arviz.plot_lm#

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")