arviz.from_cmdstan#
- arviz.from_cmdstan(posterior: Optional[Union[str, List[str]]] = None, *, posterior_predictive: Optional[Union[str, List[str]]] = None, predictions: Optional[Union[str, List[str]]] = None, prior: Optional[Union[str, List[str]]] = None, prior_predictive: Optional[Union[str, List[str]]] = None, observed_data: Optional[str] = None, observed_data_var: Optional[Union[str, List[str]]] = None, constant_data: Optional[str] = None, constant_data_var: Optional[Union[str, List[str]]] = None, predictions_constant_data: Optional[str] = None, predictions_constant_data_var: Optional[Union[str, List[str]]] = None, log_likelihood: Optional[Union[str, List[str]]] = None, index_origin: Optional[int] = None, coords: Optional[Dict[str, List[Any]]] = None, dims: Optional[Dict[str, List[str]]] = None, disable_glob: Optional[bool] = False, save_warmup: Optional[bool] = None, dtypes: Optional[Dict] = None) arviz.data.inference_data.InferenceData [source]#
Convert CmdStan data into an InferenceData object.
For a usage example read the Creating InferenceData section on from_cmdstan
- Parameters
- posteriorstr or list of str, optional
List of paths to output.csv files.
- posterior_predictivestr or list of str, optional
Posterior predictive samples for the fit. If endswith “.csv” assumes file.
- predictionsstr or list of str, optional
Out of sample predictions samples for the fit. If endswith “.csv” assumes file.
- priorstr or list of str, optional
List of paths to output.csv files
- prior_predictivestr or list of str, optional
Prior predictive samples for the fit. If endswith “.csv” assumes file.
- observed_datastr, optional
Observed data used in the sampling. Path to data file in Rdump or JSON format.
- observed_data_varstr or list of str, optional
Variable(s) used for slicing observed_data. If not defined, all data variables are imported.
- constant_datastr, optional
Constant data used in the sampling. Path to data file in Rdump or JSON format.
- constant_data_varstr or list of str, optional
Variable(s) used for slicing constant_data. If not defined, all data variables are imported.
- predictions_constant_datastr, optional
Constant data for predictions used in the sampling. Path to data file in Rdump or JSON format.
- predictions_constant_data_varstr or list of str, optional
Variable(s) used for slicing predictions_constant_data. If not defined, all data variables are imported.
- log_likelihooddict of {str: str}, list of str or str, optional
Pointwise log_likelihood for the data. log_likelihood is extracted from the posterior. It is recommended to use this argument as a dictionary whose keys are observed variable names and its values are the variables storing log likelihood arrays in the Stan code. In other cases, a dictionary with keys equal to its values is used. By default, if a variable
log_lik
is present in the Stan model, it will be retrieved as pointwise log likelihood values. UseFalse
to avoid this behaviour.- index_originint, optional
Starting value of integer coordinate values. Defaults to the value in rcParam
data.index_origin
.- coordsdict of {str: array_like}, optional
A dictionary containing the values that are used as index. The key is the name of the dimension, the values are the index values.
- dimsdict of {str: list of str}, optional
A mapping from variables to a list of coordinate names for the variable.
- disable_globbool
Don’t use glob for string input. This means that all string input is assumed to be variable names (samples) or a path (data).
- save_warmupbool
Save warmup iterations into InferenceData object, if found in the input files. If not defined, use default defined by the rcParams.
- dtypesdict or str
A dictionary containing dtype information (int, float) for parameters. If input is a string, it is assumed to be a model code or path to model code file.
- Returns
- InferenceData object