statsmodels.genmod.families.family.NegativeBinomial.fitted NegativeBinomial.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter class statsmodels.tsa.kalmanf.kalmanfilter.KalmanFilter [source] Kalman Filter code intended for use with the ARMA model. Notes The notation for the state-space form follows Durbin and Koopman (2001). The observation equations is The state equation is For the present purposed epsilon_{t} is assumed to always be zero. Methods R(params, r, k, q, p) The coefficient matrix for the state vector in the observation equation. T(params, r, k,

statsmodels.genmod.families.family.Binomial.fitted Binomial.fitted(lin_pred) Fitted values based on linear predictors lin_pred. Parameters: lin_pred : array Values of the linear predictor of the model. dot(X,beta) in a classical linear model. Returns: mu : array The mean response variables given by the inverse of the link function.

statsmodels.sandbox.regression.gmm.NonlinearIVGMM.from_formula classmethod NonlinearIVGMM.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.Data

statsmodels.nonparametric.kernel_regression.KernelCensoredReg.loo_likelihood KernelCensoredReg.loo_likelihood()

statsmodels.stats.weightstats.zconfint statsmodels.stats.weightstats.zconfint(x1, x2=None, value=0, alpha=0.05, alternative='two-sided', usevar='pooled', ddof=1.0) [source] confidence interval based on normal distribution z-test Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case value : float In the one sample case, value is the mean of x1 under the Null hypothesis. In the two sample case, value is the difference between mean of x1 and mean of x2

statsmodels.genmod.generalized_estimating_equations.GEE.from_formula classmethod GEE.from_formula(formula, groups, data, subset=None, time=None, offset=None, exposure=None, *args, **kwargs) [source]

statsmodels.genmod.families.family.Binomial class statsmodels.genmod.families.family.Binomial(link=) [source] Binomial exponential family distribution. Parameters: link : a link instance, optional The default link for the Binomial family is the logit link. Available links are logit, probit, cauchy, log, and cloglog. See statsmodels.family.links for more information. See also statsmodels.genmod.families.family.Family, Link Functions Notes endog for Binomial can be specified in one of th

statsmodels.genmod.cov_struct.Autoregressive.covariance_matrix_solve Autoregressive.covariance_matrix_solve(expval, index, stdev, rhs) [source] Solves matrix equations of the form covmat * soln = rhs and returns the values of soln, where covmat is the covariance matrix represented by this class. Parameters: expval: array-like : The expected value of endog for each observed value in the group. index: integer : The group index. stdev : array-like The standard deviation of endog for each

statsmodels.sandbox.distributions.transformed.ExpTransf_gen.expect ExpTransf_gen.expect(func=None, args=(), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds) Calculate expected value of a function with respect to the distribution. The expected value of a function f(x) with respect to a distribution dist is defined as: ubound E[x] = Integral(f(x) * dist.pdf(x)) lbound Parameters: func : callable, optional Function for which integral is calculated. Takes only one