statsmodels.regression.mixed_linear_model.MixedLMResults¶
-
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 model (class instance) Pointer to MixedLM model instance that called fit. params (ndarray) 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 (ndarray) The fitted fixed-effects coefficients cov_re (ndarray) The fitted random-effects covariance matrix bse_fe (ndarray) The standard errors of the fitted fixed effects coefficients bse_re (ndarray) The standard errors of the fitted random effects covariance matrix and variance components. The first k_re * (k_re + 1) parameters are the standard errors for the lower triangle of cov_re, the remaining elements are the standard errors for the variance components. Methods
bootstrap
([nrep, method, disp, store])simple bootstrap to get mean and variance of estimator conf_int
([alpha, cols])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_nlfun
(fun)This is not Implemented initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance 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. remove_data
()Remove data arrays, all nobs arrays from result and model. save
(fname[, remove_data])Save a pickle of this instance. 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. 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. Methods
bootstrap
([nrep, method, disp, store])simple bootstrap to get mean and variance of estimator conf_int
([alpha, cols])Construct confidence interval for the fitted parameters. cov_params
([r_matrix, column, scale, cov_p, …])Compute the variance/covariance matrix. f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis. get_nlfun
(fun)This is not Implemented initialize
(model, params, **kwargs)Initialize (possibly re-initialize) a Results instance. load
(fname)Load a pickled results instance 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. remove_data
()Remove data arrays, all nobs arrays from result and model. save
(fname[, remove_data])Save a pickle of this instance. 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. 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. Properties
aic
Akaike information criterion bic
Bayesian information criterion 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 covjac
covariance of parameters based on outer product of jacobian of log-likelihood covjhj
covariance of parameters based on HJJH df_modelwc
Model WC fittedvalues
Returns the fitted values for the model. hessv
cached Hessian of log-likelihood llf
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. resid
Returns the residuals for the model. score_obsv
cached Jacobian of log-likelihood tvalues
Return the t-statistic for a given parameter estimate. use_t
Flag indicating to use the Student’s distribution in inference.