relife.regression.RegressionFittingResult
relife.regression.RegressionFittingResult¶
- class relife.regression.RegressionFittingResult(opt: scipy.optimize._optimize.OptimizeResult, jac: numpy.ndarray, var: numpy.ndarray, n_samples: int, cindex: float)[source]¶
Bases:
relife.parametric.FittingResultClass for the result of the fitted regression, inheriting from FittingResult.
Used as the type for the instance attribute ‘result’ for an object of the Regression class, after fitting.
Methods
converts FittingResult into a dictionary.
Standard error estimation function.
Attributes
cindex- asdict() dict¶
converts FittingResult into a dictionary.
- Returns
Returns the fitting result as a dictionary.
- Return type
dict
- standard_error(jac_f: numpy.ndarray) numpy.ndarray¶
Standard error estimation function.
- Parameters
jac_f (1D array) – The Jacobian of a function f with respect to params.
- Returns
Standard error for f(params).
- Return type
1D array
References
- 1
Meeker, W. Q., Escobar, L. A., & Pascual, F. G. (2022). Statistical methods for reliability data. John Wiley & Sons.
- opt: scipy.optimize._optimize.OptimizeResult¶
Optimization result (see scipy.optimize.OptimizeResult doc).
- jac: numpy.ndarray¶
Jacobian of the negative log-likelihood with the lifetime data.
- var: numpy.ndarray¶
Covariance matrix (computed as the inverse of the Hessian matrix)
- se: numpy.ndarray¶
Standard error, square root of the diagonal of the covariance matrix.
- n_samples: int¶
Number of observations (samples).
- n_params: int¶
Number of parameters.
- AIC: float¶
Akaike Information Criterion.
- AICc: float¶
Akaike Information Criterion with a correction for small sample sizes.
- BIC: float¶
Bayesian Information Criterion.