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
Name
Type
Description
cap_data
pandas.DataFrame
A DataFrame with the create_cap_data and model predictions.
Raises
Name
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.