Class to contain results from likelihood models
Parameters: | model : LikelihoodModel instance or subclass instance
params : 1d array_like
normalized_cov_params : 2d array
scale : float
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Returns: | **Attributes** : mle_retvals : dict
mle_settings : dict
model : model instance
params : ndarray
scale : float
tvalues : array
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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.
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 |
t_test(r_matrix[, cov_p, scale, use_t]) | Compute a t-test for a each linear hypothesis of the form Rb = q |
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. |
Attributes
use_t |