beta regression in statsmodels

betareg.py

# -*- coding: utf-8 -*-

u"""
Beta regression for modeling rates and proportions.

References
----------
Grün, Bettina, Ioannis Kosmidis, and Achim Zeileis. Extended beta regression
in R: Shaken, stirred, mixed, and partitioned. No. 2011-22. Working Papers in
Economics and Statistics, 2011.

Smithson, Michael, and Jay Verkuilen. "A better lemon squeezer?
Maximum-likelihood regression with beta-distributed dependent variables."
Psychological methods 11.1 (2006): 54.
"""
import numpy as np
import pandas as pd
import statsmodels.api as sm

from scipy.special import gammaln as lgamma

from statsmodels.base.model import GenericLikelihoodModel
from statsmodels.genmod.families import Binomial

# this is only need while #2024 is open.
class Logit(sm.families.links.Logit):

    """Logit tranform that won't overflow with large numbers."""

    def inverse(self, z):
        return 1 / (1. + np.exp(-z))

_init_example = """

    Beta regression with default of logit-link for exog and log-link
    for precision.

    >>> mod = Beta(endog, exog)
    >>> rslt = mod.fit()
    >>> print rslt.summary()

    We can also specify a formula and a specific structure and use the
    identity-link for phi.

    >>> from sm.families.links import identity
    >>> Z = patsy.dmatrix('~ temp', dat, return_type='dataframe')
    >>> mod = Beta.from_formula('iyield ~ C(batch, Treatment(10)) + temp',
    ...                         dat, Z=Z, link_phi=identity())

    In the case of proportion-data, we may think that the precision depends on
    the number of measurements. E.g for sequence data, on the number of
    sequence reads covering a site:

    >>> Z = patsy.dmatrix('~ coverage', df)
    >>> mod = Beta.from_formula('methylation ~ disease + age + gender + coverage', df, Z)
    >>> rslt = mod.fit()

"""

class Beta(GenericLikelihoodModel):

    """Beta Regression.

    This implementation uses `phi` as a precision parameter equal to
    `a + b` from the Beta parameters.
    """

    def __init__(self, endog, exog, Z=None, link=Logit(),
            link_phi=sm.families.links.Log(), **kwds):
        """
        Parameters
        ----------
        endog : array-like
            1d array of endogenous values (i.e. responses, outcomes,
            dependent variables, or 'Y' values).
        exog : array-like
            2d array of exogeneous values (i.e. covariates, predictors,
            independent variables, regressors, or 'X' values). A nobs x k
            array where `nobs` is the number of observations and `k` is
            the number of regressors. An intercept is not included by
            default and should be added by the user. See
            `statsmodels.tools.add_constant`.
        Z : array-like
            2d array of variables for the precision phi.
        link : link
            Any link in sm.families.links for `exog`
        link_phi : link
            Any link in sm.families.links for `Z`

        Examples
        --------
        {example}

        See Also
        --------
        :ref:`links`

        """.format(example=_init_example)
        assert np.all((0 < endog) & (endog < 1))
        if Z is None:
            extra_names = ['phi']
            Z = np.ones((len(endog), 1), dtype='f')
        else:
            extra_names = ['precision-%s' % zc for zc in \
                        (Z.columns if hasattr(Z, 'columns') else range(1, Z.shape[1] + 1))]
        kwds['extra_params_names'] = extra_names

        super(Beta, self).__init__(endog, exog, **kwds)
        self.link = link
        self.link_phi = link_phi
        
        self.Z = Z
        assert len(self.Z) == len(self.endog)

    def nloglikeobs(self, params):
        """
        Negative log-likelihood.

        Parameters
        ----------

        params : np.ndarray
            Parameter estimates
        """
        return -self._ll_br(self.endog, self.exog, self.Z, params)

    def fit(self, start_params=None, maxiter=100000, maxfun=5000, disp=False,
            method='bfgs', **kwds):
        """
        Fit the model.

        Parameters
        ----------
        start_params : array-like
            A vector of starting values for the regression
            coefficients.  If None, a default is chosen.
        maxiter : integer
            The maximum number of iterations
        disp : bool
            Show convergence stats.
        method : str
            The optimization method to use.
        """

        if start_params is None:
            start_params = sm.GLM(self.endog, self.exog, family=Binomial()
                                 ).fit(disp=False).params
            start_params = np.append(start_params, [0.5] * self.Z.shape[1])

        return super(Beta, self).fit(start_params=start_params,
                                        maxiter=maxiter, maxfun=maxfun,
                                        method=method, disp=disp, **kwds)

    def _ll_br(self, y, X, Z, params):
        nz = self.Z.shape[1]

        Xparams = params[:-nz]
        Zparams = params[-nz:]

        mu = self.link.inverse(np.dot(X, Xparams))
        phi = self.link_phi.inverse(np.dot(Z, Zparams))
        # TODO: derive a and b and constrain to > 0?

        if np.any(phi <= np.finfo(float).eps): return np.array(-np.inf)

        ll = lgamma(phi) - lgamma(mu * phi) - lgamma((1 - mu) * phi) \
                + (mu * phi - 1) * np.log(y) + (((1 - mu) * phi) - 1) \
                * np.log(1 - y)

        return ll

if __name__ == "__main__":

    import patsy
    dat = pd.read_table('gasoline.txt')
    Z = patsy.dmatrix('~ temp', dat, return_type='dataframe')
    # using other precison params with
    m = Beta.from_formula('iyield ~ C(batch, Treatment(10)) + temp', dat,
            Z=Z, link_phi=sm.families.links.identity())
    print m.fit().summary()

    fex = pd.read_csv('foodexpenditure.csv')
    m = Beta.from_formula(' I(food/income) ~ income + persons', fex)
    print m.fit().summary()
    #print GLM.from_formula('iyield ~ C(batch) + temp', dat, family=Binomial()).fit().summary()

    dev = pd.read_csv('methylation-test.csv')
    Z = patsy.dmatrix('~ age', dev, return_type='dataframe')
    m = Beta.from_formula('methylation ~ gender + CpG', dev,
            Z=Z,
            link_phi=sm.families.links.identity())
    print m.fit().summary()

ex.R

library(betareg)

data("GasolineYield", package = "betareg")
data("FoodExpenditure", package = "betareg")

#fe_beta = betareg(I(food/income) ~ income + persons, data = FoodExpenditure)
#print(summary(fe_beta)$coefficients)


#m = betareg(yield ~ batch + temp, data = GasolineYield)
#print(summary(m))



#gy2 <- betareg(yield ~ batch + temp | temp, data = GasolineYield, link.phi="identity")
#print(summary(gy2))


meth = read.csv('methylation-test.csv')
m =  betareg(methylation ~ gender + CpG | age, meth)
print(summary(m))

foodexpenditure.csv

gasoline.txt

iyield	gravity	pressure	temp10	temp	batch
0.122	50.8	8.6	190	205	1
0.223	50.8	8.6	190	275	1
0.347	50.8	8.6	190	345	1
0.457	50.8	8.6	190	407	1
0.08	40.8	3.5	210	218	2
0.131	40.8	3.5	210	273	2
0.266	40.8	3.5	210	347	2
0.074	40	6.1	217	212	3
0.182	40	6.1	217	272	3
0.304	40	6.1	217	340	3
0.069	38.4	6.1	220	235	4
0.152	38.4	6.1	220	300	4
0.26	38.4	6.1	220	365	4
0.336	38.4	6.1	220	410	4
0.144	40.3	4.8	231	307	5
0.268	40.3	4.8	231	367	5
0.349	40.3	4.8	231	395	5
0.1	32.2	5.2	236	267	6
0.248	32.2	5.2	236	360	6
0.317	32.2	5.2	236	402	6
0.028	41.3	1.8	267	235	7
0.064	41.3	1.8	267	275	7
0.161	41.3	1.8	267	358	7
0.278	41.3	1.8	267	416	7
0.05	38.1	1.2	274	285	8
0.176	38.1	1.2	274	365	8
0.321	38.1	1.2	274	444	8
0.14	32.2	2.4	284	351	9
0.232	32.2	2.4	284	424	9
0.085	31.8	0.2	316	365	10
0.147	31.8	0.2	316	379	10
0.18	31.8	0.2	316	428	10

methylation-test.csv

test_beta.py

# in R:
import io
import statsmodels.api as sm
import pandas as pd
import patsy
from betareg import Beta
import numpy as np

# betareg(I(food/income) ~ income + persons, data = FoodExpenditure)
_income_estimates_mean = u"""\
varname        Estimate  StdError   zvalue     Pr(>|z|)
(Intercept) -0.62254806 0.223853539 -2.781051 5.418326e-03
income      -0.01229884 0.003035585 -4.051556 5.087819e-05
persons      0.11846210 0.035340667  3.352005 8.022853e-04"""

_income_estimates_precision = u"""\
varname  Estimate StdError  zvalue     Pr(>|z|)
(phi) 35.60975   8.079598 4.407366 1.046351e-05
"""

_methylation_estimates_mean = u"""\
varname      Estimate StdError zvalue Pr(>|z|)    
(Intercept)  1.44224    0.03401  42.404   2e-16
genderM      0.06986    0.04359   1.603    0.109    
CpGCpG_1     0.60735    0.04834  12.563   2e-16
CpGCpG_2     0.97355    0.05311  18.331   2e-16"""

_methylation_estimates_precision = u"""\
varname Estimate StdError zvalue Pr(>|z|)    
(Intercept)  8.22829    1.79098   4.594 4.34e-06
age         -0.03471    0.03276  -1.059    0.289"""


expected_income_mean = pd.read_table(io.StringIO(_income_estimates_mean), sep="\s+")
expected_income_precision = pd.read_table(io.StringIO(_income_estimates_precision), sep="\s+")

expected_methylation_mean = pd.read_table(io.StringIO(_methylation_estimates_mean), sep="\s+")
expected_methylation_precision = pd.read_table(io.StringIO(_methylation_estimates_precision), sep="\s+")

income = pd.read_csv('foodexpenditure.csv')
methylation = pd.read_csv('methylation-test.csv')

def check_same(a, b, eps, name):
    assert np.allclose(a, b, rtol=0.01, atol=eps), \
            ("different from expected", name, list(a), list(b))


def test_income_coefficients():
    model = "I(food/income) ~ income + persons"

    mod = Beta.from_formula(model, income)
    rslt = mod.fit()
    yield check_same, rslt.params[:-1], expected_income_mean['Estimate'], 1e-3, "estimates"
    yield check_same, rslt.tvalues[:-1], expected_income_mean['zvalue'], 0.1, "z-scores"
    yield check_same, rslt.pvalues[:-1], expected_income_mean['Pr(>|z|)'], 1e-3, "p-values"


def test_income_precision():
    model = "I(food/income) ~ income + persons"

    mod = Beta.from_formula(model, income)
    rslt = mod.fit()
    # note that we have to exp the phi results for now.
    yield check_same, np.exp(rslt.params[-1:]), expected_income_precision['Estimate'], 1e-3, "estimate"
    #yield check_same, rslt.tvalues[-1:], expected_income_precision['zvalue'], 0.1, "z-score"
    yield check_same, rslt.pvalues[-1:], expected_income_precision['Pr(>|z|)'], 1e-3, "p-values"


def test_methylation_coefficients():
    model = "methylation ~ gender + CpG"
    Z = patsy.dmatrix("~ age", methylation)

    mod = Beta.from_formula(model, methylation, Z=Z, link_phi=sm.families.links.identity())
    rslt = mod.fit()
    yield check_same, rslt.params[:-2], expected_methylation_mean['Estimate'], 1e-2, "estimates"
    yield check_same, rslt.tvalues[:-2], expected_methylation_mean['zvalue'], 0.1, "z-scores"
    yield check_same, rslt.pvalues[:-2], expected_methylation_mean['Pr(>|z|)'], 1e-2, "p-values"

def test_methylation_precision():
    model = "methylation ~ gender + CpG"
    Z = patsy.dmatrix("~ age", methylation)

    mod = Beta.from_formula(model, methylation, Z=Z, link_phi=sm.families.links.identity())
    rslt = mod.fit()
    #yield check_same, sm.families.links.logit()(rslt.params[-2:]), expected_methylation_precision['Estimate'], 1e-3, "estimate"
    #yield check_same, rslt.tvalues[-2:], expected_methylation_precision['zvalue'], 0.1, "z-score"

This is a very useful code. It behooves best if you send a pull request to statsmodels module.

there's a pull request (not sure if it's open or closed now) on the statsmodels repo where someone else did much more work on this.

there's a pull request (not sure if it's open or closed now) on the statsmodels repo where someone else did much more work on this.

@brentp - thank you for your work on this!

I searched open and closed PRs on the statsmodels repo for "beta regression" and couldn't find the PR you mentioned. I only found #2030 and #4238.

Do you remember if code in that PR was substantially more robust than yours? If yes, would you by any chance remember what it was about, so that I can try other search queries?

thank you for your work.

if I had generated data follow beta distribution to crate beta regression mode how do it in R?
can I used the author estmation