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
  • params (array) – 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 (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