statsmodels.nonparametric.kernel_regression.KernelReg.sig_test KernelReg.sig_test(var_pos, nboot=50, nested_res=25, pivot=False) [source] Significance test for the variables in the regression. Parameters: var_pos: sequence : The position of the variable in exog to be tested. Returns: sig: str : The level of significance: * : at 90% confidence level ** : at 95% confidence level *** : at 99* confidence level ?Not Significant? : if not significant

statsmodels.sandbox.distributions.transformed.LogTransf_gen.fit_loc_scale LogTransf_gen.fit_loc_scale(data, *args) Estimate loc and scale parameters from data using 1st and 2nd moments. Parameters: data : array_like Data to fit. arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information). Returns: Lhat : float Estimated location parameter for the data. Shat : float Estimated scale parameter for the data.

statsmodels.discrete.discrete_model.DiscreteResults.llr static DiscreteResults.llr() [source]

statsmodels.genmod.generalized_linear_model.GLM.score_factor GLM.score_factor(params, scale=None) [source] weights for score for each observation This can be considered as score residuals. Parameters: params : ndarray parameter at which Hessian is evaluated scale : None or float If scale is None, then the default scale will be calculated. Default scale is defined by self.scaletype and set in fit. If scale is not None, then it is used as a fixed scale. Returns: score_factor : ndarray_1

statsmodels.robust.robust_linear_model.RLMResults.llf static RLMResults.llf()

statsmodels.emplike.descriptive.DescStatUV.test_mean DescStatUV.test_mean(mu0, return_weights=False) [source] Returns - 2 x log-likelihood ratio, p-value and weights for a hypothesis test of the mean. Parameters: mu0 : float Mean value to be tested return_weights : bool If return_weights is True the funtion returns the weights of the observations under the null hypothesis. Default is False Returns: test_results : tuple The log-likelihood ratio and p-value of mu0

statsmodels.emplike.descriptive.DescStatMV.ci_corr DescStatMV.ci_corr(sig=0.05, upper_bound=None, lower_bound=None) [source] Returns the confidence intervals for the correlation coefficient Parameters: sig : float The significance level. Default is .05 upper_bound : float Maximum value the upper confidence limit can be. Default is 99% confidence limit assuming normality. lower_bound : float Minimum value the lower condidence limit can be. Default is 99% confidence limit assuming normal

statsmodels.genmod.generalized_linear_model.GLMResults.resid_deviance static GLMResults.resid_deviance() [source]

statsmodels.regression.mixed_linear_model.MixedLMResults.bse_re static MixedLMResults.bse_re() [source] Returns the standard errors of the variance parameters. Note that the sampling distribution of variance parameters is strongly skewed unless the sample size is large, so these standard errors may not give meaningful confidence intervals of p-values if used in the usual way.

statsmodels.regression.mixed_linear_model.MixedLMResults.random_effects_cov static MixedLMResults.random_effects_cov() [source] Returns the conditional covariance matrix of the random effects for each group given the data. Returns: random_effects_cov : dict A dictionary mapping the distinct values of the group variable to the conditional covariance matrix of the random effects given the data.