interpret.predictions

interpret.predictions(model, idata, conditional=None, average_by=None, target='mean', pps=False, use_hdi=True, prob=None, transforms=None, sample_new_groups=False)

Compute Conditional Adjusted Predictions

Parameters

Name	Type	Description	Default
`model`	bambi.Model	The model for which we want to plot the predictions.	required
`idata`	arviz.InferenceData	The InferenceData object that contains the samples from the posterior distribution of the model.	required
`conditional`	(str, list, dict)	The covariates we would like to condition on. If dict, keys are the covariate names and values are the values to condition on.	`None`
`average_by`	Union[str, list, bool, None]	The covariates we would like to average by. The passed covariate(s) will marginalize over the other covariates in the model. If True, it averages over all covariates in the model to obtain the average estimate. Defaults to `None`.	`None`
`target`	str	Which model parameter to plot. Defaults to ‘mean’. Passing a parameter into target only works when pps is False as the target may not be available in the posterior predictive distribution.	`'mean'`
`pps`	bool	Whether to plot the posterior predictive samples. Defaults to `False`.	`False`
`use_hdi`	bool	Whether to compute the highest density interval (defaults to True) or the quantiles.	`True`
`prob`	float	The probability for the credibility intervals. Must be between 0 and 1. Defaults to 0.94. Changing the global variable `az.rcParam["stats.hdi_prob"]` affects this default.	`None`
`transforms`	dict	Transformations that are applied to each of the variables being plotted. The keys are the name of the variables, and the values are functions to be applied. Defaults to `None`.	`None`
`sample_new_groups`	bool	If the model contains group-level effects, and data is passed for unseen groups, whether to sample from the new groups. Defaults to `False`.	`False`

Returns

Type	Description
pandas.DataFrame	A DataFrame with the `create_cap_data` and model predictions.

Raises

Type	Description
ValueError	If `pps` is `True` and `target` is not `"mean"`. If `conditional` is a list and the length is greater than 3. If `prob` is not > 0 and < 1.