# statsmodels.base.model.LikelihoodModelResults¶

class statsmodels.base.model.LikelihoodModelResults(model, params, normalized_cov_params=None, scale=1.0, **kwargs)[source]

Class to contain results from likelihood models

Parameters: model (LikelihoodModel instance or subclass instance) – LikelihoodModelResults holds a reference to the model that is fit. params (1d array_like) – parameter estimates from estimated model normalized_cov_params (2d array) – Normalized (before scaling) covariance of params. (dot(X.T,X))**-1 scale (float) – For (some subset of models) scale will typically be the mean square error from the estimated model (sigma^2) **Attributes** mle_retvals (dict) – Contains the values returned from the chosen optimization method if full_output is True during the fit. Available only if the model is fit by maximum likelihood. See notes below for the output from the different methods. mle_settings (dict) – Contains the arguments passed to the chosen optimization method. Available if the model is fit by maximum likelihood. See LikelihoodModel.fit for more information. model (model instance) – LikelihoodResults contains a reference to the model that is fit. params (ndarray) – The parameters estimated for the model. scale (float) – The scaling factor of the model given during instantiation. tvalues (array) – The t-values of the standard errors.

Notes

The covariance of params is given by scale times normalized_cov_params.

Return values by solver if full_output is True during fit:

‘newton’
fopt : float
The value of the (negative) loglikelihood at its minimum.
iterations : int
Number of iterations performed.
score : ndarray
The score vector at the optimum.
Hessian : ndarray
The Hessian at the optimum.
warnflag : int
1 if maxiter is exceeded. 0 if successful convergence.
converged : bool
True: converged. False: did not converge.
allvecs : list
List of solutions at each iteration.
‘nm’
fopt : float
The value of the (negative) loglikelihood at its minimum.
iterations : int
Number of iterations performed.
warnflag : int
1: Maximum number of function evaluations made. 2: Maximum number of iterations reached.
converged : bool
True: converged. False: did not converge.
allvecs : list
List of solutions at each iteration.
‘bfgs’
fopt : float
Value of the (negative) loglikelihood at its minimum.
gopt : float
Value of gradient at minimum, which should be near 0.
Hinv : ndarray
value of the inverse Hessian matrix at minimum. Note that this is just an approximation and will often be different from the value of the analytic Hessian.
fcalls : int
Number of calls to loglike.
gcalls : int
warnflag : int
1: Maximum number of iterations exceeded. 2: Gradient and/or function calls are not changing.
converged : bool
True: converged. False: did not converge.
allvecs : list
Results at each iteration.
‘lbfgs’
fopt : float
Value of the (negative) loglikelihood at its minimum.
gopt : float
Value of gradient at minimum, which should be near 0.
fcalls : int
Number of calls to loglike.
warnflag : int

Warning flag:

• 0 if converged
• 1 if too many function evaluations or too many iterations
• 2 if stopped for another reason
converged : bool
True: converged. False: did not converge.
‘powell’
fopt : float
Value of the (negative) loglikelihood at its minimum.
direc : ndarray
Current direction set.
iterations : int
Number of iterations performed.
fcalls : int
Number of calls to loglike.
warnflag : int
1: Maximum number of function evaluations. 2: Maximum number of iterations.
converged : bool
True : converged. False: did not converge.
allvecs : list
Results at each iteration.
‘cg’
fopt : float
Value of the (negative) loglikelihood at its minimum.
fcalls : int
Number of calls to loglike.
gcalls : int
warnflag : int
1: Maximum number of iterations exceeded. 2: Gradient and/ or function calls not changing.
converged : bool
True: converged. False: did not converge.
allvecs : list
Results at each iteration.
‘ncg’
fopt : float
Value of the (negative) loglikelihood at its minimum.
fcalls : int
Number of calls to loglike.
gcalls : int
hcalls : int
Number of calls to hessian.
warnflag : int
1: Maximum number of iterations exceeded.
converged : bool
True: converged. False: did not converge.
allvecs : list
Results at each iteration.

Methods

 bse() 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. initialize(model, params, **kwd) llf() load(fname) load a pickle, (class method) normalized_cov_params() predict([exog, transform]) Call self.model.predict with self.params as the first argument. pvalues() remove_data() remove data arrays, all nobs arrays from result and model save(fname[, remove_data]) save a pickle of this instance summary() t_test(r_matrix[, cov_p, scale, use_t]) Compute a t-test for a each linear hypothesis of the form Rb = q t_test_pairwise(term_name[, method, alpha, …]) perform pairwise t_test with multiple testing corrected p-values 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. wald_test_terms([skip_single, …]) Compute a sequence of Wald tests for terms over multiple columns

Attributes

 use_t