statsmodels.discrete.discrete_model.MultinomialResults.bse static MultinomialResults.bse() [source]

statsmodels.discrete.discrete_model.MultinomialResults.bic static MultinomialResults.bic() [source]

statsmodels.discrete.discrete_model.MultinomialResults.aic static MultinomialResults.aic() [source]

statsmodels.regression.mixed_linear_model.MixedLMResults.tvalues static MixedLMResults.tvalues() Return the t-statistic for a given parameter estimate.

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.

statsmodels.regression.mixed_linear_model.MixedLMResults.random_effects static MixedLMResults.random_effects() [source] Returns the conditional means of all random effects given the data. Returns: random_effects : DataFrame A DataFrame with the distinct group values as the index and the conditional means of the random effects in the columns.

statsmodels.regression.mixed_linear_model.MixedLMResults.pvalues static MixedLMResults.pvalues()

statsmodels.regression.mixed_linear_model.MixedLMResults.llf static MixedLMResults.llf()

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.bse_fe static MixedLMResults.bse_fe() [source] Returns the standard errors of the fixed effect regression coefficients.