def compute_reciprocal_errors(self, key="r"):
        r"""
        Compute reciprocal erros following LaBrecque et al. (1996) according
        to:

        .. math::

            \epsilon = \left|\frac{2(|R_n| - |R_r|)}{|R_n| + |R_r|}\right|

        Parameters
        ----------
        key : str
            Parameter to calculate the reciprocal error for (default is "r").

        Examples
        --------
        >>> import reda
        >>> ert = reda.ERT()
        >>> ert.data = reda.utils.norrec.get_test_df()
        >>> ert.data = pd.DataFrame([
        ...     [1,2,3,4,95],
        ...     [3,4,2,1,-105]], columns=list("abmnr")
        ... )
        >>> ert.compute_reciprocal_errors()
        generating ids
        assigning ids
        >>> ert.data["error"].mean() == 0.1
        True
        """

        # Assign norrec ids if not already present
        if "id" not in self.data.keys():
            self.data = assign_norrec_to_df(self.data)

        # Average repetitions
        data = average_repetitions(self.data, "r")

        # Get configurations with reciprocals
        data = data.groupby("id").filter(lambda b: not b.shape[0] == 1)
        n = self.data.shape[0] - data.shape[0]
        if n > 0:
            print("Could not find reciprocals for %d configurations" % n)

        # Calc reciprocal error
        grouped = data.groupby("id")

        def _error(group):
            R_n = group["r"].iloc[0]
            R_r = group["r"].iloc[1]
            return abs(2 * (abs(R_n) - abs(R_r)) / (abs(R_n) + abs(R_r)))

        error = grouped.apply(_error)
        error.name = "error"
        self.data = pd.merge(
            self.data,
            error.to_frame().reset_index(), how='outer',
            on='id'
        )