Diagnostics¶
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hbayesdm.diagnostics.
rhat
(model_data: hbayesdm.base.TaskModel, less: float = None) → Dict[str, Union[List[T], bool]]¶ Function for extracting Rhat values from hbayesdm output.
Convenience function for extracting Rhat values from hbayesdm output. Also possible to check if all Rhat values are less than a specified value.
Parameters: - model_data – Output instance of running an hbayesdm model function.
- less – [Optional] Upper-bound value to compare extracted Rhat values to.
Returns: Dict – Keys are names of the parameters; values are their Rhat values. Or if less was specified, the dictionary values will hold True if all Rhat values (of that parameter) are less than or equal to less.
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hbayesdm.diagnostics.
print_fit
(*args, ic: str = 'looic') → pandas.core.frame.DataFrame¶ Print model-fits (mean LOOIC or WAIC values) of hbayesdm models.
Parameters: - args – Output instances of running hbayesdm model functions.
- ic – Information criterion (defaults to ‘looic’).
Returns: pd.DataFrame – Model-fit info per each hbayesdm output given as argument(s).
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hbayesdm.diagnostics.
hdi
(x: numpy.ndarray, credible_interval: float = 0.94) → numpy.ndarray¶ Calculate highest density interval (HDI).
This function acts as an alias to arviz.hpd function.
Parameters: - x – Array containing MCMC samples.
- credible_interval – Credible interval to compute. Defaults to 0.94.
Returns: np.ndarray – Array containing the lower and upper value of the computed interval.
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hbayesdm.diagnostics.
plot_hdi
(x: numpy.ndarray, credible_interval: float = 0.94, title: str = None, xlabel: str = 'Value', ylabel: str = 'Density', point_estimate: str = None, bins: Union[int, Sequence[T_co], str] = 'auto', round_to: int = 2, **kwargs)¶ Plot highest density interval (HDI).
This function redirects input to arviz.plot_posterior function.
Parameters: - x – Array containing MCMC samples.
- credible_interval – Credible interval to plot. Defaults to 0.94.
- title – String to set as title of plot.
- xlabel – String to set as the x-axis label.
- ylabel – String to set as the y-axis label.
- point_estimate – Defaults to None. Possible options are ‘mean’, ‘median’, ‘mode’.
- bins – Controls the number of bins. Defaults to ‘auto’. Accepts the same values (or keywords) as plt.hist() does.
- round_to – Controls formatting for floating point numbers. Defaults to 2.
- **kwargs – Passed as-is to plt.hist().
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hbayesdm.diagnostics.
extract_ic
(model_data: hbayesdm.base.TaskModel, ic: str = 'both', ncore: int = 2) → Dict[KT, VT]¶ Extract model comparison estimates.
Parameters: - model_data – hBayesDM output objects from running model functions.
- ic – Information criterion. ‘looic’, ‘waic’, or ‘both’. Defaults to ‘both’.
- ncore – Number of cores to use when computing LOOIC. Defaults to 2.
Returns: Dict – Leave-One-Out and/or Watanabe-Akaike information criterion estimates.