Inheritance diagram for nipy.algorithms.statistics.models.family.links:
Bases: nipy.algorithms.statistics.models.family.links.Logit
The use the CDF of a scipy.stats distribution as a link function:
g(x) = dbn.ppf(x)
Methods
clean(p) | Clip logistic values to range (tol, 1-tol) |
deriv(p) | Derivative of CDF link |
initialize(Y) | |
inverse(z) | Derivative of CDF link |
Clip logistic values to range (tol, 1-tol)
Derivative of CDF link
g(p) = 1/self.dbn.pdf(self.dbn.ppf(p))
Derivative of CDF link
g(z) = self.dbn.cdf(z)
Bases: nipy.algorithms.statistics.models.family.links.Logit
The complementary log-log transform as a link function:
g(x) = log(-log(x))
Methods
clean(p) | Clip logistic values to range (tol, 1-tol) |
deriv(p) | Derivatve of C-Log-Log transform |
initialize(Y) | |
inverse(z) | Inverse of C-Log-Log transform |
x.__init__(...) initializes x; see help(type(x)) for signature
Clip logistic values to range (tol, 1-tol)
Derivatve of C-Log-Log transform
g(p) = - 1 / (log(p) * p)
Inverse of C-Log-Log transform
g(z) = exp(-exp(z))
Bases: object
A generic link function for one-parameter exponential family, with call, inverse and deriv methods.
Methods
deriv(p) | |
initialize(Y) | |
inverse(z) |
x.__init__(...) initializes x; see help(type(x)) for signature
Bases: nipy.algorithms.statistics.models.family.links.Link
The log transform as a link function:
g(x) = log(x)
Methods
clean(x) | |
deriv(x) | Derivative of log transform |
initialize(Y) | |
inverse(z) | Inverse of log transform |
x.__init__(...) initializes x; see help(type(x)) for signature
Derivative of log transform
g(x) = 1/x
Inverse of log transform
g(x) = exp(x)
Bases: nipy.algorithms.statistics.models.family.links.Link
The logit transform as a link function:
g’(x) = 1 / (x * (1 - x)) g^(-1)(x) = exp(x)/(1 + exp(x))
Methods
clean(p) | Clip logistic values to range (tol, 1-tol) |
deriv(p) | Derivative of logit transform |
initialize(Y) | |
inverse(z) | Inverse logit transform |
x.__init__(...) initializes x; see help(type(x)) for signature
Clip logistic values to range (tol, 1-tol)
Derivative of logit transform
g(p) = 1 / (p * (1 - p))
Inverse logit transform
h(z) = exp(z)/(1+exp(z))
Bases: nipy.algorithms.statistics.models.family.links.Link
The power transform as a link function:
g(x) = x**power
Methods
deriv(x) | Derivative of power transform |
initialize(Y) | |
inverse(z) | Inverse of power transform |
Derivative of power transform
g(x) = self.power * x**(self.power - 1)
Inverse of power transform
g(x) = x**(1/self.power)