arviz_plots.plot_ppc_tstat

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arviz_plots.plot_ppc_tstat#

arviz_plots.plot_ppc_tstat(dt, var_names=None, group='posterior_predictive', filter_vars=None, sample_dims=None, t_stat='median', kind=None, point_estimate=None, ci_kind=None, ci_prob=None, plot_collection=None, coords=None, backend=None, data_pairs=None, labeller=None, aes_by_visuals=None, visuals=None, stats=None, **pc_kwargs)[source]#

Plot Bayesian t-stat for observed data and posterior/prior predictive.

Parameters:
dtxarray.DataTree

If group is “posterior_predictive”, it should contain the posterior_predictive and observed_data groups. If group is “prior_predictive”, it should contain the prior_predictive group.

var_namesstr or list of str, optional

One or more variables to be plotted. Prefix the variables by ~ when you want to exclude them from the plot.

groupstr,

Group to be plotted. Defaults to “posterior_predictive”. It could also be “prior_predictive”.

filter_vars{None, “like”, “regex”}, default=None

If None, interpret var_names as the real variables names. If “like”, interpret var_names as substrings of the real variables names. If “regex”, interpret var_names as regular expressions on the real variables names.

sample_dimsstr or sequence of hashable, optional

Dimensions to reduce unless mapped to an aesthetic. Defaults to rcParams["data.sample_dims"]

t_statstr, float, or callable() default “median”

Test statistics to compute from the observations and predictive distributions. Allowed strings are “mean”, “median”, “std”, “var”, “min”, “max”, “iqr” (interquartile range) and “mad” (median absolute deviation). Alternative a quantile can be passed as a float (or str) in the interval (0, 1). Finally, a user defined function is also accepted.

kind{“kde”, “hist”, “dot”, “ecdf”}, optional

How to represent the marginal density. Defaults to rcParams["plot.density_kind"]

point_estimate{“mean”, “median”, “mode”}, optional

Which point estimate to plot. Defaults to rcParam stats.point_estimate

ci_kind{“eti”, “hdi”}, optional

Which credible interval to use. Defaults to rcParams["stats.ci_kind"]

ci_probfloat, optional

Indicates the probability that should be contained within the plotted credible interval. Defaults to rcParams["stats.ci_prob"]

plot_collectionPlotCollection, optional
coordsdict, optional
backend{“matplotlib”, “bokeh”, “plotly”}, optional
labellerlabeller, optional
data_pairsdict, optional

Dictionary of keys prior/posterior predictive data and values observed data variable names. If None, it will assume that the observed data and the predictive data have the same variable name.

aes_by_visualsmapping of {strsequence of str}, optional

Mapping of visuals to aesthetics that should use their mapping in plot_collection when plotted. Valid keys are the same as for visuals exept for “remove_axis”

visualsmapping of {strmapping or False}, optional

Valid keys are:

  • dist -> depending on the value of kind passed to:

    • “kde” -> passed to line_xy

    • “ecdf” -> passed to ecdf_line

    • “hist” -> passed to :func: hist

  • observed_tstat -> passed to scatter_x.

  • credible_interval -> passed to line_x. Defaults to False.

  • point_estimate -> passed to scatter_x. Defaults to False.

  • point_estimate_text -> passed to point_estimate_text. Defaults to False.

  • title -> passed to labelled_title

  • rug -> passed to scatter_x. Defaults to False.

  • remove_axis -> not passed anywhere, can only be False to skip calling this function

observed_tstat defaults to False, no observed data is plotted, if group is “prior_predictive”. Pass an (empty) mapping to plot the observed tstats.

statsmapping, optional

Valid keys are:

  • dist -> passed to kde, ecdf, …

**pc_kwargs

Passed to arviz_plots.PlotCollection.wrap

Returns:
PlotCollection

Examples

Use 25th percentile (quantile 0.25) as t-statistic

>>> from arviz_plots import plot_ppc_tstat, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> dt = load_arviz_data('radon')
>>> plot_ppc_tstat(dt, t_stat="0.25")
../../_images/arviz_plots-plot_ppc_tstat-1.png

Define custom t-statistic function and plot histogram

>>> def cv(x):
>>>     return np.std(x, axis=0) / np.mean(x, axis=0)
>>> plot_ppc_tstat(dt, t_stat=cv, kind="hist")
../../_images/arviz_plots-plot_ppc_tstat-2.png

Use median as t-statistic and plot point-interval

>>> azp.plot_ppc_tstat(
>>>     dt,
>>>     visuals={
>>>         "dist": False,
>>>         "credible_interval": {},
>>>         "point_estimate": {},
>>>     }
>>> )
../../_images/arviz_plots-plot_ppc_tstat-3.png

T-statistic for the observed data and posterior predictive data.

Test statistics

Test statistics
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