3.2.2.3.26. statsmodels.discrete.discrete_model.NegativeBinomialResults¶
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class
statsmodels.discrete.discrete_model.
NegativeBinomialResults
(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)[source]¶ A results class for NegativeBinomial 1 and 2
Parameters: model : A DiscreteModel instance
params : array-like
The parameters of a fitted model.
hessian : array-like
The hessian of the fitted model.
scale : float
A scale parameter for the covariance matrix.
Returns: Attributes
aic : float
Akaike information criterion. -2*(llf - p) where p is the number of regressors including the intercept.
bic : float
Bayesian information criterion. -2*llf + ln(nobs)*p where p is the number of regressors including the intercept.
bse : array
The standard errors of the coefficients.
df_resid : float
See model definition.
df_model : float
See model definition.
fitted_values : array
Linear predictor XB.
llf : float
Value of the loglikelihood
llnull : float
Value of the constant-only loglikelihood
llr : float
Likelihood ratio chi-squared statistic; -2*(llnull - llf)
llr_pvalue : float
The chi-squared probability of getting a log-likelihood ratio statistic greater than llr. llr has a chi-squared distribution with degrees of freedom df_model.
prsquared : float
McFadden’s pseudo-R-squared. 1 - (llf / llnull)
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__init__
(model, mlefit, cov_type='nonrobust', cov_kwds=None, use_t=None)¶
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3.2.2.3.26.1. Methods¶
__init__ (model, mlefit[, cov_type, ...]) |
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aic () |
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bic () |
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bse () |
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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. |
f_test (r_matrix[, cov_p, scale, invcov]) |
Compute the F-test for a joint linear hypothesis. |
fittedvalues () |
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get_margeff ([at, method, atexog, dummy, count]) |
Get marginal effects of the fitted model. |
initialize (model, params, **kwd) |
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llf () |
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llnull () |
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llr () |
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llr_pvalue () |
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lnalpha () |
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lnalpha_std_err () |
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load (fname) |
load a pickle, (class method) |
normalized_cov_params () |
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predict ([exog, transform]) |
Call self.model.predict with self.params as the first argument. |
prsquared () |
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pvalues () |
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remove_data () |
remove data arrays, all nobs arrays from result and model |
resid () |
Residuals |
save (fname[, remove_data]) |
save a pickle of this instance |
summary ([yname, xname, title, alpha, yname_list]) |
Summarize the Regression Results |
summary2 ([yname, xname, title, alpha, ...]) |
Experimental function to summarize 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. |