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
See also
statsmodels.LikelihoodModelResults
- Attributes
- modelclass instance
Pointer to MixedLM model instance that called fit.
normalized_cov_params
arraySee specific model class docstring
- paramsarray
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_paramsarray
The fitted fixed-effects coefficients
- cov_rearray
The fitted random-effects covariance matrix
bse_fe
arrayReturns the standard errors of the fixed effect regression coefficients.
bse_re
arrayReturns the standard errors of the variance parameters.
Methods
aic
()Akaike information criterion
bic
()Bayesian information criterion
bootstrap
([nrep, method, disp, store])simple bootstrap to get mean and variance of estimator
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
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
Model WC
f_test
(r_matrix[, cov_p, scale, invcov])Compute the F-test for a joint linear hypothesis.
Returns the fitted values for the model.
get_nlfun
(fun)This is not Implemented
hessv
()cached Hessian of log-likelihood
initialize
(model, params, **kwd)Initialize (possibly re-initialize) a Results instance.
llf
()Log-likelihood of model
load
(fname)load a pickle, (class method)
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.
pvalues
()The two-tailed p values for the t-stats of the params.
The conditional means of random effects given the data.
Returns the conditional covariance matrix of the random effects for each group given the 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
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