mixedmodels.py 文件源码

def simple_slope_percentiles(res, df, target, varying, percs=[25, 50, 75]):
    exog = {}
    for param in res.fe_params.index:
        if len(param.split(":")) != 1:
            continue
        if param == "Intercept":
            exog[param] = 1.0
        else:
            exog[param] = np.median(df[param])
    ret_vals = collections.OrderedDict()
    for varying_perc in percs:
        exog[varying] = np.percentile(df[varying], varying_perc)
        ret_vals[exog[varying]] = collections.defaultdict(list)
        for target_perc in [25, 75]:
            exog[target] = np.percentile(df[target], target_perc)
            exog_arr = np.array([exog[param] if len(param.split(":")) == 1 else exog[param.split(":")[0]] * exog[param.split(":")[1]]
                for param in res.fe_params.index])
            ret_vals[exog[varying]]["endog"].append(res.model.predict(res.fe_params, exog=exog_arr))
            ret_vals[exog[varying]]["target"].append(exog[target])
    return ret_vals