model.vars[data_type]["p_pred"].stats()["mean"], columns=["mean"], index=model.input_data.index
    )
    pred_ui = pandas.DataFrame(
        model.vars[data_type]["p_pred"].stats()["95% HPD interval"],
        columns=["lower", "upper"],
        index=model.input_data.index,
    )
    obs = pandas.DataFrame(model.vars[data_type]["p_obs"].value, columns=["value"], index=model.input_data.index)

    # subset only test data
    pred_test = pred.ix[test_ix]
    obs_test = obs.ix[test_ix]
    pred_ui_test = pred_ui.ix[test_ix]

    # record statistics for test data
    output["bias_" + rate_type] = mu.bias(pred_test, obs_test)
    output["rmse_" + rate_type] = mu.rmse(pred_test, obs_test)
    output["mae_" + rate_type] = mu.mae(pred_test, obs_test)
    output["mare_" + rate_type] = mu.mare(pred_test, obs_test)
    output["pc_" + rate_type] = mu.pc(pred_ui_test, obs_test)
    output["time_" + rate_type] = elapsed

    # save information
    output.to_csv("/clustertmp/dismod/model_comparison_" + str(model_num) + rate_type + str(replicate) + ".csv")

    # create and save conversion plots
    dismod3.graphics.plot_acorr(model)
    pl.savefig("/clustertmp/dismod/model_comparison_" + str(model_num) + rate_type + str(replicate) + "acorr.png")
    dismod3.graphics.plot_trace(model)
    pl.savefig("/clustertmp/dismod/model_comparison_" + str(model_num) + rate_type + str(replicate) + "trace.png")
def test_bias():
    bias = mu.bias(pred, obs)
    assert bias == 1