arviz_stats.rhat_nested

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arviz_stats.rhat_nested#

arviz_stats.rhat_nested(data, sample_dims=None, group='posterior', var_names=None, filter_vars=None, method='rank', coords=None, superchain_ids=None, chain_axis=0, draw_axis=1)[source]#

Compute nested R-hat.

Nested R-hat is a convergence diagnostic useful when running many short chains. It is calculated on superchains, which are groups of chains that have been initialized at the same point.

Note that there is a slight difference in the calculation of R-hat and nested R-hat, as nested R-hat is lower bounded by 1. This means that nested R-hat with one chain per superchain will not be exactly equal to basic R-hat see [1] for details.

Parameters:
dataarray_like, xarray.DataArray, xarray.Dataset, xarray.DataTree, DataArrayGroupBy, DatasetGroupBy, or idata-like

Input data. It will have different pre-processing applied to it depending on its type:

  • array-like: call array layer within arviz-stats.

  • xarray object: apply dimension aware function to all relevant subsets

  • others: passed to arviz_base.convert_to_dataset

At least 2 posterior chains are needed to compute this diagnostic of one or more stochastic parameters.

sample_dimsiterable of hashable, optional

Dimensions to be considered sample dimensions and are to be reduced. Default rcParams["data.sample_dims"].

grouphashable, default “posterior”

Group on which to compute the R-hat.

var_namesstr or list of str, optional

Names of the variables for which the Rhat should be computed.

filter_vars{None, “like”, “regex”}, default None
methodstr, default “rank”

Valid methods are: - “rank” # recommended by Vehtari et al. (2021) - “split” - “folded” - “z_scale” - “identity”

coordsdict, optional

Dictionary of dimension/index names to coordinate values defining a subset of the data for which to perform the computation.

superchain_idslist

Lisf ot length chains specifying which superchain each chain belongs to. There should be equal numbers of chains in each superchain. All chains within the same superchain are assumed to have been initialized at the same point.

chain_axis, draw_axisint, optional

Integer indicators of the axis that correspond to the chain and the draw dimension. chain_axis can be None.

See also

arviz.rhat

Calculate estimate of the effective sample size (ess).

arviz.ess

Calculate Markov Chain Standard Error statistic.

plot_forest

Forest plot to compare HDI intervals from a number of distributions.

References

[1]

Margossian et al Nested R-hat: Assessing the convergence of Markov Chain Monte Carlo when running many short chains. Bayesian Analysis, (2024). https://doi.org/10.1214/24-BA1453

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