7.5.3.2. 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

Returns:

Attributes model : class instance Pointer to PHreg model instance that called fit. normalized_cov_params : array The sampling covariance matrix of the estimates fe_params : array The fitted fixed-effects coefficients re_params : array The fitted random-effects covariance matrix bse_fe : array The standard errors of the fitted fixed effects coefficients bse_re : array The standard errors of the fitted random effects covariance matrix

See also

statsmodels.LikelihoodModelResults

__init__(model, params, cov_params)[source]¶

Methods

__init__(model, params, cov_params)
bse()
bse_fe()	Returns the standard errors of the fixed effect regression coefficients.
bse_re()	Returns the standard errors of the variance parameters.
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.
f_test(r_matrix[, cov_p, scale, invcov])	Compute the F-test for a joint linear hypothesis.
initialize(model, params, **kwd)
llf()
load(fname)	load a pickle, (class method)
normalized_cov_params()
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
profile_re(re_ix[, num_low, dist_low, ...])	Calculate a series of values along a 1-dimensional profile likelihood.
pvalues()
random_effects()	Returns the conditional means of all 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
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[, cov_p, scale, use_t])	Compute a t-test for a each linear hypothesis of the form Rb = q
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.

Attributes

use_t