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)

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
Number of calls to gradient/score.
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
Number of calls to gradient/score.
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
Number of calls to gradient/score.
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.

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 (ndarray) The t-values of the standard errors.

Methods

conf_int([alpha, cols]) Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …]) Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis.
initialize(model, params, **kwargs) Initialize (possibly re-initialize) a Results instance.
load(fname) Load a pickled results instance
normalized_cov_params() See specific model class docstring
predict([exog, transform]) Call self.model.predict with self.params as the first argument.
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.
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.

Methods

conf_int([alpha, cols]) Construct confidence interval for the fitted parameters.
cov_params([r_matrix, column, scale, cov_p, …]) Compute the variance/covariance matrix.
f_test(r_matrix[, cov_p, scale, invcov]) Compute the F-test for a joint linear hypothesis.
initialize(model, params, **kwargs) Initialize (possibly re-initialize) a Results instance.
load(fname) Load a pickled results instance
normalized_cov_params() See specific model class docstring
predict([exog, transform]) Call self.model.predict with self.params as the first argument.
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.
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.

Properties

bse The standard errors of the parameter estimates.
llf Log-likelihood of model
pvalues The two-tailed p values for the t-stats of the params.
tvalues Return the t-statistic for a given parameter estimate.
use_t Flag indicating to use the Student’s distribution in inference.