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:

Attributes:

modelclass instance: Pointer to MixedLM model instance that called fit.
normalized_cov_paramsndarray: See specific model class docstring
paramsndarray: 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_paramsndarray: The fitted fixed-effects coefficients
cov_rendarray: The fitted random-effects covariance matrix
bse_fendarray: The standard errors of the fitted fixed effects coefficients
bse_rendarray: 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, rng])	Simple bootstrap to get mean and variance of estimator
`conf_int`([alpha])	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, invcov])	Compute the F-test for a joint linear hypothesis
`get_nlfun`(fun)	Get delta-method function for nonlinear tests, 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[, 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, invcov, use_f, ...])	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

See also

Methods

`bootstrap`([nrep, method, disp, store, rng])	Simple bootstrap to get mean and variance of estimator
`conf_int`([alpha])	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, invcov])	Compute the F-test for a joint linear hypothesis
`get_nlfun`(fun)	Get delta-method function for nonlinear tests, 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[, 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, invcov, use_f, ...])	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`	Return the standard errors of the fixed effect regression coefficients.
`bse_re`	Return 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`	Return 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`	Return the conditional covariance matrix of the random effects for each group given the data
`resid`	Return 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