interpret.plot_predictions

interpret.plot_predictions(model, idata, conditional=None, average_by=None, target='mean', sample_new_groups=False, pps=False, use_hdi=True, prob=None, transforms=None, legend=True, ax=None, fig_kwargs=None, subplot_kwargs=None)

Plot 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, 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'`
`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`
`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`
`legend`	bool	Whether to automatically include a legend in the plot. Defaults to `True`.	`True`
`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`
`ax`	matplotlib.axes._subplots.AxesSubplot	A matplotlib axes object or a sequence of them. If None, this function instantiates a new axes object. Defaults to `None`.	`None`
`fig_kwargs`	optional	Keyword arguments passed to the matplotlib figure function as a dict. For example, `fig_kwargs=dict(figsize=(11, 8)), sharey=True` would make the figure 11 inches wide by 8 inches high and would share the y-axis values.	`None`
`subplot_kwargs`	optional	Keyword arguments used to determine the covariates used for the horizontal, group, and panel axes. For example, `subplot_kwargs=dict(main="x", group="y", panel="z")` would plot the horizontal axis as `x`, the color (hue) as `y`, and the panel axis as `z`.	`None`

Returns

Type	Description
(matplotlib.figure.Figure, matplotlib.axes._subplots.AxesSubplot)	A tuple with the figure and the axes.

Raises

Type	Description
ValueError	If `conditional` and `average_by` are both `None`. If length of `conditional` is greater than 3 and `average_by` is `None`. If main covariate is not numeric or categoric.