I understand from this question - When is R squared negative?

that the R squared value of a linear regression model can be negative if the intercept is constrained. And this makes sense if you define R squared as -

$$R^2 = 1-\frac{SSE}{SST}$$

One says $SSE>SST$. But then, $$SST = SSA + SSE $$ Total Sum of squares = Sum of squared errors + Sum of squared residual. And with this we get - $$R^2 = \frac{SSA}{SST}$$ And now it is hard to imagine how $R^2$ can be negative. Aren't SSA and SST >0 always?

I understand from this question - When is R squared negative?

that the R squared value of a linear regression model can be negative if the intercept is constrained. And this makes sense if you define R squared as -

$$R^2 = 1-\frac{SSE}{SST}$$

One says $SSE>SST$. But then, $$SST = SSA + SSE $$ Total Sum of squares = Sum of squared errors + Sum of squared residual. And with this we get - $$R^2 = \frac{SSA}{SST}$$ And now it is hard to imagine how $R^2$ can be negative. Aren't SSA and SST >0 always?