7  R-Squared

The coefficient of determination, often referred to as \(R^2\), is an important measure of model fit in statistics and data science when the dependent variable is quantitative. First introduced by Write (Write1921?), \(R^2\) is the proportion of variance in the dependent variable explained by the independent variable(s).

To begin, we must first define variance. Broadly, variance is a measure of spread around the mean. Most textbooks provide the following equation for variance:

Total sum of squares:

\[ SS_{total} = \Sigma{(x_i - \bar{x})^2} \]

Regression sum of squares:

\[ SS_{regression} = \Sigma{(\hat{y_i} - \bar{y})^2} \]

Error sum of squares:

\[ SS_{error} = \Sigma{(y_i - \hat{y_i})^2} \]

Thinking of variance as a measure of variation is generally approachable for students, and interestingly, Write (1921) used the word variation instead of variance in his seminal paper. The key for students is in the numerator, where each observation is subtracted from the mean (i.e. \(x_i - \bar{x}\)).

Plot representing variance.

It turns out there are several ways of calculating \(R^2\), many provide equivelent solutions. Most introductions rely on algebra and complicated formulas. This paper provides an approach to understanding \(R^2\) using visualizations.

Scatter plots with residuals (black lines), resisuals squared (grey squares), and mean square error (orange; average area of the residuals squared).

Scatter plots with total variance (grey square) with error variance (left; orange) and regression variance (left; blue)

Scatter plot with variance components.

7.0.1 Example 2

library(VisualStats)
set.seed(42)
df <- VisualStats::simulate(n = 100, r_squared = .7)
formu <- y ~ x1 + x2
lm(formu, df) |> summary()

Call:
lm(formula = formu, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.0818 -1.5623 -0.1948  1.5037  5.9495 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   5.0042     0.2405   20.80  < 2e-16 ***
x1            2.6608     0.2310   11.52  < 2e-16 ***
x2           -1.7987     0.2661   -6.76 1.04e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.393 on 97 degrees of freedom
Multiple R-squared:  0.6416,    Adjusted R-squared:  0.6342 
F-statistic: 86.82 on 2 and 97 DF,  p-value: < 2.2e-16

r_squared_vis(df, formu,
              plot_total_variance = FALSE,
              plot_error_variance = FALSE,
              plot_regression_variance = FALSE,
              plot_all_variances = FALSE,
              plot_residuals_squared = FALSE,
              plot_residuals = FALSE)

Scatter plot of observed values versus predicted values.

p1 <- r_squared_vis(df, formu,
              plot_total_variance = TRUE,
              plot_error_variance = FALSE,
              plot_regression_variance = FALSE,
              plot_all_variances = FALSE,
              plot_residuals_squared = FALSE,
              plot_residuals = FALSE) + 
    ggplot2::ylim(c(-20,20)) +
    ggplot2::xlim(c(-20,20)) + ggplot2::ggtitle('')
p2 <- variance_vis(df$y, 
                   sample_variance_col = '#999999', 
                   plot_sample_variance = TRUE,
                   plot_population_variance = FALSE,
                   variance_position = 'middle',
                   point_size = 1) + 
    ggplot2::ylim(c(0,20))
cowplot::plot_grid(p1, p2)

Total Variance

r_squared_vis(df, formu,
              plot_total_variance = FALSE,
              plot_error_variance = FALSE,
              plot_regression_variance = FALSE,
              plot_all_variances = FALSE,
              plot_residuals_squared = TRUE,
              plot_residuals = TRUE)

Resisuals and squared residuals

r_squared_vis(df, formu,
              plot_total_variance = FALSE,
              plot_error_variance = TRUE,
              plot_regression_variance = FALSE,
              plot_all_variances = FALSE,
              plot_residuals_squared = TRUE,
              plot_residuals = TRUE)

Resisuals and squared residuals

r_squared_vis(df, formu,
              plot_total_variance = TRUE,
              plot_error_variance = TRUE,
              plot_regression_variance = FALSE,
              plot_all_variances = FALSE,
              plot_residuals_squared = FALSE,
              plot_residuals = FALSE)

Resisuals and squared residuals