Weighted Least Squares In R: A Quick WLS Tutorial

Introduction to Weighted Least Squares in R

This post will discuss the implementation of Weighted Least Squares (WLS) in R. The OLS method minimizes the sum of squared residuals, while the WLS weights the square residuals. The WLS technique is used when the OLS assumption related to constant variance in the errors is violated.

The WLS technique is also known as weighted linear regression it is a generalization of ordinary least squares (OLS) and linear regression in which knowledge of the variance of observations is incorporated into the regression.

WLS in R Language

Let us perform the WLS in R.

Here we will use the mtcars dataset. You need to load this data set first using the attach() function. For example,

attach(mtcars)

Consider the following example regarding the weighted least squares in which the reciprocal of $wt$ variable is used as weights. Here two different weighted models are performed and then check the fit of the model using anova() function.

# Weighted Model 1
w_model1 <- lm(mpg ~ wt + hp, data = mtcars)

# Weighted Model 2
w_model2 <- lm(mpg ~ wt + hp, data = mtcars, weights = 1/wt)

Check/ Test The Model Fit

To check the model fit, summary statistics of the fitted model, and different diagnostic plots of the fitted model, one can use the built-in functions as,

anova(w_model1, w_model2 )
summary(w_model1)
summary(w_model2)

plot(w_model1)
plot(w_model2)

The output of the First Weighted Model is:

Call:
lm(formula = mpg ~ wt + hp, data = mtcars)

Residuals:
   Min     1Q Median     3Q    Max 
-3.941 -1.600 -0.182  1.050  5.854 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 37.22727    1.59879  23.285  < 2e-16 ***
wt          -3.87783    0.63273  -6.129 1.12e-06 ***
hp          -0.03177    0.00903  -3.519  0.00145 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.593 on 29 degrees of freedom
Multiple R-squared:  0.8268,	Adjusted R-squared:  0.8148 
F-statistic: 69.21 on 2 and 29 DF,  p-value: 9.109e-12

The output of the Second Weighted Model is

Call:
lm(formula = mpg ~ wt + hp, data = mtcars, weights = 1/wt)

Weighted Residuals:
    Min      1Q  Median      3Q     Max 
-2.2271 -1.0538 -0.3894  0.6397  3.7627 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 39.002317   1.541462  25.302  < 2e-16 ***
wt          -4.443823   0.688300  -6.456 4.59e-07 ***
hp          -0.031460   0.009776  -3.218  0.00317 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.554 on 29 degrees of freedom
Multiple R-squared:  0.8389,	Adjusted R-squared:  0.8278 
F-statistic: 75.49 on 2 and 29 DF,  p-value: 3.189e-12

Graphical Representation of Models

The graphical representation of both models is:

FAQS Weighted Least Square in R

1. How weighted least squares can be performed in R Lanague?
2. How lm() function can be used to conduct a WLS in R?
3. What are the important arguments for performing a weighted least squares model in R?

Learn about Generalized Least Squares