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	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.ci_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

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

Raises

Name	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 categorical.