels`") if pairs is not None: import copy self.pairs = copy.deepcopy(pairs) if labels is not None: self.labels = np.asarray(labels) self.return_cov = return_cov def _make_pairs(self, i, j): """ Create arrays containing all unique ordered pairs of i, j. The arrays i and j must be one-dimensional containing non-negative integers. """ mat = np.zeros((len(i) * len(j), 2), dtype=np.int32) # Create the pairs and order them f = np.ones(len(j)) mat[:, 0] = np.kron(f, i).astype(np.int32) f = np.ones(len(i)) mat[:, 1] = np.kron(j, f).astype(np.int32) mat.sort(1) # Remove repeated rows try: dtype = np.dtype((np.void, mat.dtype.itemsize * mat.shape[1])) bmat = np.ascontiguousarray(mat).view(dtype) _, idx = np.unique(bmat, return_index=True) except TypeError: # workaround for old numpy that can't call unique with complex # dtypes rs = np.random.RandomState(4234) bmat = np.dot(mat, rs.uniform(size=mat.shape[1])) _, idx = np.unique(bmat, return_index=True) mat = mat[idx, :] return mat[:, 0], mat[:, 1] def _pairs_from_labels(self): from collections import defaultdict pairs = defaultdict(lambda: defaultdict(lambda: None)) model = self.model df = pd.DataFrame({"labels": self.labels, "groups": model.groups}) gb = df.groupby(["groups", "labels"]) ulabels = np.unique(self.labels) for g_ix, g_lb in enumerate(model.group_labels): # Loop over label pairs for lx1 in range(len(ulabels)): for lx2 in range(lx1 + 1): lb1 = ulabels[lx1] lb2 = ulabels[lx2] try: i1 = gb.groups[(g_lb, lb1)] i2 = gb.groups[(g_lb, lb2)] except KeyError: continue i1, i2 = self._make_pairs(i1, i2) clabel = str(lb1) + "/" + str(lb2) # Variance parameters belong in their own equiv class. jj = np.flatnonzero(i1 == i2) if len(jj) > 0: clabelv = clabel + "/v" pairs[g_lb][clabelv] = (i1[jj], i2[jj]) # Covariance parameters jj = np.flatnonzero(i1 != i2) if len(jj) > 0: i1 = i1[jj] i2 = i2[jj] pairs[g_lb][clabel] = (i1, i2) self.pairs = pairs def initialize(self, model): super(Equivalence, self).initialize(model) if self.model.weights is not None: warnings.warn("weights not implemented for equalence cov_struct, " "using unweighted covariance estimate", NotImplementedWarning) if not hasattr(self, 'pairs'): self._pairs_from_labels() # Initialize so that any equivalence class containing a # variance parameter has value 1. self.dep_params = defaultdict(lambda: 0.) self._var_classes = set([]) for gp in self.model.group_labels: for lb in self.pairs[gp]: j1, j2 = self.pairs[gp][lb] if np.any(j1 == j2): if not np.all(j1 == j2): warnings.warn( "equivalence class contains both variance " "and covariance parameters", OutputWarning) self._var_classes.add(lb) self.dep_params[lb] = 1 # Need to start indexing at 0 within each group. # rx maps olds indices to new indices rx = -1 * np.ones(len(self.model.endog), dtype=np.int32) for g_ix, g_lb in enumerate(self.model.group_labels): ii = self.model.group_indices[g_lb] rx[ii] = np.arange(len(ii), dtype=np.int32) # Reindex for gp in self.model.group_labels: for lb in self.pairs[gp].keys(): a, b = self.pairs[gp][lb] self.pairs[gp][lb] = (rx[a], rx[b]) def update(self, params): endog = self.model.endog_li varfunc = self.model.family.variance cached_means = self.model.cached_means dep_params = defaultdict(lambda: [0., 0., 0.]) n_pairs = defaultdict(lambda: 0) dim = len(params) for k, gp in enumerate(self.model.group_labels): expval, _ = cached_means[k] stdev = np.sqrt(varfunc(expval)) resid = (endog[k] - expval) / stdev for lb in self.pairs[gp].keys(): if (not self.return_cov) and lb in self._var_classes: continue jj = self.pairs[gp][lb] dep_params[lb][0] += np.sum(resid[jj[0]] * resid[jj[1]]) if not self.return_cov: dep_params[lb][1] += np.sum(resid[jj[0]] ** 2) dep_params[lb][2] += np.sum(resid[jj[1]] ** 2) n_pairs[lb] += len(jj[0]) if self.return_cov: for lb in dep_params.keys(): dep_params[lb] = dep_params[lb][0] / (n_pairs[lb] - dim) else: for lb in dep_params.keys(): den = np.sqrt(dep_params[lb][1] * dep_params[lb][2]) dep_params[lb] = dep_params[lb][0] / den for lb in self._var_classes: dep_params[lb] = 1. self.dep_params = dep_params self.n_pairs = n_pairs def covariance_matrix(self, expval, index): dim = len(expval) cmat = np.zeros((dim, dim)) g_lb = self.model.group_labels[index] for lb in self.pairs[g_lb].keys(): j1, j2 = self.pairs[g_lb][lb] cmat[j1, j2] = self.dep_params[lb] cmat = cmat + cmat.T np.fill_diagonal(cmat, cmat.diagonal() / 2) return cmat, not self.return_cov update.__doc__ = CovStruct.update.__doc__ covariance_matrix.__doc__ = CovStruct.covariance_matrix.__doc__