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

model : bambi.Model: The model for which we want to plot the predictions.
idata : arviz.InferenceData: The InferenceData object that contains the samples from the posterior distribution of the model.
conditional : (str, list, dict) = None: The covariates we would like to condition on. If dict, keys are the covariate names and values are the values to condition on.
average_by : str | list | None = 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.
target : str = 'mean': 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. Defaults to "mean".
sample_new_groups : bool = False: If the model contains group-level effects, and data is passed for unseen groups, whether to sample from the new groups. Defaults to False.
pps : bool = False: Whether to plot the posterior predictive samples. Defaults to False.
use_hdi : bool = True: Whether to compute the highest density interval (defaults to True) or the quantiles.
prob : float = None: 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.
legend : bool = True: Whether to automatically include a legend in the plot. Defaults to True.
transforms : dict = None: 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.
ax : matplotlib.axes._subplots.AxesSubplot = None: A matplotlib axes object or a sequence of them. If None, this function instantiates a new axes object. Defaults to None.
fig_kwargs : optional = None: 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.
subplot_kwargs : optional = None: 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.

Returns

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

Raises

: 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.