statsmodels.base.model.LikelihoodModelResults

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

Class to contain results from likelihood models

Parameters

modelLikelihoodModel instance or subclass instance

LikelihoodModelResults holds a reference to the model that is fit.

params1d array_like

parameter estimates from estimated model

normalized_cov_params2d array

Normalized (before scaling) covariance of params. (dot(X.T,X))**-1

scalefloat

For (some subset of models) scale will typically be the mean square error from the estimated model (sigma^2)

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’

foptfloat

The value of the (negative) loglikelihood at its minimum.

iterationsint

Number of iterations performed.

scorendarray

The score vector at the optimum.

Hessianndarray

The Hessian at the optimum.

warnflagint

1 if maxiter is exceeded. 0 if successful convergence.

convergedbool

True: converged. False: did not converge.

allvecslist

List of solutions at each iteration.

‘nm’

foptfloat

The value of the (negative) loglikelihood at its minimum.

iterationsint

Number of iterations performed.

warnflagint

1: Maximum number of function evaluations made. 2: Maximum number of iterations reached.

convergedbool

True: converged. False: did not converge.

allvecslist

List of solutions at each iteration.

‘bfgs’

foptfloat

Value of the (negative) loglikelihood at its minimum.

goptfloat

Value of gradient at minimum, which should be near 0.

Hinvndarray

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.

fcallsint

Number of calls to loglike.

gcallsint

Number of calls to gradient/score.

warnflagint

1: Maximum number of iterations exceeded. 2: Gradient and/or function calls are not changing.

convergedbool

True: converged. False: did not converge.

allvecslist

Results at each iteration.

‘lbfgs’

foptfloat

Value of the (negative) loglikelihood at its minimum.

goptfloat

Value of gradient at minimum, which should be near 0.

fcallsint

Number of calls to loglike.

warnflagint

Warning flag:

• 0 if converged

• 1 if too many function evaluations or too many iterations

• 2 if stopped for another reason

convergedbool

True: converged. False: did not converge.

‘powell’

foptfloat

Value of the (negative) loglikelihood at its minimum.

direcndarray

Current direction set.

iterationsint

Number of iterations performed.

fcallsint

Number of calls to loglike.

warnflagint

1: Maximum number of function evaluations. 2: Maximum number of iterations.

convergedbool

True : converged. False: did not converge.

allvecslist

Results at each iteration.

‘cg’

foptfloat

Value of the (negative) loglikelihood at its minimum.

fcallsint

Number of calls to loglike.

gcallsint

Number of calls to gradient/score.

warnflagint

1: Maximum number of iterations exceeded. 2: Gradient and/ or function calls not changing.

convergedbool

True: converged. False: did not converge.

allvecslist

Results at each iteration.

‘ncg’

foptfloat

Value of the (negative) loglikelihood at its minimum.

fcallsint

Number of calls to loglike.

gcallsint

Number of calls to gradient/score.

hcallsint

Number of calls to hessian.

warnflagint

1: Maximum number of iterations exceeded.

convergedbool

True: converged. False: did not converge.

allvecslist

Results at each iteration.

Attributes

mle_retvalsdict

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_settingsdict

Contains the arguments passed to the chosen optimization method. Available if the model is fit by maximum likelihood. See LikelihoodModel.fit for more information.

modelmodel instance

LikelihoodResults contains a reference to the model that is fit.

paramsndarray

The parameters estimated for the model.

scalefloat

The scaling factor of the model given during instantiation.

tvaluesarray

Return the t-statistic for a given parameter estimate.

Methods

bse()	The standard errors of the parameter estimates.
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)	Initialize (possibly re-initialize) a Results instance.
llf()	Log-likelihood of model
load(fname)	load a pickle, (class method)
normalized_cov_params()	See specific model class docstring
predict([exog, transform])	Call self.model.predict with self.params as the first argument.
pvalues()	The two-tailed p values for the t-stats of the params.
remove_data()	remove data arrays, all nobs arrays from result and model
save(fname[, remove_data])	save a pickle of this instance
summary()	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