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:statsmodels.LikelihoodModelResults (See) –
  • **Attributes**
  • model (class instance) – Pointer to MixedLM model instance that called fit.
  • normalized_cov_params (array) – The sampling covariance matrix of the estimates
  • fe_params (array) – The fitted fixed-effects coefficients
  • cov_re (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 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.

See also



bootstrap([nrep, method, disp, store]) simple bootstrap to get mean and variance of estimator
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
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.
hessv() cached Hessian of log-likelihood
initialize(model, params, **kwd)
load(fname) load a pickle, (class method)
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.
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