Plot residuals from a known linear model in R

Question

This question is similar to this one, where I would like to plot the residuals, except that my residuals are known, since I'm simply comparing simulated and observed values with an expected 1:1 linear relationship.

For example, consider a data.frame subset:

sub <- data.frame(observed=c(-0.75, 0, 1.84, -0.33, -1.6),
                  simulated=c(-0.73644, 0.00422, 1.69897, 0.04321, -1.59478))
sub$residual <- with(sub, observed - simulated)
sub

  observed simulated residual
1    -0.75  -0.73644 -0.01356
2     0.00   0.00422 -0.00422
3     1.84   1.69897  0.14103
4    -0.33   0.04321 -0.37321
5    -1.60  -1.59478 -0.00522

par(pty="s")
plot(simulated ~ observed, sub)
abline(0, 1)  # expected 1:1 line
sub.lm <- lm(simulated ~ observed, sub)
abline(sub.lm, col="red")  # the best-fit line is close to the 1:1

The wrong approach would be to inspect and plot the imperfect linear model fit:

plot(sub.lm)   # these are useful plots, but slightly different than I would expect
resid(sub.lm)  # and these are obviously different than calculated directly

          1           2           3           4           5 
-0.06967768 -0.03752067 -0.08096466  0.31321064 -0.12504764 

How can I perform a similar analysis/plot with the known residuals from a perfect 1:1 linear model?

What I've tried so far is:

plot(residual ~ 1, sub)

But it isn't quite the same as plot(sub.lm) from above.

Answer 1

The most obvious thing to do is to plot residuals vs predicted (i.e. simulated) - you can plot this directly, without reference to a program to fit regression.

However, if you want to use a regression to fit it, you need an intercept of 0 and an offset of simulated, if your program allows for an offset. (In R that's lm(observed~0,offset=simulated))

That fit can be used produce most of the usual diagnostic plots, but not all (leverage, for example, is 0, so the leverage based plots won't work).