statsmodels.genmod.generalized_linear_model.GLM.predict GLM.predict(params, exog=None, exposure=None, offset=None, linear=False) [source] Return predicted values for a design matrix Parameters: params : array-like Parameters / coefficients of a GLM. exog : array-like, optional Design / exogenous data. Is exog is None, model exog is used. exposure : array-like, optional Exposure time values, only can be used with the log link function. See notes for details. offset : array-like, option

statsmodels.stats.power.tt_ind_solve_power statsmodels.stats.power.tt_ind_solve_power = > solve for any one parameter of the power of a two sample t-test for t-test the keywords are: effect_size, nobs1, alpha, power, ratio exactly one needs to be None, all others need numeric values Parameters: effect_size : float standardized effect size, difference between the two means divided by the standard deviation. effect_size has to be positive. nobs1 : int or float number of observations of

statsmodels.sandbox.distributions.extras.SkewNorm2_gen.interval SkewNorm2_gen.interval(alpha, *args, **kwds) Confidence interval with equal areas around the median. Parameters: alpha : array_like of float Probability that an rv will be drawn from the returned range. Each value should be in the range [0, 1]. arg1, arg2, ... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). loc : array_like, optional location parameter,

statsmodels.regression.quantile_regression.QuantReg.information QuantReg.information(params) Fisher information matrix of model Returns -Hessian of loglike evaluated at params.

statsmodels.stats.power.tt_solve_power statsmodels.stats.power.tt_solve_power = > solve for any one parameter of the power of a one sample t-test for the one sample t-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. This test can also be used for a paired t-test, where effect size is defined in terms of the mean difference, and nobs is the number of pairs. Parameters: effect_size : float standardized effect size, mean d

statsmodels.genmod.families.links.Log class statsmodels.genmod.families.links.Log [source] 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. Methods deriv(p) Derivative of log transform link function deriv2(p) Second derivative of the link function g??(p) inverse(z) Inverse of log transform link function inverse_deriv(z) Derivative of the inverse of the log transform link 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.robust.norms.TukeyBiweight.rho TukeyBiweight.rho(z) [source] The robust criterion function for Tukey?s biweight estimator Parameters: z : array-like 1d array Returns: rho : array rho(z) = -(1 - (z/c)**2)**3 * c**2/6. for |z| <= R rho(z) = 0 for |z| > R

statsmodels.genmod.families.links.identity.inverse_deriv identity.inverse_deriv(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: The value of the derivative of the inverse of the power transform : function :

statsmodels.discrete.discrete_model.MNLogit.score_obs MNLogit.score_obs(params) [source] Jacobian matrix for multinomial logit model log-likelihood Parameters: params : array The parameters of the multinomial logit model. Returns: jac : ndarray, (nobs, k_vars*(J-1)) The derivative of the loglikelihood for each observation evaluated at params . Notes for , for observations In the multinomial model the score vector is K x (J-1) but is returned as a flattened array. The Jacobian has