mixedmodels.py 文件源码

def simple_slope_categories(res, df, target, cat, cats):
    exog = {}
    for param in res.fe_params.index:
        if len(param.split(":")) != 1:
            continue
        if param == "Intercept":
            exog[param] = 1.0
        elif param in cats:
            exog[param] = 0
        else:
            exog[param] = np.mean(df[param])
    if cat != None:
        exog[cat] = 1
    x_points = []
    y_points = []
    for target_perc in [10, 90]:
        exog[target] = np.percentile(df[target], target_perc)
#        exog[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])
        y_points.append(res.model.predict(res.fe_params, exog=exog_arr))
        x_points.append(exog[target])
    return x_points, y_points