def posterior_predictive_to_xarray(self):
        """Convert posterior_predictive samples to xarray."""
        posterior_predictive = self.posterior_predictive
        columns = self.posterior[0].columns
        if (
            isinstance(posterior_predictive, (tuple, list))
            and posterior_predictive[0].endswith(".csv")
        ) or (isinstance(posterior_predictive, str) and posterior_predictive.endswith(".csv")):
            if isinstance(posterior_predictive, str):
                posterior_predictive = [posterior_predictive]
            chain_data = []
            for path in posterior_predictive:
                parsed_output = _read_output(path)
                for sample, *_ in parsed_output:
                    chain_data.append(sample)
            data = _unpack_dataframes(chain_data)
        else:
            if isinstance(posterior_predictive, str):
                posterior_predictive = [posterior_predictive]
            posterior_predictive_cols = [
                col
                for col in columns
                if any(item == col.split(".")[0] for item in posterior_predictive)
            ]
            data = _unpack_dataframes([item[posterior_predictive_cols] for item in self.posterior])
        return dict_to_dataset(data, coords=self.coords, dims=self.dims)