7.2.3.2.1. statsmodels.genmod.generalized_linear_model.GLMResults¶
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class
statsmodels.genmod.generalized_linear_model.GLMResults(model, params, normalized_cov_params, scale, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]¶ Class to contain GLM results.
GLMResults inherits from statsmodels.LikelihoodModelResults
Parameters: See statsmodels.LikelihoodModelReesults
Returns: Attributes
aic : float
Akaike Information Criterion -2 * llf + 2*(df_model + 1)
bic : float
Bayes Information Criterion deviance - df_resid * log(nobs)
deviance : float
See statsmodels.families.family for the distribution-specific deviance functions.
df_model : float
See GLM.df_model
df_resid : float
See GLM.df_resid
fit_history : dict
Contains information about the iterations. Its keys are iterations, deviance and params.
fittedvalues : array
Linear predicted values for the fitted model. dot(exog, params)
llf : float
Value of the loglikelihood function evalued at params. See statsmodels.families.family for distribution-specific loglikelihoods.
model : class instance
Pointer to GLM model instance that called fit.
mu : array
See GLM docstring.
nobs : float
The number of observations n.
normalized_cov_params : array
See GLM docstring
null_deviance : float
The value of the deviance function for the model fit with a constant as the only regressor.
params : array
The coefficients of the fitted model. Note that interpretation of the coefficients often depends on the distribution family and the data.
pearson_chi2 : array
Pearson’s Chi-Squared statistic is defined as the sum of the squares of the Pearson residuals.
pinv_wexog : array
See GLM docstring.
pvalues : array
The two-tailed p-values for the parameters.
resid_anscombe : array
Anscombe residuals. See statsmodels.families.family for distribution- specific Anscombe residuals.
resid_deviance : array
Deviance residuals. See statsmodels.families.family for distribution- specific deviance residuals.
resid_pearson : array
Pearson residuals. The Pearson residuals are defined as (endog - mu)/sqrt(VAR(mu)) where VAR is the distribution specific variance function. See statsmodels.families.family and statsmodels.families.varfuncs for more information.
resid_response : array
Respnose residuals. The response residuals are defined as endog - fittedvalues
resid_working : array
Working residuals. The working residuals are defined as resid_response/link’(mu). See statsmodels.family.links for the derivatives of the link functions. They are defined analytically.
scale : float
The estimate of the scale / dispersion for the model fit. See GLM.fit and GLM.estimate_scale for more information.
stand_errors : array
The standard errors of the fitted GLM. #TODO still named bse
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__init__(model, params, normalized_cov_params, scale, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]¶
Methods
__init__(model, params, ...[, cov_type, ...])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()initialize(model, params, **kwd)llf()llnull()load(fname)load a pickle, (class method) normalized_cov_params()null()null_deviance()pearson_chi2()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. Attributes
use_t-