## prediction_draws()


Join posterior predictive draws to a covariate DataFrame.


Usage


``` python
prediction_draws(
    dt,
    newdata,
    var_name,
    idata_group="predictions",
    constant_data_group="predictions_constant_data",
    join_on="obs_ind"
)
```


The newdata frame is the *left* table. Draws are attached to it. This avoids the denormalisation problem where coefficient values are duplicated for every obs_ind -- the join goes in the right direction.


## Parameters


`dt: xr.DataTree or arviz.InferenceData`  
ArviZ InferenceData or xarray DataTree containing prediction samples.

`newdata: pl.DataFrame | pd.DataFrame | None`  
Covariate grid. If None, attempts to read from dt\[constant_data_group\]. If the group is not found, raises a clear error directing user to pass newdata explicitly.

`var_name: str`  
Name of the predictive variable to extract (e.g., "mu"). Dimensions are auto-detected from the DataArray.

`idata_group: str = ``"predictions"`  
InferenceData group containing the predictive draws ("predictions", "posterior_predictive", or custom). Default "predictions".

`constant_data_group: str = ``"predictions_constant_data"`  
Group name for the covariate grid that aligns with the prediction draws. Default "predictions_constant_data". Set this parameter if your data uses a different naming convention.

`join_on: str | list[str] = ``"obs_ind"`  
Column(s) to join newdata to the draws on. Default "obs_ind".


## Returns


`pl.DataFrame`  
Tidy DataFrame with columns: chain, draw, \[join_on cols\], \[covariate cols\], var_name One row per (chain, draw, obs_ind).


## Examples

Basic usage -- newdata read from dt

pred_df = prediction_draws(dt, newdata=None, var_name="mu") \# -\> columns: chain, draw, obs_ind, x, group, mu \# -\> 4 x 1000 x 80 = 320,000 rows

Filter before plotting -- only group 0

pred_df.filter(pl.col("group") == 0) \# -\> 4 x 1000 x 20 = 80,000 rows

Provide custom newdata (e.g., a finer grid)

fine_grid = pl.DataFrame({"x": np.linspace(0, 20, 200), "group": …}) prediction_draws(dt, newdata=fine_grid, var_name="mu")