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arviz.plot_lm — ArviZ dev documentation

arviz.plot_lm(y, idata=None, x=None, y_model=None, y_hat=None, num_samples=50, kind_pp='samples', kind_model='lines', xjitter=False, plot_dim=None, backend=None, y_kwargs=None, y_hat_plot_kwargs=None, y_hat_fill_kwargs=None, y_model_plot_kwargs=None, y_model_fill_kwargs=None, y_model_mean_kwargs=None, backend_kwargs=None, show=None, figsize=None, textsize=None, axes=None, legend=True, grid=True)[source]#

Posterior predictive and mean plots for regression-like data.

Parameters:

ystr or DataArray or ndarray

If str, variable name from observed_data.

idataInferenceData, Optional

Optional only if y is not str.

xstr, tuple of strings, DataArray or array_like, optional

If str or tuple, variable name from constant_data. If ndarray, could be 1D, or 2D for multiple plots. If None, coords name of y (y should be DataArray).

y_modelstr or Sequence, Optional

If str, variable name from posterior. Its dimensions should be same as y plus added chains and draws.

y_hatstr, Optional

If str, variable name from posterior_predictive. Its dimensions should be same as y plus added chains and draws.

num_samplesint, Optional, Default 50

Significant if kind_pp is “samples” or kind_model is “lines”. Number of samples to be drawn from posterior predictive or

kind_pp{“samples”, “hdi”}, Default “samples”

Options to visualize uncertainty in data.

kind_model{“lines”, “hdi”}, Default “lines”

Options to visualize uncertainty in mean of the data.

plot_dimstr, Optional

Necessary if y is multidimensional.

backendstr, Optional

Select plotting backend {“matplotlib”,”bokeh”}. Default “matplotlib”.

y_kwargsdict, optional

Passed to matplotlib.axes.Axes.plot() in matplotlib and bokeh:bokeh.plotting.Figure.circle() in bokeh

y_hat_plot_kwargsdict, optional

Passed to matplotlib.axes.Axes.plot() in matplotlib and bokeh:bokeh.plotting.Figure.circle() in bokeh

y_hat_fill_kwargsdict, optional

Passed to arviz.plot_hdi()

y_model_plot_kwargsdict, optional

Passed to matplotlib.axes.Axes.plot() in matplotlib and bokeh:bokeh.plotting.Figure.line() in bokeh

y_model_fill_kwargsdict, optional

Significant if kind_model is “hdi”. Passed to arviz.plot_hdi()

y_model_mean_kwargsdict, optional

Passed to matplotlib.axes.Axes.plot() in matplotlib and bokeh:bokeh.plotting.Figure.line() in bokeh

backend_kwargsdict, optional

These are kwargs specific to the backend being used. Passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure().

figsize(float, float), optional

Figure size. If None it will be defined automatically.

textsizefloat, optional

Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on figsize.

axes2D numpy array_like of matplotlib Axes or bokeh_figures, optional

A 2D array of locations into which to plot the densities. If not supplied, Arviz will create its own array of plot areas (and return it).

showbool, optional

Call backend show function.

legendbool, optional

Add legend to figure. By default True.

gridbool, optional

Add grid to figure. By default True.

Returns:

axes: matplotlib axes or bokeh figures

See also

plot_ts

Plot timeseries data

plot_ppc

Plot for posterior/prior predictive checks

Examples

Plot regression default plot

>>> import arviz as az
>>> import numpy as np
>>> import xarray as xr
>>> idata = az.load_arviz_data('regression1d')
>>> x = xr.DataArray(np.linspace(0, 1, 100))
>>> idata.posterior["y_model"] = idata.posterior["intercept"] + idata.posterior["slope"]*x
>>> az.plot_lm(idata=idata, y="y", x=x)

Plot regression data and mean uncertainty

>>> az.plot_lm(idata=idata, y="y", x=x, y_model="y_model")

Plot regression data and mean uncertainty in hdi form

>>> az.plot_lm(
...     idata=idata, y="y", x=x, y_model="y_model", kind_pp="hdi", kind_model="hdi"
... )

Plot regression data for multi-dimensional y using plot_dim

>>> data = az.from_dict(
...     observed_data = { "y": np.random.normal(size=(5, 7)) },
...     posterior_predictive = {"y": np.random.randn(4, 1000, 5, 7) / 2},
...     dims={"y": ["dim1", "dim2"]},
...     coords={"dim1": range(5), "dim2": range(7)}
... )
>>> az.plot_lm(idata=data, y="y", plot_dim="dim1")

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