Class to contain GLM results.
GLMResults inherits from statsmodels.LikelihoodModelResults
Parameters: | See statsmodels.LikelihoodModelReesults : |
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See also
statsmodels.LikelihoodModelResults
Attributes
llf | |
normalized_cov_params() |
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.family.family for the distribution-specific deviance functions. |
df_model | float | See GLM.df_model |
df_resid | float | See GLM.df_resid |
fittedvalues | array | Linear predicted values for the fitted model. dot(exog, params) |
model | class instance | Pointer to GLM model instance that called fit. |
mu | array | See GLM docstring. |
nobs | float | The number of observations n. |
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. |
pearsonX2 | array | Pearson’s Chi-Squared statistic is defined as the sum of the squares of the Pearson residuals. |
pinv_wexog | array | See GLM docstring. |
resid_anscombe | array | Anscombe residuals. See statsmodels.family.family for distribution- specific Anscombe residuals. |
resid_dev | array | Deviance residuals. See statsmodels.family.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.family.family and statsmodels.family.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 | array | 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 |
Methods
conf_int([alpha, cols]) | Returns the confidence interval of the fitted parameters. |
cov_params([r_matrix, column, scale, other]) | Returns the variance/covariance matrix. |
f_test(r_matrix[, scale, invcov]) | Compute an Fcontrast/F-test for a contrast matrix. |
t([column]) | Return the t-statistic for a given parameter estimate. |
t_test(r_matrix[, scale]) | Compute a tcontrast/t-test for a row vector array. |