arviz_stats.mode

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arviz_stats.mode#

arviz_stats.mode(data, dim=None, group='posterior', var_names=None, filter_vars=None, coords=None, **kwargs)[source]#

Compute the mode.

The mode is the value that appears most frequently in a data set. If the data is of type float, we assume it is continuous and use the half-sample method [1]. If the data is of type int, we assume it is discrete and use numpy.unique to find the most frequent value.

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 then treated as xarray.Dataset. This option is discouraged due to needing this conversion which is completely automated and will be needed again in future executions or similar functions.

    It is recommended to first perform the conversion manually and then call arviz_stats.mode. This allows controlling the conversion step and inspecting its results.

dimsequence of hashable, optional

Dimensions over which to compute the mode. Defaults to rcParams["data.sample_dims"].

grouphashable, default “posterior”

Group on which to compute the mode

var_namesstr or list of str, optional

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

filter_vars{None, “like”, “regex”}, default None
coordsdict, optional

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

**kwargsany, optional

Forwarded to the array or dataarray interface for mode.

Returns:
ndarray, xarray.DataArray, xarray.Dataset, xarray.DataTree

Requested mode of the provided input.

See also

xarray.Dataset.mean, xarray.Dataset.median

References

[1]

Bickel DR, Fruehwirth R. On a Fast, Robust Estimator of the Mode: Comparisons to Other Robust Estimators with Applications. Computational Statistics & Data Analysis. 2006. https://doi.org/10.1016/j.csda.2005.07.011 arXiv preprint https://doi.org/10.48550/arXiv.math/0505419

Examples

Calculate the mode of a Normal random variable:

In [1]: import arviz_stats as azs
   ...: import numpy as np
   ...: data = np.random.default_rng().normal(size=2000)
   ...: azs.mode(data)
   ...: 
Out[1]: array(0.38766724)

Calculate the modes for specific variables:

In [2]: import arviz_base as azb
   ...: dt = azb.load_arviz_data("centered_eight")
   ...: azs.mode(dt, var_names=["mu", "theta"])
   ...: 
Out[2]: 
<xarray.DataTree 'posterior'>
Group: /posterior
    Dimensions:  (school: 8)
    Coordinates:
      * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
    Data variables:
        mu       float64 8B 6.265
        theta    (school) float64 64B 3.258 5.921 3.061 6.42 2.393 3.359 3.554 3.351

Calculate the modes excluding the school dimension:

In [3]: azs.mode(dt, dim=["chain", "draw"])
Out[3]: 
<xarray.DataTree 'posterior'>
Group: /posterior
    Dimensions:  (school: 8)
    Coordinates:
      * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
    Data variables:
        mu       float64 8B 6.265
        theta    (school) float64 64B 3.258 5.921 3.061 6.42 2.393 3.359 3.554 3.351
        tau      float64 8B 1.176
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