statsmodels.tsa.vector_ar.var_model.VARResults.stderr static VARResults.stderr() [source] Standard errors of coefficients, reshaped to match in size

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.sandbox.stats.multicomp.distance_st_range statsmodels.sandbox.stats.multicomp.distance_st_range(mean_all, nobs_all, var_all, df=None, triu=False) [source] pairwise distance matrix, outsourced from tukeyhsd CHANGED: meandiffs are with sign, studentized range uses abs q_crit added for testing TODO: error in variance calculation when nobs_all is scalar, missing 1/n

statsmodels.discrete.discrete_model.CountResults.load classmethod CountResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

statsmodels.tsa.vector_ar.var_model.VARProcess.plotsim VARProcess.plotsim(steps=1000) [source] Plot a simulation from the VAR(p) process for the desired number of steps

statsmodels.stats.weightstats.CompareMeans.zconfint_diff CompareMeans.zconfint_diff(alpha=0.05, alternative='two-sided', usevar='pooled') [source] confidence interval for the difference in means Parameters: alpha : float significance level for the confidence interval, coverage is 1-alpha alternative : string This specifies the alternative hypothesis for the test that corresponds to the confidence interval. The alternative hypothesis, H1, has to be one of the following : ?two-sided?: H1:

statsmodels.genmod.families.links.nbinom class statsmodels.genmod.families.links.nbinom(alpha=1.0) [source] The negative binomial link function. Notes g(p) = log(p/(p + 1/alpha)) nbinom is an alias of NegativeBinomial. nbinom = NegativeBinomial(alpha=1.) Methods deriv(p) Derivative of the negative binomial transform inverse(z) Inverse of the negative binomial transform inverse_deriv(z) Derivative of the inverse of the negative binomial transform