What does the formula y ~ x + 0 in R actually calculate?

Question

What is the statistical difference between doing a linear regression in R with the formula set to y ~ x + 0 instead of y ~ x? How do I interpret those two different results?

Answer 1

Adding +0 (or -1) to a model formula (e.g., in lm()) in R suppresses the intercept. This is generally considered a bad thing to do; see:

• When is it OK to remove the intercept in lm()?
• When forcing intercept of 0 in linear regression is acceptable/advisable

The estimated slope is calculated differently depending on whether the intercept is estimated as well, namely:

\begin{align} \hat\beta_1 &= \frac{\sum x_iy_i - \frac{\big(\sum x_i\big)\big(\sum y_i\big)}{N}}{\sum x_i^2 - \frac{\big(\sum x_i\big)^2}{N}} \tag{with intercept} \\[15pt] \hat\beta_1 &= \frac{\sum x_iy_i}{\sum x_i^2} \tag{without intercept} \end{align}

Since the quantity to be subtracted (the "subtrahend") in both the numerator and denominator are not necessarily $0$, the estimate of the slope is biased when the intercept is suppressed.

The value for $R^2$ is also calculated differently; see:

• Removal of statistically significant intercept term boosts $R^2$ in linear model
• How can R2 have two different values for the same regression (without an intercept)

Here are the underlying formulas:

\begin{align} R^2 &= 1 - \frac{\sum (y_i - \hat y_i)^2}{\sum (y_i - \bar y)^2} \tag{with intercept} \\[15pt] R^2 &= 1 - \frac{\sum (y_i - \hat y_i)^2}{\sum y_i^2} \tag{without intercept} \end{align}

Answer 2

It depends on context (of course), in the lm(...) command in R it will suppress the intercept. That is, you do regression though the origin.

Note that most textbook on the subject of regression, will tell you that forcing the intercept (to any value) is a bad idea.

The interpretation of x does not change, but the value (comparing with and without an intercept) will change, sometimes very significantly.