statsmodels.regression.mixed_linear_model.MixedLMResults

class statsmodels.regression.mixed_linear_model.MixedLMResults(model, params, cov_params)

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

aic()
bic()
bootstrap([nrep, method, disp, store])	simple bootstrap to get mean and variance of estimator
bse()
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
covjhj()	covariance of parameters based on HJJH
df_modelwc()
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.
get_nlfun(fun)
hessv()	cached Hessian of log-likelihood
initialize(model, params, **kwd)
jacv(*args, **kwds)	jacv is deprecated, use score_obsv instead!
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, vtype[, num_low, ...])	Profile-likelihood inference for variance parameters.
pvalues()
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
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

Methods

aic()
bic()
bootstrap([nrep, method, disp, store])	simple bootstrap to get mean and variance of estimator
bse()
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
covjhj()	covariance of parameters based on HJJH
df_modelwc()
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.
get_nlfun(fun)
hessv()	cached Hessian of log-likelihood
initialize(model, params, **kwd)
jacv(*args, **kwds)	jacv is deprecated, use score_obsv instead!
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, vtype[, num_low, ...])	Profile-likelihood inference for variance parameters.
pvalues()
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
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

Attributes

use_t