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# scikits.statsmodels.discretemod.DiscreteResults

class `scikits.statsmodels.discretemod.``DiscreteResults`(model, params, hessian, scale=1.0)

  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

  `margeff`([params, at, method, atexog, dummy, ...]) | Get marginal effects of the fitted model.
  `conf_int`([alpha, cols]) | Returns the confidence interval of the fitted parameters.

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