statsmodels.miscmodels.tmodel.TLinearModel.loglike TLinearModel.loglike(params) [source]

statsmodels.sandbox.regression.gmm.GMMResults.conf_int GMMResults.conf_int(alpha=0.05, cols=None, method='default') Returns the confidence interval of the fitted parameters. Parameters: alpha : float, optional The significance level for the confidence interval. ie., The default alpha = .05 returns a 95% confidence interval. cols : array-like, optional cols specifies which confidence intervals to return method : string Not Implemented Yet Method to estimate the confidence_interval. ?Def

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.stats.power.TTestIndPower.solve_power TTestIndPower.solve_power(effect_size=None, nobs1=None, alpha=None, power=None, ratio=1.0, alternative='two-sided') [source] 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.

statsmodels.tsa.vector_ar.irf.IRAnalysis.plot_cum_effects IRAnalysis.plot_cum_effects(orth=False, impulse=None, response=None, signif=0.05, plot_params=None, subplot_params=None, plot_stderr=True, stderr_type='asym', repl=1000, seed=None) Plot cumulative impulse response functions Parameters: orth : bool, default False Compute orthogonalized impulse responses impulse : string or int variable providing the impulse response : string or int variable affected by the impulse signif : float

statsmodels.tsa.vector_ar.var_model.VARResults.resid_acov VARResults.resid_acov(nlags=1) [source] Compute centered sample autocovariance (including lag 0) Parameters: nlags : int

statsmodels.discrete.discrete_model.BinaryResults.cov_params BinaryResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like

statsmodels.regression.linear_model.GLS.hessian GLS.hessian(params) The Hessian matrix of the model

statsmodels.tsa.vector_ar.var_model.VARResults.ma_rep VARResults.ma_rep(maxn=10) Compute MA() coefficient matrices Parameters: maxn : int Number of coefficient matrices to compute Returns: coefs : ndarray (maxn x k x k)

statsmodels.miscmodels.count.PoissonZiGMLE.nloglikeobs PoissonZiGMLE.nloglikeobs(params) [source] Loglikelihood of Poisson model Parameters: params : array-like The parameters of the model. Returns: The log likelihood of the model evaluated at `params` : Notes