statsmodels.regression.mixed_linear_model.MixedLMResults¶
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class
statsmodels.regression.mixed_linear_model.
MixedLMResults
(model, params, cov_params)[source]¶ Class to contain results of fitting a linear mixed effects model.
MixedLMResults inherits from statsmodels.LikelihoodModelResults
Parameters: See statsmodels.LikelihoodModelResults See also
statsmodels.LikelihoodModelResults
Attributes
normalized_cov_params
()See specific model class docstring bse_fe
()Returns the standard errors of the fixed effect regression coefficients. bse_re
()Returns the standard errors of the variance parameters. model (class instance) Pointer to MixedLM model instance that called fit. params (array) A packed parameter vector for the profile parameterization. The first k_fe elements are the estimated fixed effects coefficients. The remaining elements are the estimated variance parameters. The variance parameters are all divided by scale and are not the variance parameters shown in the summary. fe_params (array) The fitted fixed-effects coefficients cov_re (array) The fitted random-effects covariance matrix Methods
aic
()Akaike information criterion bic
()Bayesian information criterion bootstrap
([nrep, method, disp, store])simple bootstrap to get mean and variance of estimator bse
()The standard errors of the parameter estimates. bse_fe
()Returns the standard errors of the fixed effect regression coefficients. bse_re
()Returns the standard errors of the variance parameters. bsejac
()standard deviation of parameter estimates based on covjac bsejhj
()standard deviation of parameter estimates based on covHJH conf_int
([alpha, cols, method])Returns the confidence interval of the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Returns the variance/covariance matrix. covjac
()covariance of parameters based on outer product of jacobian of log-likelihood covjhj
()covariance of parameters based on HJJH df_modelwc
()Model WC f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. fittedvalues
()Returns the fitted values for the model. get_nlfun
(fun)This is not Implemented hessv
()cached Hessian of log-likelihood initialize
(model, params, **kwd)Initialize (possibly re-initialize) a Results instance. llf
()Log-likelihood of model load
(fname)load a pickle, (class method); use only on trusted files, as unpickling can run arbitrary code. normalized_cov_params
()See specific model class docstring predict
([exog, transform])Call self.model.predict with self.params as the first argument. profile_re
(re_ix, vtype[, num_low, …])Profile-likelihood inference for variance parameters. pvalues
()The two-tailed p values for the t-stats of the params. random_effects
()The conditional means of random effects given the data. random_effects_cov
()Returns the conditional covariance matrix of the random effects for each group given the data. remove_data
()remove data arrays, all nobs arrays from result and model resid
()Returns the residuals for the model. save
(fname[, remove_data])save a pickle of this instance score_obsv
()cached Jacobian of log-likelihood summary
([yname, xname_fe, xname_re, title, …])Summarize the mixed model regression results. t_test
(r_matrix[, scale, use_t])Compute a t-test for a each linear hypothesis of the form Rb = q t_test_pairwise
(term_name[, method, alpha, …])perform pairwise t_test with multiple testing corrected p-values tvalues
()Return the t-statistic for a given parameter estimate. wald_test
(r_matrix[, cov_p, scale, invcov, …])Compute a Wald-test for a joint linear hypothesis. wald_test_terms
([skip_single, …])Compute a sequence of Wald tests for terms over multiple columns