arviz.loo_pit¶
-
arviz.
loo_pit
(idata=None, *, y=None, y_hat=None, log_weights=None)[source]¶ Compute leave one out (PSIS-LOO) probability integral transform (PIT) values.
- Parameters
- idata: InferenceData
InferenceData object.
- y: array, DataArray or str
Observed data. If str, idata must be present and contain the observed data group
- y_hat: array, DataArray or str
Posterior predictive samples for
y
. It must have the same shape as y plus an extra dimension at the end of size n_samples (chains and draws stacked). If str or None, idata must contain the posterior predictive group. If None, y_hat is taken equal to y, thus, y must be str too.- log_weights: array or DataArray
Smoothed log_weights. It must have the same shape as
y_hat
- dask_kwargsdict, optional
Dask related kwargs passed to
wrap_xarray_ufunc()
.
- Returns
- loo_pit: array or DataArray
Value of the LOO-PIT at each observed data point.
Examples
Calculate LOO-PIT values using as test quantity the observed values themselves.
In [1]: import arviz as az ...: data = az.load_arviz_data("centered_eight") ...: az.loo_pit(idata=data, y="obs") ...: Out[1]: array([0.9371042 , 0.6431774 , 0.35337847, 0.60401409, 0.30656125, 0.38854481, 0.91068648, 0.65459044])
Calculate LOO-PIT values using as test quantity the square of the difference between each observation and mu. Both
y
andy_hat
inputs will be array-like, butidata
will still be passed in order to calculate thelog_weights
from there.In [2]: T = data.observed_data.obs - data.posterior.mu.median(dim=("chain", "draw")) ...: T_hat = data.posterior_predictive.obs - data.posterior.mu ...: T_hat = T_hat.stack(__sample__=("chain", "draw")) ...: az.loo_pit(idata=data, y=T**2, y_hat=T_hat**2) ...: Out[2]: <xarray.DataArray (school: 8)> array([0.87398249, 0.30511323, 0.29976772, 0.21657564, 0.37194385, 0.20309491, 0.82478976, 0.3397874 ]) Coordinates: * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon'