Class to contain results of fitting a linear mixed effects model.
MixedLMResults inherits from statsmodels.LikelihoodModelResults
Parameters: | See statsmodels.LikelihoodModelResults : |
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Returns: | **Attributes** : model : class instance
normalized_cov_params : array
fe_params : array
re_params : array
bse_fe : array
bse_re : array
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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 |