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

tvalues() Return the t-statistic for a given parameter estimate.
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.

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); use only on trusted files, as unpickling can run arbitrary code.
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