How to compute Studentized Residuals in Python?

Question

I've tried searching for an answer to this problem but so far I haven't found any. I used statsmodel to implement an Ordinary Least Squares regression model on a mean-imputed dataset. I can access the list of residuals in the OLS results, but not studentized residuals. How can I calculate/get studentized residuals? I know the formula for calculating studentized residuals but I'm not exactly sure how to code this formula in Python.

Thanks in advance.

UPDATE: I've found the answer. I can get a dataframe containing the studentized residuals from the outlier_test() function from OLS reults.

Answer 1

I was dealing with the same issue. The solution is to use the statsmodels library:

from statsmodels.stats.outliers_influence import OLSInfluence

It has a resid_studentized_internal method included.

Answer 2

Nodar's implementation is incorrect here is the corrected formula from https://newonlinecourses.science.psu.edu/stat501/node/339/ as well as the deleted studentized residual in case people don't want to use statsmodels package. Both formulas return the same result as the examples in the link above

def internally_studentized_residual(X,Y):
    X = np.array(X, dtype=float)
    Y = np.array(Y, dtype=float)
    mean_X = np.mean(X)
    mean_Y = np.mean(Y)
    n = len(X)
    diff_mean_sqr = np.dot((X - mean_X), (X - mean_X))
    beta1 = np.dot((X - mean_X), (Y - mean_Y)) / diff_mean_sqr
    beta0 = mean_Y - beta1 * mean_X
    y_hat = beta0 + beta1 * X
    residuals = Y - y_hat
    h_ii = (X - mean_X) ** 2 / diff_mean_sqr + (1 / n)
    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):
    #formula from https://newonlinecourses.science.psu.edu/stat501/node/401/
    r = internally_studentized_residual(X,Y)
    n = len(r)
    return [r_i*math.sqrt((n-2-1)/(n-2-r_i**2)) for r_i in r]

Answer 3

Use the OLSRresults.outlier_test() function to produce a dataset that contains the studentized residual for each observation.

For example:

#import necessary packages and functions
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.formula.api import ols

#create dataset
df = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],
                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19]})

#fit simple linear regression model
model = ols('rating ~ points', data=df).fit()

#calculate studentized residuals
stud_res = model.outlier_test()

#display studentized residuals
print(stud_res)

student_resid    unadj_p     bonf(p)
0   -0.486471   0.641494    1.000000
1   -0.491937   0.637814    1.000000
2    0.172006   0.868300    1.000000
3    1.287711   0.238781    1.000000
4    0.106923   0.917850    1.000000
5    0.748842   0.478355    1.000000
6   -0.968124   0.365234    1.000000
7   -2.409911   0.046780    0.467801
8    1.688046   0.135258    1.000000
9   -0.014163   0.989095    1.000000

This tutorial provides a full explanation: https://www.statology.org/studentized-residuals-in-python/

Answer 4

For a simple linear regression, you can calculate studentized residuals using following

define mean of X and Y as :

mean_X = sum(X) / len(X) 
mean_Y = sum(Y) / len(Y) 

Now you have to estimate coefficients beta_0 and beta_1

beta1 = sum([(X[i] - mean_X)*(Y[i] - mean_Y) for i in range(len(X))]) / sum([(X[i] - mean_X)**2 for i in range(len(X))]) 
beta0 = mean_Y - beta1 * mean_X

Now you need to find fitted values, by using this

y_hat = [beta0 + beta1*X[i] for i in range(len(X))]

Now compute Residuals, which is Y - Y_hat

residuals = [Y[i] - y_hat[i] for i in range(len(Y))]

We need to find H matrix which is where X is the matrix of our independent variables.

To find leverage, we have to take the diagonal elements of H matrix, in the following way:

leverage = numpy.diagonal(H)

Find Standard Error if regression as

Var_e = sum([(Y[i] - y_hat[i])**2 for i in range(len(Y)) ]) / (len(Y) -2)
SE_regression = math.sqrt(Var_e*[(1-leverage[i]) for i in range len(leverage)])

Now you can compute Studentized Residuals

studentized_residuals = [residuals[i]/SE_regression for i in range(len(residuals))] 

Note that we have two types of studentized residuals. One is Internally Studentized Residuals and second is Externally Studentized Residuals

My solution finds Internally Studentized Residuals.

I made corrections in my calculation. For externally studentized residuals, refer @kkawabat's answer