问计算学院化的删除残差和outlier_test()以预测OLS
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Stack Overflow用户

提问于 2019-12-24 09:28:48

回答 1查看 329关注 0票数 0

我正在使用statmodel OLS进行迭代异常值消除。我已经用来拟合模型了。

ols_result = sm.OLS(y,X).fit()

然后我可以得到学院化的删除残差外部和bonferroni

ols_result.outlier_test(method="bonf")

我正在删除超过bonferroni p> %10的样本，其中cooks距离在每次迭代中也是最高的。直到我没有得到bonf(p)>%10的样本，然后我得到了原始样本的子集。

假设我有400个样本，在删除异常值后，我得到了380个样本。现在我想找出学院化的删除残差和bonferroni，再用400个样本对380个样本进行回归拟合。以查看删除的异常值是否真的是异常值。

这就是问题所在。我正在寻找一种简单的方法来使用statmodels OLS模型来获得拟合值的残差和厨师距离，而不是自己编写这些函数。但是.outlier_test()和.get_influence()似乎可以处理OLS Result对象。

你们有没有什么简单的方法来实现这些测试而不需要太多的代码等等。

提前感谢

statistics

linear-regression

python

回答 1

Stack Overflow用户

回答已采纳

发布于 2019-12-26 15:42:57

经过对这种实用性的深入研究。我找不到用statmodels库做这件事的简单方法。相反，我编写了自己的代码，也得到了statmodel源代码的帮助。如果有人需要我在下面分享的代码

def internally_studentized_residual(X,Y,y_hat):
    """

    Calculate studentized residuals internal

    Parameters
    ______________________________________________________________________

    X:            List
                  Variable x-axis

    Y:            List
                  Response variable of the X 


    y_hat:        List
                  Predictions of the response variable with the given X

    Returns
    ______________________________________________________________________

    Dataframe :   List
                  Studentized Residuals   


    """

#     print(len(Y))
    X = np.array(X, dtype=float)
    Y = np.array(Y, dtype=float)
    y_hat = np.array(y_hat,dtype=float)
    mean_X = np.mean(X)
    mean_Y = np.mean(Y)
    n = len(X)

#     print(X.shape,Y.shape,y_hat.shape)

    residuals = Y - y_hat

    X_inverse = np.linalg.pinv(X.reshape(-1,1))[0]
    h_ii = X_inverse.T * X

    Var_e = math.sqrt(sum((Y - y_hat) ** 2)/(n-2))
    SE_regression = Var_e/((1-h_ii) ** 0.5)
    studentized_residuals = residuals/SE_regression
    return studentized_residuals

def deleted_studentized_residual(X,Y,y_hat):
    """

    Calculate studentized residuals external

    Parameters
    ______________________________________________________________________

    X:            List
                  Variable x-axis

    Y:            List
                  Response variable of the X 


    y_hat:        List
                  Predictions of the response variable with the given X

    Returns
    ______________________________________________________________________

    Dataframe :   List
                  Studentized Residuals External  


    """
    #formula from https://newonlinecourses.science.psu.edu/stat501/node/401/
    r = internally_studentized_residual(X,Y,y_hat)
    n = len(r)
    return [r_i*math.sqrt((n-2-1)/(n-2-r_i**2)) for r_i in r]


def outlier_test(X,Y,y_hat,alpha=0.1):
    """

    outlier test for the points

    Parameters
    ______________________________________________________________________

    X:            List
                  Variable x-axis

    Y:            List
                  Response variable of the X 


    y_hat:        List
                  Predictions of the response variable with the given X

    alpha:        float
                  alpha value for multiple test

    Returns
    ______________________________________________________________________

    Dataframe :   studentized, unadjusted p values and benferroni multiple 
                  test dataframe  


    """
    resid = deleted_studentized_residual(X,Y,y_hat)
    df = len(X) - 1 
    p_vals = stats.t.sf(np.abs(resid),df) * 2
    bonf_test = multipletests(p_vals,alpha,method="bonf")
    df_result = pd.DataFrame()
    df_result.loc[:,"student_resid"] = resid
    df_result.loc[:,"unadj_p"] = p_vals
    df_result.loc[:,"bonf(p)"] = bonf_test[1]
    df_result.index = X.index

    return df_result