scikits.statsmodels.glm.GLMResults

class scikits.statsmodels.glm.GLMResults(model, params, normalized_cov_params, scale)

Class to contain GLM results.

GLMResults inherits from statsmodels.LikelihoodModelResults

Parameters:See statsmodels.LikelihoodModelReesults :

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.

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