arviz.mcse#
- arviz.mcse(data, *, var_names=None, method='mean', prob=None, dask_kwargs=None)[source]#
Calculate Markov Chain Standard Error statistic.
- Parameters:
- data
obj
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_names
list
Names of variables to include in the rhat report
- method
str
Select mcse method. Valid methods are: - “mean” - “sd” - “median” - “quantile”
- prob
float
Quantile information.
- dask_kwargs
dict
, optional Dask related kwargs passed to
wrap_xarray_ufunc()
.
- data
- 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> Size: 656B 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]: az.mcse(data, method="quantile", prob=0.7) Out[2]: <xarray.Dataset> Size: 656B 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