arviz_stats.loo_r2

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arviz_stats.loo_r2#

arviz_stats.loo_r2(data, var_name, n_simulations=4000, summary=True, point_estimate=None, ci_kind=None, ci_prob=None, circular=False, round_to=None)[source]#

Compute LOO-adjusted \(R^2\).

Parameters:
data: DataTree or InferenceData

It should contain groups observed_data, posterior_predictive and log_likelihood.

var_namestr

Name of the observed variable

n_simulationsint, default 4000

Number of Dirichlet-weighted bootstrap samples for variance estimation.

circularbool, default False

Whether the variable is circular (angles in radians, range [-π, π]).

summary: bool

Whether to return a summary (default) or an array of \(R^2\) samples. The summary is a named tuple with a point estimate and a credible interval

point_estimate: str

The point estimate to compute. If None, the default value is used. Defaults values are defined in rcParams[“stats.point_estimate”]. Ignored if summary is False.

ci_kind: str

The kind of credible interval to compute. If None, the default value is used. Defaults values are defined in rcParams[“stats.ci_kind”]. Ignored if summary is False.

ci_prob: float

The probability for the credible interval. If None, the default value is used. Defaults values are defined in rcParams[“stats.ci_prob”]. Ignored if summary is False.

circular: bool

Whether to compute the Bayesian \(R^2\) for circular data. Defaults to False. It’s assumed that the circular data is in radians and ranges from -π to π. We use the same definition of \(R^2\) for circular data as in the linear case, but using circular variance instead of regular variance.

round_to: int or str or None, optional
If integer, number of decimal places to round the result. Integers can be negative.

If string of the form ‘2g’ number of significant digits to round the result. Defaults to rcParams[“stats.round_to”] if None. Use the string “None” or “none” to return raw numbers.

Returns:
Namedtuple or array

See also

arviz_stats.bayesian_r2

Bayesian \(R^2\).

arviz_stats.residual_r2

Residual \(R^2\).

Examples

Calculate LOO-adjusted \(R^2\) for Bayesian logistic regression:

In [1]: from arviz_stats import loo_r2
   ...: from arviz_base import load_arviz_data
   ...: data = load_arviz_data('anes')
   ...: loo_r2(data, var_name="vote")
   ...: 
Out[1]: loo_R2(mean=0.48, eti_lb=0.4, eti_ub=0.55)

Calculate LOO-adjusted \(R^2\) for circular regression:

In [2]: data = load_arviz_data('periwinkles')
   ...: loo_r2(data, var_name='direction', circular=True)
   ...: 
Out[2]: loo_R2(mean=0.8, eti_lb=0.72, eti_ub=0.87)
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