arviz.plot_dot#

arviz.plot_dot(values, binwidth=None, dotsize=1, stackratio=1, hdi_prob=None, rotated=False, dotcolor='C0', intervalcolor='C3', markersize=None, markercolor='C0', marker='o', figsize=None, linewidth=None, point_estimate='auto', nquantiles=50, quartiles=True, point_interval=None, ax=None, show=None, plot_kwargs=None, backend=None, backend_kwargs=None, **kwargs)[source]#

Plot distribution as dot plot or quantile dot plot.

This function uses the Wilkinson’s Algorithm [1] to allot dots to bins. The quantile dot plots was inspired from the paper When (ish) is My Bus? [2].

Parameters

valuesarray-like: Values to plot
binwidthfloat, optional: Width of the bin for drawing the dot plot.
dotsizefloat, optional: The size of the dots relative to the bin width. The default, 1, makes dots be just about as wide as the bin width.
stackratiofloat, optional: The distance between the center of the dots in the same stack relative to the bin height. The default, 1, makes dots in the same stack just touch each other.
point_intervalbool, optional: Plots the point interval. Uses hdi_prob to plot the HDI interval
point_estimatestr, optional: Plot point estimate per variable. Values should be ‘mean’, ‘median’, ‘mode’ or None. Defaults to ‘auto’ i.e. it falls back to default set in rcParams.
dotcolorstring, optional: The color of the dots. Should be a valid matplotlib color.
intervalcolorstring, optional: The color of the interval. Should be a valid matplotlib color.
linewidthint, optional: Line width throughout. If None it will be autoscaled based on figsize.
markersizeint, optional: Markersize throughout. If None it will be autoscaled based on figsize.
markercolor: string, optional: The color of the marker when plot_interval is True. Should be a valid matplotlib color.
marker: string, optional: The shape of the marker. Valid for matplotlib backend Defaults to “o”.
hdi_probfloat, optional: Valid only when point_interval is True. Plots HDI for chosen percentage of density. Defaults to stats.hdi_prob rcParam.
rotatedbool, optional: Whether to rotate the dot plot by 90 degrees.
nquantilesint, optional: Number of quantiles to plot, used for quantile dot plots Defaults to 50.
quartilesbool, optional: If True then the quartile interval will be plotted with the HDI. Defaults to True.
figsizetuple, optional: Figure size. If None it will be defined automatically.
plot_kwargsdict, optional: Keywords passed for customizing the dots. Passed to matplotlib.patches.Circle in matplotlib and bokeh.plotting.Figure.circle() in bokeh
backend: str, optional: Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.
axaxes, optional: Matplotlib axes or bokeh figures.
show: bool, optional: Call backend show function.
backend_kwargs: dict, optional: These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure(). For additional documentation check the plotting method of the backend.

Returns

axesmatplotlib axes or bokeh figures

See also

plot_dist: Plot distribution as histogram or kernel density estimates.

References

1: Leland Wilkinson (1999) Dot Plots, The American Statistician, 53:3, 276-281, DOI: 10.1080/00031305.1999.10474474
2: Matthew Kay, Tara Kola, Jessica R. Hullman, and Sean A. Munson. 2016. When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. DOI:https://doi.org/10.1145/2858036.2858558

Examples

Plot dot plot for a set of data points

>>> import arviz as az
>>> import numpy as np
>>> values = np.random.normal(0, 1, 500)
>>> az.plot_dot(values)

Manually adjust number of quantiles to plot

>>> az.plot_dot(values, nquantiles=100)

Add a point interval under the dot plot

>>> az.plot_dot(values, point_interval=True)

Rotate the dot plots by 90 degrees i.e swap x and y axis

>>> az.plot_dot(values, point_interval=True, rotated=True)

arviz.plot_dist_comparison

arviz.plot_elpd