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scikits.statsmodels.discrete.discrete_model.DiscreteResults

class scikits.statsmodels.discrete.discrete_model.DiscreteResults(model, mlefit)[source]

A results class for the discrete dependent variable models.

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)

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[, q_matrix, scale, invcov]) Compute an Fcontrast/F-test for a contrast matrix.
initialize(model, params, **kwd)
margeff([at, method, atexog, dummy, count]) Get marginal effects of the fitted model.
normalized_cov_params()
t([column]) deprecated: Return the t-statistic for a given parameter estimate.
t_test(r_matrix[, q_matrix, scale]) Compute a tcontrast/t-test for a row vector array of the form Rb = q

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