arviz.mcse — ArviZ dev documentation (original) (raw)
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 of arviz.convert_to_dataset() for details For ndarray: shape = (chain, draw). For n-dimensional ndarray transform first to dataset with az.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:
Return the msce dataset
See also
Compute autocovariance estimates for every lag for the input array.
Create a data frame with summary statistics.
Plot quantile or local Monte Carlo Standard Error.
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