# A Beautiful Regression Formula

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# Lost in Translation between R and Python 4

Hello statistics aficionados

This is the next article in our series **“Lost in Translation between R and Python”**. The aim of this series is to provide high-quality R **and** Python 3 code to achieve some non-trivial tasks. If you are to learn R, check out the R tab below. Similarly, if you are to learn Python, the Python tab will be your friend.

The last one was a deep dive into historic mortality rates.

No Covid-19, no public data for a change: This post focusses on a real beauty, namely a decomposition of the R-squared in a linear regression model

E(y) = \alpha + \sum_{j = 1}^p x_j \beta_j

fitted by least-squares. If the **response y and all p covariables are standardized to variance 1** beforehand, then the R-squared can be obtained as the cross-product of the fitted coefficients and the usual correlations between each covariable and the response:

R^2 = \sum_{j = 1}^p \text{cor}(y, x_j)\hat\beta_j.

Two elegant derivations can be found in this answer to the same question, written by the number 1 contributor to crossvalidated: whuber. Look up a couple of his posts – and statistics will suddenly feel super easy and clear.

Direct consequences of the formula are:

- If a covariable is uncorrelated with the response, it cannot contribute to the R-squared, i.e. neither improve nor worsen. This is not obvious.
- A correlated covariable only improves R-squared if its coefficient is non-zero. Put differently: if the effect of a covariable is already fully covered by the other covariables, it does not improve the R-squared. This
*is*somewhat obvious.

Note that all formulas refer to in-sample calculations.

Since we do not want to bore you with math, we simply demonstrate the result with short R and Python codes based on the famous iris dataset.

y <- "Sepal.Width" x <- c("Sepal.Length", "Petal.Length", "Petal.Width") # Scaled version of iris iris2 <- data.frame(scale(iris[c(y, x)])) # Fit model fit <- lm(reformulate(x, y), data = iris2) summary(fit) # multiple R-squared: 0.524 (betas <- coef(fit)[x]) # Sepal.Length Petal.Length Petal.Width # 1.1533143 -2.3734841 0.9758767 # Correlations (scaling does not matter here) (cors <- cor(iris[, y], iris[x])) # Sepal.Length Petal.Length Petal.Width # -0.1175698 -0.4284401 -0.3661259 # The R-squared? sum(betas * cors) # 0.524

# Import packages import numpy as np import pandas as pd from sklearn import datasets from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression # Load data iris = datasets.load_iris(as_frame=True).data print("The data:", iris.head(3), sep = "\n") # Specify response yvar = "sepal width (cm)" # Correlations of everyone with response cors = iris.corrwith(iris[yvar]).drop(yvar) print("\nCorrelations:", cors, sep = "\n") # Prepare scaled response and covariables X = StandardScaler().fit_transform(iris.drop(yvar, axis=1)) y = StandardScaler().fit_transform(iris[[yvar]]) # Fit linear regression OLS = LinearRegression().fit(X, y) betas = OLS.coef_[0] print("\nScaled coefs:", betas, sep = "\n") # R-squared via scikit-learn: 0.524 print(f"\nUsual R-squared:\t {OLS.score(X, y): .3f}") # R-squared via decomposition: 0.524 rsquared = betas @ cors.values print(f"Applying the formula:\t {rsquared: .3f}")

**Indeed: the cross-product of coefficients and correlations equals the R-squared of 52%.**

The Python notebook and R code can be found at:

- Python: https://github.com/lorentzenchr/notebooks/blob/master/blogposts/2021-03-14%20rsquared_decomposition.py
- R: https://github.com/lorentzenchr/notebooks/blob/master/blogposts/2021-03-14%20rsquared_decomposition.r

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