statsmodels.regression.mixed_linear_model.MixedLMResults

class statsmodels.regression.mixed_linear_model.MixedLMResults(model, params, cov_params)

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.