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Computing the R-squared of a linear regression model with weighted observations in R
# Compare four methods for computing the R-squared (R2, coefficient of determination)
# with wieghted observations for a linear regression model in R.
# This work was written by Daniel Himmelstein (@dhimmel) with guidance
# from Alex Pankov (@a-pankov). It is released as CC0 (public domain).
get_r2_cor <- function(y, y_pred, w) {
# Calculate R2 using the correlation coefficient method
xy = cbind(y, y_pred)
return(boot::corr(d=xy, w=w) ^ 2)
}
get_r2_ss <- function(y, y_pred, w) {
# Calculate R2 using the Sum of Squares method
# https://en.wikipedia.org/wiki/Coefficient_of_determination#Definitions
ss_residual = sum(w * (y - y_pred) ^ 2)
ss_total = sum(w * (y - weighted.mean(y, w)) ^ 2)
return(1 - ss_residual / ss_total)
}
get_r2_likehood <- function(model, model_intercept, n) {
# Calculate R2 using the generalized (likelihood) method
# https://en.wikipedia.org/wiki/Coefficient_of_determination#Generalized_R2
L_0 = exp(as.numeric(logLik(model_intercept)))
L_null = exp(as.numeric(logLik(model)))
return(1 - (L_0 / L_null) ^ (2 / n))
}
simulate <- function(weighted = T, n = 50, seed = 0) {
# Randomly generate data, perform regression, and return the r-squared values
# produced by various methods.
# Simulate x (the predictor), y (the outcome), and w (the observation weights)
set.seed(seed)
x = runif(n)
y = runif(n)
if (weighted) {w = runif(n)} else {w = rep(1, n)}
# Fit linear regression models and compute predictions
model_intercept = lm(y ~ 1, weight=w)
model = lm(y ~ x, weight=w)
y_pred = predict(model)
# Calculate and return the four R2 measures
return(c(
r2_cor = get_r2_cor(y, y_pred, w),
r2_lm = summary(model)$r.squared,
r2_likelihood = get_r2_likehood(model, model_intercept, n),
r2_ss = get_r2_ss(y, y_pred, w)
))
}
#### Execution
seed = 1
simulate(T, seed = seed) # differing observation weights
simulate(F, seed = seed) # equal observation weights
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