Source code for statsmodels.miscmodels.ordinal_model

# -*- coding: utf-8 -*-
Created on Sat Aug 22 20:24:42 2015

Author: Josef Perktold
License: BSD-3

import warnings

from statsmodels.compat.pandas import Appender

import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
from scipy import stats

from statsmodels.base.model import (
import statsmodels.base.wrapper as wrap
# for results wrapper:
import statsmodels.regression.linear_model as lm
from import cache_readonly

[docs] class OrderedModel(GenericLikelihoodModel): """Ordinal Model based on logistic or normal distribution The parameterization corresponds to the proportional odds model in the logistic case. The model assumes that the endogenous variable is ordered but that the labels have no numeric interpretation besides the ordering. The model is based on a latent linear variable, where we observe only a discretization. y_latent = X beta + u The observed variable is defined by the interval y = {0 if y_latent <= cut_0 1 of cut_0 < y_latent <= cut_1 ... K if cut_K < y_latent The probability of observing y=k conditional on the explanatory variables X is given by prob(y = k | x) = Prob(cut_k < y_latent <= cut_k+1) = Prob(cut_k - x beta < u <= cut_k+1 - x beta = F(cut_k+1 - x beta) - F(cut_k - x beta) Where F is the cumulative distribution of u which is either the normal or the logistic distribution, but can be set to any other continuous distribution. We use standardized distributions to avoid identifiability problems. Parameters ---------- endog : array_like Endogenous or dependent ordered categorical variable with k levels. Labels or values of endog will internally transformed to consecutive integers, 0, 1, 2, ... pd.Series with ordered Categorical as dtype should be preferred as it gives the order relation between the levels. If endog is not a pandas Categorical, then categories are sorted in lexicographic order (by numpy.unique). exog : array_like Exogenous, explanatory variables. This should not include an intercept. pd.DataFrame are also accepted. see Notes about constant when using formulas offset : array_like Offset is added to the linear prediction with coefficient equal to 1. distr : string 'probit' or 'logit', or a distribution instance The default is currently 'probit' which uses the normal distribution and corresponds to an ordered Probit model. The distribution is assumed to have the main methods of scipy.stats distributions, mainly cdf, pdf and ppf. The inverse cdf, ppf, is only use to calculate starting values. Notes ----- Status: experimental, core results are verified, still subclasses `GenericLikelihoodModel` which will change in future versions. The parameterization of OrderedModel requires that there is no constant in the model, neither explicit nor implicit. The constant is equivalent to shifting all thresholds and is therefore not separately identified. Patsy's formula specification does not allow a design matrix without explicit or implicit constant if there are categorical variables (or maybe splines) among explanatory variables. As workaround, statsmodels removes an explicit intercept. Consequently, there are two valid cases to get a design matrix without intercept when using formulas: - specify a model without explicit and implicit intercept which is possible if there are only numerical variables in the model. - specify a model with an explicit intercept which statsmodels will remove. Models with an implicit intercept will be overparameterized, the parameter estimates will not be fully identified, cov_params will not be invertible and standard errors might contain nans. The computed results will be dominated by numerical imprecision coming mainly from convergence tolerance and numerical derivatives. The model will raise a ValueError if a remaining constant is detected. """ _formula_max_endog = np.inf def __init__(self, endog, exog, offset=None, distr='probit', **kwds): if distr == 'probit': self.distr = stats.norm elif distr == 'logit': self.distr = stats.logistic else: self.distr = distr if offset is not None: offset = np.asarray(offset) self.offset = offset endog, labels, is_pandas = self._check_inputs(endog, exog) super(OrderedModel, self).__init__(endog, exog, **kwds) k_levels = None # initialize if not is_pandas: if self.endog.ndim == 1: unique, index = np.unique(self.endog, return_inverse=True) self.endog = index labels = unique if np.isnan(labels).any(): msg = ("NaN in dependent variable detected. " "Missing values need to be removed.") raise ValueError(msg) elif self.endog.ndim == 2: if not hasattr(self, "design_info"): raise ValueError("2-dim endog not supported") # this branch is currently only in support of from_formula # we need to initialize k_levels correctly for df_resid k_levels = self.endog.shape[1] labels = [] # Note: Doing the following here would break from_formula # self.endog = self.endog.argmax(1) if self.k_constant > 0: raise ValueError("There should not be a constant in the model") self._initialize_labels(labels, k_levels=k_levels) # adjust df self.k_extra = self.k_levels - 1 self.df_model = self.k_vars self.df_resid = self.nobs - (self.k_vars + self.k_extra) self.results_class = OrderedResults def _check_inputs(self, endog, exog): """Handle endog that is pandas Categorical. Checks if self.distrib is legal and provides Pandas ordered Categorical support for endog. Parameters ---------- endog : array_like Endogenous, dependent variable, 1-D. exog : array_like Exogenous, explanatory variables. Currently not used. Returns ------- endog : array_like or pandas Series If the original endog is a pandas ordered Categorical Series, then the returned endog are the ``codes``, i.e. integer representation of ordere categorical variable labels : None or list If original endog is pandas ordered Categorical Series, then the categories are returned. Otherwise ``labels`` is None. is_pandas : bool This is True if original endog is a pandas ordered Categorical Series and False otherwise. """ if not isinstance(self.distr, stats.rv_continuous): msg = ( f"{} is not a scipy.stats distribution." ) warnings.warn(msg) labels = None is_pandas = False if isinstance(endog, pd.Series): if isinstance(endog.dtypes, CategoricalDtype): if not endog.dtype.ordered: warnings.warn("the endog has ordered == False, " "risk of capturing a wrong order for the " "categories. ordered == True preferred.", Warning) endog_name = labels = endog.values.categories endog = if endog.min() == -1: # means there is a missing value raise ValueError("missing values in categorical endog are " "not supported") = endog_name is_pandas = True return endog, labels, is_pandas def _initialize_labels(self, labels, k_levels=None): self.labels = labels if k_levels is None: self.k_levels = len(labels) else: self.k_levels = k_levels if self.exog is not None: self.nobs, self.k_vars = self.exog.shape else: # no exog in model self.nobs, self.k_vars = self.endog.shape[0], 0 threshold_names = [str(x) + '/' + str(y) for x, y in zip(labels[:-1], labels[1:])] # from if self.exog is not None: # avoid extending several times if len(self.exog_names) > self.k_vars: raise RuntimeError("something wrong with exog_names, too long") self.exog_names.extend(threshold_names) else: = threshold_names
[docs] @classmethod def from_formula(cls, formula, data, subset=None, drop_cols=None, *args, **kwargs): # we want an explicit Intercept in the model that we can remove # Removing constant with "0 +" or "- 1" does not work for categ. exog endog_name = formula.split("~")[0].strip() original_endog = data[endog_name] model = super(OrderedModel, cls).from_formula( formula, data=data, drop_cols=["Intercept"], *args, **kwargs) if model.endog.ndim == 2: if not (isinstance(original_endog.dtype, CategoricalDtype) and original_endog.dtype.ordered): msg = ("Only ordered pandas Categorical are supported as " "endog in formulas") raise ValueError(msg) labels = original_endog.values.categories model._initialize_labels(labels) model.endog = model.endog.argmax(1) = endog_name return model
from_formula.__func__.__doc__ = Model.from_formula.__doc__
[docs] def cdf(self, x): """Cdf evaluated at x. Parameters ---------- x : array_like Points at which cdf is evaluated. In the model `x` is the latent variable plus threshold constants. Returns ------- Value of the cumulative distribution function of the underlying latent variable evaluated at x. """ return self.distr.cdf(x)
[docs] def pdf(self, x): """Pdf evaluated at x Parameters ---------- x : array_like Points at which cdf is evaluated. In the model `x` is the latent variable plus threshold constants. Returns ------- Value of the probability density function of the underlying latent variable evaluated at x. """ return self.distr.pdf(x)
[docs] def prob(self, low, upp): """Interval probability. Probability that value is in interval (low, upp], computed as prob = cdf(upp) - cdf(low) Parameters ---------- low : array_like lower bound for interval upp : array_like upper bound for interval Returns ------- float or ndarray Probability that value falls in interval (low, upp] """ return np.maximum(self.cdf(upp) - self.cdf(low), 0)
[docs] def transform_threshold_params(self, params): """transformation of the parameters in the optimization Parameters ---------- params : nd_array Contains (exog_coef, transformed_thresholds) where exog_coef are the coefficient for the explanatory variables in the linear term, transformed threshold or cutoff points. The first, lowest threshold is unchanged, all other thresholds are in terms of exponentiated increments. Returns ------- thresh : nd_array Thresh are the thresholds or cutoff constants for the intervals. """ th_params = params[-(self.k_levels - 1):] thresh = np.concatenate((th_params[:1], np.exp(th_params[1:]))).cumsum() thresh = np.concatenate(([-np.inf], thresh, [np.inf])) return thresh
[docs] def transform_reverse_threshold_params(self, params): """obtain transformed thresholds from original thresholds or cutoffs Parameters ---------- params : ndarray Threshold values, cutoff constants for choice intervals, which need to be monotonically increasing. Returns ------- thresh_params : ndarrray Transformed threshold parameter. The first, lowest threshold is unchanged, all other thresholds are in terms of exponentiated increments. Transformed parameters can be any real number without restrictions. """ thresh_params = np.concatenate((params[:1], np.log(np.diff(params[:-1])))) return thresh_params
[docs] def predict(self, params, exog=None, offset=None, which="prob"): """ Predicted probabilities for each level of the ordinal endog. Parameters ---------- params : ndarray Parameters for the Model, (exog_coef, transformed_thresholds). exog : array_like, optional Design / exogenous data. If exog is None, model exog is used. offset : array_like, optional Offset is added to the linear prediction with coefficient equal to 1. If offset is not provided and exog is None, uses the model's offset if present. If not, uses 0 as the default value. which : {"prob", "linpred", "cumprob"} Determines which statistic is predicted. - prob : predicted probabilities to be in each choice. 2-dim. - linear : 1-dim linear prediction of the latent variable ``x b + offset`` - cumprob : predicted cumulative probability to be in choice k or lower Returns ------- predicted values : ndarray If which is "prob", then 2-dim predicted probabilities with observations in rows and one column for each category or level of the categorical dependent variable. If which is "cumprob", then "prob" ar cumulatively added to get the cdf at k, i.e. probability of observing choice k or lower. If which is "linpred", then the conditional prediction of the latent variable is returned. In this case, the return is one-dimensional. """ # note, exog and offset handling is in linpred thresh = self.transform_threshold_params(params) xb = self._linpred(params, exog=exog, offset=offset) if which == "linpred": return xb xb = xb[:, None] low = thresh[:-1] - xb upp = thresh[1:] - xb if which == "prob": prob = self.prob(low, upp) return prob elif which in ["cum", "cumprob"]: cumprob = self.cdf(upp) return cumprob else: raise ValueError("`which` is not available")
def _linpred(self, params, exog=None, offset=None): """Linear prediction of latent variable `x b + offset`. Parameters ---------- params : ndarray Parameters for the model, (exog_coef, transformed_thresholds) exog : array_like, optional Design / exogenous data. Is exog is None, model exog is used. offset : array_like, optional Offset is added to the linear prediction with coefficient equal to 1. If offset is not provided and exog is None, uses the model's offset if present. If not, uses 0 as the default value. Returns ------- linear : ndarray 1-dim linear prediction given by exog times linear params plus offset. This is the prediction for the underlying latent variable. If exog and offset are None, then the predicted values are zero. """ if exog is None: exog = self.exog if offset is None: offset = self.offset else: if offset is None: offset = 0 if offset is not None: offset = np.asarray(offset) if exog is not None: _exog = np.asarray(exog) _params = np.asarray(params) linpred =[:-(self.k_levels - 1)]) else: # means self.exog is also None linpred = np.zeros(self.nobs) if offset is not None: linpred += offset return linpred def _bounds(self, params): """Integration bounds for the observation specific interval. This defines the lower and upper bounds for the intervals of the choices of all observations. The bounds for observation are given by a_{k_i-1} - linpred_i, a_k_i - linpred_i where - k_i is the choice in observation i. - a_{k_i-1} and a_k_i are thresholds (cutoffs) for choice k_i - linpred_i is the linear prediction for observation i Parameters ---------- params : ndarray Parameters for the model, (exog_coef, transformed_thresholds) Return ------ low : ndarray Lower bounds for choice intervals of each observation, 1-dim with length nobs upp : ndarray Upper bounds for choice intervals of each observation, 1-dim with length nobs. """ thresh = self.transform_threshold_params(params) thresh_i_low = thresh[self.endog] thresh_i_upp = thresh[self.endog + 1] xb = self._linpred(params) low = thresh_i_low - xb upp = thresh_i_upp - xb return low, upp
[docs] @Appender(GenericLikelihoodModel.loglike.__doc__) def loglike(self, params): return self.loglikeobs(params).sum()
[docs] def loglikeobs(self, params): """ Log-likelihood of OrderdModel for all observations. Parameters ---------- params : array_like The parameters of the model. Returns ------- loglike_obs : array_like The log likelihood for each observation of the model evaluated at ``params``. """ low, upp = self._bounds(params) prob = self.prob(low, upp) return np.log(prob + 1e-20)
[docs] def score_obs_(self, params): """score, first derivative of loglike for each observations This currently only implements the derivative with respect to the exog parameters, but not with respect to threshold parameters. """ low, upp = self._bounds(params) prob = self.prob(low, upp) pdf_upp = self.pdf(upp) pdf_low = self.pdf(low) # TODO the following doesn't work yet because of the incremental exp # parameterization. The following was written based on Greene for the # simple non-incremental parameterization. # k = self.k_levels - 1 # idx = self.endog # score_factor = np.zeros((self.nobs, k + 1 + 2)) #+2 avoids idx bounds # # rows = np.arange(self.nobs) # shift = 1 # score_factor[rows, shift + idx-1] = -pdf_low # score_factor[rows, shift + idx] = pdf_upp # score_factor[:, 0] = pdf_upp - pdf_low score_factor = (pdf_upp - pdf_low)[:, None] score_factor /= prob[:, None] so = np.column_stack((-score_factor[:, :1] * self.exog, score_factor[:, 1:])) return so
@property def start_params(self): """Start parameters for the optimization corresponding to null model. The threshold are computed from the observed frequencies and transformed to the exponential increments parameterization. The parameters for explanatory variables are set to zero. """ # start params based on model without exog freq = np.bincount(self.endog) / len(self.endog) start_ppf = self.distr.ppf(np.clip(freq.cumsum(), 0, 1)) start_threshold = self.transform_reverse_threshold_params(start_ppf) start_params = np.concatenate((np.zeros(self.k_vars), start_threshold)) return start_params
[docs] @Appender( def fit(self, start_params=None, method='nm', maxiter=500, full_output=1, disp=1, callback=None, retall=0, **kwargs): fit_method = super(OrderedModel, self).fit mlefit = fit_method(start_params=start_params, method=method, maxiter=maxiter, full_output=full_output, disp=disp, callback=callback, **kwargs) # use the proper result class ordmlefit = OrderedResults(self, mlefit) # TODO: temporary, needs better fix, modelwc adds 1 by default ordmlefit.hasconst = 0 result = OrderedResultsWrapper(ordmlefit) return result
[docs] class OrderedResults(GenericLikelihoodModelResults): """Results class for OrderedModel This class inherits from GenericLikelihoodModelResults and not all inherited methods might be appropriate in this case. """
[docs] def pred_table(self): """prediction table returns pandas DataFrame """ # todo: add category labels categories = np.arange(self.model.k_levels) observed = pd.Categorical(self.model.endog, categories=categories, ordered=True) predicted = pd.Categorical(self.predict().argmax(1), categories=categories, ordered=True) table = pd.crosstab(predicted, observed.astype(int), margins=True, dropna=False).T.fillna(0) return table
@cache_readonly def llnull(self): """ Value of the loglikelihood of model without explanatory variables """ params_null = self.model.start_params return self.model.loglike(params_null) # next 3 are copied from discrete @cache_readonly def prsquared(self): """ McFadden's pseudo-R-squared. `1 - (llf / llnull)` """ return 1 - self.llf/self.llnull @cache_readonly def llr(self): """ Likelihood ratio chi-squared statistic; `-2*(llnull - llf)` """ return -2*(self.llnull - self.llf) @cache_readonly def llr_pvalue(self): """ The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribution with degrees of freedom `df_model`. """ # number of restrictions is number of exog return stats.distributions.chi2.sf(self.llr, self.model.k_vars) @cache_readonly def resid_prob(self): """probability residual Probability-scale residual is ``P(Y < y) − P(Y > y)`` where `Y` is the observed choice and ``y`` is a random variable corresponding to the predicted distribution. References ---------- Shepherd BE, Li C, Liu Q (2016) Probability-scale residuals for continuous, discrete, and censored data. The Canadian Journal of Statistics. 44:463–476. Li C and Shepherd BE (2012) A new residual for ordinal outcomes. Biometrika. 99: 473–480 """ from statsmodels.stats.diagnostic_gen import prob_larger_ordinal_choice endog = self.model.endog fitted = self.predict() r = prob_larger_ordinal_choice(fitted)[1] resid_prob = r[np.arange(endog.shape[0]), endog] return resid_prob
class OrderedResultsWrapper(lm.RegressionResultsWrapper): pass wrap.populate_wrapper(OrderedResultsWrapper, OrderedResults)

Last update: Dec 14, 2023