Source code for statsmodels.genmod.families.links

'''
Defines the link functions to be used with GLM and GEE families.
'''

import numpy as np
import scipy.stats
FLOAT_EPS = np.finfo(float).eps





[docs]class Logit(Link): """ The logit transform Notes ----- call and derivative use a private method _clean to make trim p by machine epsilon so that p is in (0,1) Alias of Logit: logit = Logit() """ def _clean(self, p): """ Clip logistic values to range (eps, 1-eps) Parameters ----------- p : array-like Probabilities Returns -------- pclip : array Clipped probabilities """ return np.clip(p, FLOAT_EPS, 1. - FLOAT_EPS) def __call__(self, p): """ The logit transform Parameters ---------- p : array-like Probabilities Returns ------- z : array Logit transform of `p` Notes ----- g(p) = log(p / (1 - p)) """ p = self._clean(p) return np.log(p / (1. - p))
[docs] def inverse(self, z): """ Inverse of the logit transform Parameters ---------- z : array-like The value of the logit transform at `p` Returns ------- p : array Probabilities Notes ----- g^(-1)(z) = exp(z)/(1+exp(z)) """ z = np.asarray(z) t = np.exp(-z) return 1. / (1. + t)
[docs] def deriv(self, p): """ Derivative of the logit transform Parameters ---------- p: array-like Probabilities Returns ------- g'(p) : array Value of the derivative of logit transform at `p` Notes ----- g'(p) = 1 / (p * (1 - p)) Alias for `Logit`: logit = Logit() """ p = self._clean(p) return 1. / (p * (1 - p))
[docs] def inverse_deriv(self, z): """ Derivative of the inverse of the logit transform Parameters ---------- z : array-like `z` is usually the linear predictor for a GLM or GEE model. Returns ------- g'^(-1)(z) : array The value of the derivative of the inverse of the logit function """ t = np.exp(z) return t/(1 + t)**2
[docs] def deriv2(self, p): """ Second derivative of the logit function. Parameters ---------- p : array-like probabilities Returns ------- g''(z) : array The value of the second derivative of the logit function """ v = p * (1 - p) return (2*p - 1) / v**2
[docs]class logit(Logit): pass
[docs]class Power(Link): """ The power transform Parameters ---------- power : float The exponent of the power transform Notes ----- Aliases of Power: inverse = Power(power=-1) sqrt = Power(power=.5) inverse_squared = Power(power=-2.) identity = Power(power=1.) """ def __init__(self, power=1.): self.power = power def __call__(self, p): """ Power transform link function Parameters ---------- p : array-like Mean parameters Returns ------- z : array-like Power transform of x Notes ----- g(p) = x**self.power """ if self.power == 1: return p else: return np.power(p, self.power)
[docs] def inverse(self, z): """ Inverse of the power transform link function Parameters ---------- `z` : array-like Value of the transformed mean parameters at `p` Returns ------- `p` : array Mean parameters Notes ----- g^(-1)(z`) = `z`**(1/`power`) """ if self.power == 1: return z else: return np.power(z, 1. / self.power)
[docs] def deriv(self, p): """ Derivative of the power transform Parameters ---------- p : array-like Mean parameters Returns -------- g'(p) : array Derivative of power transform of `p` Notes ----- g'(`p`) = `power` * `p`**(`power` - 1) """ if self.power == 1: return np.ones_like(p) else: return self.power * np.power(p, self.power - 1)
[docs] def deriv2(self, p): """ Second derivative of the power transform Parameters ---------- p : array-like Mean parameters Returns -------- g''(p) : array Second derivative of the power transform of `p` Notes ----- g''(`p`) = `power` * (`power` - 1) * `p`**(`power` - 2) """ if self.power == 1: return np.zeros_like(p) else: return self.power * (self.power - 1) * np.power(p, self.power - 2)
[docs] def inverse_deriv(self, z): """ Derivative of the inverse of the power transform Parameters ---------- z : array-like `z` is usually the linear predictor for a GLM or GEE model. Returns ------- g^(-1)'(z) : array The value of the derivative of the inverse of the power transform function """ if self.power == 1: return np.ones_like(z) else: return np.power(z, (1 - self.power)/self.power) / self.power
[docs]class inverse_power(Power): """ The inverse transform Notes ----- g(p) = 1/p Alias of statsmodels.family.links.Power(power=-1.) """ def __init__(self): super(inverse_power, self).__init__(power=-1.)
class sqrt(Power): """ The square-root transform Notes ----- g(`p`) = sqrt(`p`) Alias of statsmodels.family.links.Power(power=.5) """ def __init__(self): super(sqrt, self).__init__(power=.5)
[docs]class inverse_squared(Power): """ The inverse squared transform Notes ----- g(`p`) = 1/(`p`\*\*2) Alias of statsmodels.family.links.Power(power=2.) """ def __init__(self): super(inverse_squared, self).__init__(power=-2.)
[docs]class identity(Power): """ The identity transform Notes ----- g(`p`) = `p` Alias of statsmodels.family.links.Power(power=1.) """ def __init__(self): super(identity, self).__init__(power=1.)
[docs]class Log(Link): """ The log transform Notes ----- call and derivative call a private method _clean to trim the data by machine epsilon so that p is in (0,1). log is an alias of Log. """ def _clean(self, x): return np.clip(x, FLOAT_EPS, np.inf) def __call__(self, p, **extra): """ Log transform link function Parameters ---------- x : array-like Mean parameters Returns ------- z : array log(x) Notes ----- g(p) = log(p) """ x = self._clean(p) return np.log(x)
[docs] def inverse(self, z): """ Inverse of log transform link function Parameters ---------- z : array The inverse of the link function at `p` Returns ------- p : array The mean probabilities given the value of the inverse `z` Notes ----- g^{-1}(z) = exp(z) """ return np.exp(z)
[docs] def deriv(self, p): """ Derivative of log transform link function Parameters ---------- p : array-like Mean parameters Returns ------- g'(p) : array derivative of log transform of x Notes ----- g'(x) = 1/x """ p = self._clean(p) return 1. / p
[docs] def deriv2(self, p): """ Second derivative of the log transform link function Parameters ---------- p : array-like Mean parameters Returns ------- g''(p) : array Second derivative of log transform of x Notes ----- g''(x) = -1/x^2 """ p = self._clean(p) return -1. / p**2
[docs] def inverse_deriv(self, z): """ Derivative of the inverse of the log transform link function Parameters ---------- z : array The inverse of the link function at `p` Returns ------- g^(-1)'(z) : array The value of the derivative of the inverse of the log function, the exponential function """ return np.exp(z)
[docs]class log(Log): """ The log transform Notes ----- log is a an alias of Log. """ pass
# TODO: the CDFLink is untested
[docs]class probit(CDFLink): """ The probit (standard normal CDF) transform Notes ----- g(p) = scipy.stats.norm.ppf(p) probit is an alias of CDFLink. """ pass
[docs]class cauchy(CDFLink): """ The Cauchy (standard Cauchy CDF) transform Notes ----- g(p) = scipy.stats.cauchy.ppf(p) cauchy is an alias of CDFLink with dbn=scipy.stats.cauchy """ def __init__(self): super(cauchy, self).__init__(dbn=scipy.stats.cauchy)
[docs] def deriv2(self, p): """ Second derivative of the Cauchy link function. Parameters ---------- p: array-like Probabilities Returns ------- g''(p) : array Value of the second derivative of Cauchy link function at `p` """ a = np.pi * (p - 0.5) d2 = 2 * np.pi**2 * np.sin(a) / np.cos(a)**3 return d2
[docs]class CLogLog(Logit): """ The complementary log-log transform CLogLog inherits from Logit in order to have access to its _clean method for the link and its derivative. Notes ----- CLogLog is untested. """ def __call__(self, p): """ C-Log-Log transform link function Parameters ---------- p : array Mean parameters Returns ------- z : array The CLogLog transform of `p` Notes ----- g(p) = log(-log(1-p)) """ p = self._clean(p) return np.log(-np.log(1 - p))
[docs] def inverse(self, z): """ Inverse of C-Log-Log transform link function Parameters ---------- z : array-like The value of the inverse of the CLogLog link function at `p` Returns ------- p : array Mean parameters Notes ----- g^(-1)(`z`) = 1-exp(-exp(`z`)) """ return 1 - np.exp(-np.exp(z))
[docs] def deriv(self, p): """ Derivative of C-Log-Log transform link function Parameters ---------- p : array-like Mean parameters Returns ------- g'(p) : array The derivative of the CLogLog transform link function Notes ----- g'(p) = - 1 / ((p-1)*log(1-p)) """ p = self._clean(p) return 1. / ((p - 1) * (np.log(1 - p)))
[docs] def deriv2(self, p): """ Second derivative of the C-Log-Log ink function Parameters ---------- p : array-like Mean parameters Returns ------- g''(p) : array The second derivative of the CLogLog link function """ p = self._clean(p) fl = np.log(1 - p) d2 = -1 / ((1 - p)**2 * fl) d2 *= 1 + 1 / fl return d2
[docs] def inverse_deriv(self, z): """ Derivative of the inverse of the C-Log-Log transform link function Parameters ---------- z : array-like The value of the inverse of the CLogLog link function at `p` Returns ------- g^(-1)'(z) : array The derivative of the inverse of the CLogLog link function """ return np.exp(z - np.exp(z))
[docs]class cloglog(CLogLog): """ The CLogLog transform link function. Notes ----- g(`p`) = log(-log(1-`p`)) cloglog is an alias for CLogLog cloglog = CLogLog() """ pass
[docs]class NegativeBinomial(Link): ''' The negative binomial link function Parameters ---------- alpha : float, optional Alpha is the ancillary parameter of the Negative Binomial link function. It is assumed to be nonstochastic. The default value is 1. Permissible values are usually assumed to be in (.01, 2). ''' def __init__(self, alpha=1.): self.alpha = alpha def _clean(self, x): return np.clip(x, FLOAT_EPS, np.inf) def __call__(self, p): ''' Negative Binomial transform link function Parameters ---------- p : array-like Mean parameters Returns ------- z : array The negative binomial transform of `p` Notes ----- g(p) = log(p/(p + 1/alpha)) ''' p = self._clean(p) return np.log(p/(p + 1/self.alpha))
[docs] def inverse(self, z): ''' Inverse of the negative binomial transform Parameters ----------- z : array-like The value of the inverse of the negative binomial link at `p`. Returns ------- p : array Mean parameters Notes ----- g^(-1)(z) = exp(z)/(alpha*(1-exp(z))) ''' return -1/(self.alpha * (1 - np.exp(-z)))
[docs] def deriv(self, p): ''' Derivative of the negative binomial transform Parameters ---------- p : array-like Mean parameters Returns ------- g'(p) : array The derivative of the negative binomial transform link function Notes ----- g'(x) = 1/(x+alpha*x^2) ''' return 1/(p + self.alpha * p**2)
[docs] def deriv2(self, p): ''' Second derivative of the negative binomial link function. Parameters ---------- p : array-like Mean parameters Returns ------- g''(p) : array The second derivative of the negative binomial transform link function Notes ----- g''(x) = -(1+2*alpha*x)/(x+alpha*x^2)^2 ''' numer = -(1 + 2 * self.alpha * p) denom = (p + self.alpha * p**2)**2 return numer / denom
[docs] def inverse_deriv(self, z): ''' Derivative of the inverse of the negative binomial transform Parameters ----------- z : array-like Usually the linear predictor for a GLM or GEE model Returns ------- g^(-1)'(z) : array The value of the derivative of the inverse of the negative binomial link ''' t = np.exp(z) return t / (self.alpha * (1-t)**2)
[docs]class nbinom(NegativeBinomial): """ The negative binomial link function. Notes ----- g(p) = log(p/(p + 1/alpha)) nbinom is an alias of NegativeBinomial. nbinom = NegativeBinomial(alpha=1.) """ pass