arviz.mcse#
- arviz.mcse(data, *, var_names=None, method='mean', prob=None, dask_kwargs=None)[source]#
Calculate Markov Chain Standard Error statistic.
- Parameters
- dataobj
Any object that can be converted to an
arviz.InferenceData
object Refer to documentation ofarviz.convert_to_dataset()
for details For ndarray: shape = (chain, draw). For n-dimensional ndarray transform first to dataset withaz.convert_to_dataset
.- var_nameslist
Names of variables to include in the rhat report
- methodstr
Select mcse method. Valid methods are: - “mean” - “sd” - “median” - “quantile”
- probfloat
Quantile information.
- dask_kwargsdict, optional
Dask related kwargs passed to
wrap_xarray_ufunc()
.
- Returns
- xarray.Dataset
Return the msce dataset
See also
Examples
Calculate the Markov Chain Standard Error using the default arguments:
In [1]: import arviz as az ...: data = az.load_arviz_data("non_centered_eight") ...: az.mcse(data) ...: Out[1]: <xarray.Dataset> Dimensions: (school: 8) Coordinates: * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: mu float64 0.06787 theta_t (school) float64 0.02117 0.01655 0.01758 ... 0.01886 0.0185 0.01861 tau float64 0.0739 theta (school) float64 0.1196 0.09312 0.1104 ... 0.09868 0.1054 0.1068
Calculate the Markov Chain Standard Error using the quantile method:
In [2]: az.mcse(data, method="quantile", prob=0.7) Out[2]: <xarray.Dataset> Dimensions: (school: 8) Coordinates: * school (school) object 'Choate' 'Deerfield' ... "St. Paul's" 'Mt. Hermon' Data variables: mu float64 0.0966 theta_t (school) float64 0.02069 0.03194 0.02927 ... 0.02107 0.03014 tau float64 0.08686 theta (school) float64 0.1886 0.1385 0.1313 ... 0.1247 0.1144 0.1243