add ploting arguments

bambi/models.py

@@ -1,4 +1,5 @@
 # pylint: disable=no-name-in-module
+# pylint: disable=too-many-lines
 import re
 import warnings
 from collections import OrderedDict
@@ -726,7 +727,62 @@ def plot(self, draws=5000, var_names=None):
         warnings.warn("plot will be deprecated, please use plot_priors", FutureWarning)
         return self.plot_priors(draws, var_names)
 
-    def plot_priors(self, draws=5000, var_names=None):
+    def plot_priors(
+        self,
+        draws=5000,
+        var_names=None,
+        random_seed=None,
+        figsize=None,
+        textsize=None,
+        hdi_prob=None,
+        round_to=2,
+        point_estimate="mean",
+        kind="kde",
+        bins=None,
+        ax=None,
+    ):
+        """
+        Samples from the prior distribution and plot its marginals.
+
+        Parameters
+        ----------
+        draws : int
+            Number of draws to sample from the prior predictive distribution. Defaults to 5000.
+        var_names : str or list
+            A list of names of variables for which to compute the posterior predictive
+            distribution. Defaults to both observed and unobserved RVs.
+        random_seed : int
+            Seed for the random number generator.
+        figsize: tuple
+            Figure size. If None it will be defined automatically.
+        textsize: float
+            Text size scaling factor for labels, titles and lines. If None it will be autoscaled
+            based on figsize.
+        hdi_prob: float, optional
+            Plots highest density interval for chosen percentage of density.
+            Use 'hide' to hide the highest density interval. Defaults to 0.94.
+        round_to: int, optional
+            Controls formatting of floats. Defaults to 2 or the integer part, whichever is bigger.
+        point_estimate: Optional[str]
+            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.
+        kind: str
+            Type of plot to display (kde or hist) For discrete variables this argument is ignored
+            and a histogram is always used.
+        bins: integer or sequence or 'auto', optional
+            Controls the number of bins, accepts the same keywords `matplotlib.hist()` does.
+            Only works if `kind == hist`. If None (default) it will use `auto` for continuous
+            variables and `range(xmin, xmax + 1)` for discrete variables.
+        ax: 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).
+        **kwargs
+            Passed as-is to plt.hist() or plt.plot() function depending on the value of `kind`.
+
+        Returns
+        -------
+        axes: matplotlib axes or bokeh figures
+        """
         if not self.built:
             raise ValueError("Cannot plot priors until model is built!")
 
@@ -736,9 +792,20 @@ def plot_priors(self, draws=5000, var_names=None):
                 unobserved_rvs_names, include_transformed=False
             )
 
-        pps = self.prior_predictive(draws=draws, var_names=var_names)
-
-        axes = plot_posterior(pps, group="prior", credible_interval=None, point_estimate=None)
+        pps = self.prior_predictive(draws=draws, var_names=var_names, random_seed=random_seed)
+
+        axes = plot_posterior(
+            pps,
+            group="prior",
+            figsize=figsize,
+            textsize=textsize,
+            hdi_prob=hdi_prob,
+            round_to=round_to,
+            point_estimate=point_estimate,
+            kind=kind,
+            bins=bins,
+            ax=ax,
+        )
 
         return axes