statsmodels.stats.weightstats.DescrStatsW.mean static DescrStatsW.mean() [source] weighted mean of data

statsmodels.stats.weightstats.CompareMeans.ttest_ind CompareMeans.ttest_ind(alternative='two-sided', usevar='pooled', value=0) [source] ttest for the null hypothesis of identical means this should also be the same as onewaygls, except for ddof differences Parameters: x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following ?two-sided?: H1: difference in means not equal to value (def

statsmodels.stats.diagnostic.het_white statsmodels.stats.diagnostic.het_white(resid, exog, retres=False) White?s Lagrange Multiplier Test for Heteroscedasticity Parameters: resid : array_like residuals, square of it is used as endogenous variable exog : array_like possible explanatory variables for variance, squares and interaction terms are included in the auxilliary regression. resstore : instance (optional) a class instance that holds intermediate results. Only returned if store=Tru

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.regression.linear_model.OLSResults.el_test OLSResults.el_test(b0_vals, param_nums, return_weights=0, ret_params=0, method='nm', stochastic_exog=1, return_params=0) [source] Tests single or joint hypotheses of the regression parameters using Empirical Likelihood. Parameters: b0_vals : 1darray The hypothesized value of the parameter to be tested param_nums : 1darray The parameter number to be tested print_weights : bool If true, returns the weights that optimize the likelihoo

statsmodels.genmod.cov_struct.Nested.update Nested.update(params) [source] Updates the association parameter values based on the current regression coefficients. Parameters: params : array-like Working values for the regression parameters.

statsmodels.genmod.cov_struct.Nested.initialize Nested.initialize(model) [source] Called on the first call to update ilabels is a list of n_i x n_i matrices containing integer labels that correspond to specific correlation parameters. Two elements of ilabels[i] with the same label share identical variance components. designx is a matrix, with each row containing dummy variables indicating which variance components are associated with the corresponding element of QY.

statsmodels.stats.diagnostic.acorr_ljungbox statsmodels.stats.diagnostic.acorr_ljungbox(x, lags=None, boxpierce=False) Ljung-Box test for no autocorrelation Parameters: x : array_like, 1d data series, regression residuals when used as diagnostic test lags : None, int or array_like If lags is an integer then this is taken to be the largest lag that is included, the test result is reported for all smaller lag length. If lags is a list or array, then all lags are included up to the largest

statsmodels.genmod.families.family.NegativeBinomial.deviance NegativeBinomial.deviance(endog, mu, scale=1.0) [source] Returns the value of the deviance function. Parameters: endog : array-like Endogenous response variable mu : array-like Fitted mean response variable scale : float, optional An optional scale argument Returns: deviance : float Deviance function as defined below Notes deviance = sum(piecewise) where piecewise is defined as If : If :

statsmodels.regression.linear_model.GLSAR.predict GLSAR.predict(params, exog=None) Return linear predicted values from a design matrix. Parameters: params : array-like Parameters of a linear model exog : array-like, optional. Design / exogenous data. Model exog is used if None. Returns: An array of fitted values : Notes If the model has not yet been fit, params is not optional.