3.2.2.3.13. statsmodels.discrete.discrete_model.L1MultinomialResults¶

class statsmodels.discrete.discrete_model.L1MultinomialResults(model, mlefit)[source]¶

A results class for multinomial data fit by l1 regularization

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) nnz_params : Integer The number of nonzero parameters in the model. Train with trim_params == True or else numerical error will distort this. trimmed : Boolean array trimmed[i] == True if the ith parameter was trimmed from the model.

__init__(model, mlefit)[source]¶