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_paramsndarray

See specific model class docstring

paramsndarray

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_paramsndarray

The fitted fixed-effects coefficients

cov_rendarray

The fitted random-effects covariance matrix

bse_fendarray

The standard errors of the fitted fixed effects coefficients

bse_rendarray

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, 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[, 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, invcov, use_f, ...])

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.