arviz_stats.mcse

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arviz_stats.mcse#

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

Calculate Markov Chain Standard Error statistic.

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

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 ESS.

var_namesstr or list of str, optional

Names of the variables for which the mcse 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.

methodstr, default “mean”

Valid methods are:

  • “mean”

  • “sd”

  • “median”

  • “quantile”

probfloat, or tuple of two floats, optional

Probability value “quantile”.

chain_axis, draw_axisint, optional

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

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

Requested mcse summary of the provided input

See also

arviz.ess

Compute autocovariance estimates for every lag for the input array.

arviz.summary

Create a data frame with summary statistics.

arviz_plots.plot_mcse

Plot quantile or local Monte Carlo Standard Error.

Examples

Calculate the Markov Chain Standard Error using the default arguments:

In [1]: from arviz_base import load_arviz_data
   ...: import arviz_stats as azs
   ...: data = load_arviz_data('non_centered_eight')
   ...: azs.mcse(data)
   ...: 
Out[1]: 
<xarray.DataTree 'posterior'>
Group: /posterior
    Dimensions:  (school: 8)
    Coordinates:
      * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
    Data variables:
        mu       float64 8B 0.08102
        theta_t  (school) float64 64B 0.02339 0.01925 0.02092 ... 0.01931 0.01906
        tau      float64 8B 0.0791
        theta    (school) float64 64B 0.1285 0.103 0.1306 ... 0.1158 0.1193 0.1218

Calculate the Markov Chain Standard Error using the quantile method:

In [2]: azs.mcse(data, method="quantile", prob=0.7)
Out[2]: 
<xarray.DataTree 'posterior'>
Group: /posterior
    Dimensions:  (school: 8)
    Coordinates:
      * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
    Data variables:
        mu       float64 8B 0.1305
        theta_t  (school) float64 64B 0.034 0.02491 0.0319 ... 0.02363 0.03383
        tau      float64 8B 0.1145
        theta    (school) float64 64B 0.1776 0.1047 0.1426 ... 0.156 0.1508 0.1209
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