statsmodels.discrete.discrete_model.Logit.cdf Logit.cdf(X) [source] The logistic cumulative distribution function Parameters: X : array-like X is the linear predictor of the logit model. See notes. Returns: 1/(1 + exp(-X)) : Notes In the logit model,

statsmodels.genmod.families.links.Log.inverse_deriv Log.inverse_deriv(z) [source] Derivative of the inverse of the log transform link function Parameters: z : array The inverse of the link function at p Returns: The value of the derivative of the inverse of the logit function :

statsmodels.genmod.families.links.Log.inverse Log.inverse(z) [source] 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)

statsmodels.genmod.families.links.Log.deriv2 Log.deriv2(p) Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.genmod.families.links.Log.deriv Log.deriv(p) [source] 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

statsmodels.genmod.families.links.Link.deriv2 Link.deriv2(p) [source] Second derivative of the link function g??(p) implemented through numerical differentiation

statsmodels.genmod.families.links.Link.inverse Link.inverse(z) [source] Inverse of the link function. Just a placeholder. Parameters: z : array-like z is usually the linear predictor of the transformed variable in the IRLS algorithm for GLM. Returns: The value of the inverse of the link function g^(-1)(z) = p :

statsmodels.genmod.families.links.Link.inverse_deriv Link.inverse_deriv(z) [source] Derivative of the inverse link function g^(-1)(z). Parameters: z : array-like z is usually the linear predictor for a GLM or GEE model. Returns: The value of the derivative of the inverse of the link function : Notes This reference implementation gives the correct result but it inefficient, so it can be overriden in subclasses.

statsmodels.genmod.families.links.Link.deriv Link.deriv(p) [source] Derivative of the link function g?(p). Just a placeholder. Parameters: p : array-like Returns: The value of the derivative of the link function g?(p) :

statsmodels.sandbox.regression.gmm.LinearIVGMM.score_cu LinearIVGMM.score_cu(params, epsilon=None, centered=True)