Class to contain GLM results.
GLMResults inherits from statsmodels.LikelihoodModelResults
Parameters: | See statsmodels.LikelihoodModelReesults : |
---|---|
Returns: | **Attributes** : aic : float
bic : float
deviance : float
df_model : float
df_resid : float
fit_history : dict
fittedvalues : array
llf : float
model : class instance
mu : array
nobs : float
normalized_cov_params : array
null_deviance : float
params : array
pearson_chi2 : array
pinv_wexog : array
pvalues : array
resid_anscombe : array
resid_deviance : array
resid_pearson : array
resid_response : array
resid_working : array
scale : float
stand_errors : array
|
Methods
aic() | |
bic() | |
bse() | |
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. |
deviance() | |
f_test(r_matrix[, cov_p, scale, invcov]) | Compute the F-test for a joint linear hypothesis. |
fittedvalues() | |
get_prediction([exog, exposure, offset, ...]) | compute prediction results |
initialize(model, params, **kwd) | |
llf() | |
llnull() | |
load(fname) | load a pickle, (class method) |
mu() | |
normalized_cov_params() | |
null() | |
null_deviance() | |
pearson_chi2() | |
plot_added_variable(focus_exog[, ...]) | Create an added variable plot for a fitted regression model. |
plot_ceres_residuals(focus_exog[, frac, ...]) | Produces a CERES (Conditional Expectation Partial Residuals) plot for a fitted regression model. |
plot_partial_residuals(focus_exog[, ax]) | Create a partial residual, or ‘component plus residual’ plot for a fited regression model. |
predict([exog, transform]) | Call self.model.predict with self.params as the first argument. |
pvalues() | |
remove_data() | remove data arrays, all nobs arrays from result and model |
resid_anscombe() | |
resid_deviance() | |
resid_pearson() | |
resid_response() | |
resid_working() | |
save(fname[, remove_data]) | save a pickle of this instance |
summary([yname, xname, title, alpha]) | Summarize the Regression Results |
summary2([yname, xname, title, alpha, ...]) | Experimental summary for 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. |
wald_test_terms([skip_single, ...]) | Compute a sequence of Wald tests for terms over multiple columns |
Methods
aic() | |
bic() | |
bse() | |
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. |
deviance() | |
f_test(r_matrix[, cov_p, scale, invcov]) | Compute the F-test for a joint linear hypothesis. |
fittedvalues() | |
get_prediction([exog, exposure, offset, ...]) | compute prediction results |
initialize(model, params, **kwd) | |
llf() | |
llnull() | |
load(fname) | load a pickle, (class method) |
mu() | |
normalized_cov_params() | |
null() | |
null_deviance() | |
pearson_chi2() | |
plot_added_variable(focus_exog[, ...]) | Create an added variable plot for a fitted regression model. |
plot_ceres_residuals(focus_exog[, frac, ...]) | Produces a CERES (Conditional Expectation Partial Residuals) plot for a fitted regression model. |
plot_partial_residuals(focus_exog[, ax]) | Create a partial residual, or ‘component plus residual’ plot for a fited regression model. |
predict([exog, transform]) | Call self.model.predict with self.params as the first argument. |
pvalues() | |
remove_data() | remove data arrays, all nobs arrays from result and model |
resid_anscombe() | |
resid_deviance() | |
resid_pearson() | |
resid_response() | |
resid_working() | |
save(fname[, remove_data]) | save a pickle of this instance |
summary([yname, xname, title, alpha]) | Summarize the Regression Results |
summary2([yname, xname, title, alpha, ...]) | Experimental summary for 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. |
wald_test_terms([skip_single, ...]) | Compute a sequence of Wald tests for terms over multiple columns |
Attributes
use_t |