def plot_loglogs(cls, loc=None, iloc=None, show_censors=False, censor_styles=None, **kwargs):
    """
    Specifies a plot of the log(-log(SV)) versus log(time) where SV is the estimated survival function.
    """

    def loglog(s):
        return np.log(-np.log(s))

    if (loc is not None) and (iloc is not None):
        raise ValueError("Cannot set both loc and iloc in call to .plot().")

    if censor_styles is None:
        censor_styles = {}

    set_kwargs_ax(kwargs)
    set_kwargs_color(kwargs)
    set_kwargs_drawstyle(kwargs)
    kwargs["logx"] = True

    dataframe_slicer = create_dataframe_slicer(iloc, loc)

    # plot censors
    ax = kwargs["ax"]
    colour = kwargs["c"]

    if show_censors and cls.event_table["censored"].sum() > 0:
        cs = {"marker": "+", "ms": 12, "mew": 1}
        cs.update(censor_styles)
        times = dataframe_slicer(cls.event_table.loc[(cls.event_table["censored"] > 0)]).index.values.astype(float)
        v = cls.predict(times)
        # don't log times, as Pandas will take care of all log-scaling later.
        ax.plot(times, loglog(v), linestyle="None", color=colour, **cs)

    # plot estimate
    dataframe_slicer(loglog(cls.survival_function_)).plot(**kwargs)
    ax.set_xlabel("log(timeline)")
    ax.set_ylabel("log(-log(survival_function_))")
    return ax
Specifies a plot of the log(-log(SV)) versus log(time) where SV is the estimated survival function.