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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

Methods

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 bool(x) -> bool

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