I'm plotting a logarithmic regression's line of best fit as well as the confidence intervals around that line. The code I'm using works well enough, except I'd rather that the confidence intervals both be "gray" (rather than the default "red" and "green"). Unfortunately, I'm not seeing a way to isolate them when specifying colour changes. I'd like
lty = 1, col = "black"
lty=2, col = "gray"
R6cl <- lm(log(R6$y) ~ R6$x)
pR6cl <- predict(R6cl, interval="confidence")
plot(R6$x, log(R6$y), type = "p")
matlines(x = R6$x, y = log(R6$y), lwd = 2, lty = 1, col = "black")
lwd are vectorized. You can use
R6cl <- lm(log(y) ~ x, data = R6) ## don't use $ in formula pR6cl <- predict(R6cl, interval = "confidence") plot(log(y) ~ x, data = R6) ## Read `?plot.formula` matlines(R6$x, pR6cl, lwd = 2, lty = c(1, 2, 2), col = c(1, 2, 2))
You can check the last figure in Piecewise regression with a quadratic polynomial and a straight line joining smoothly at a break point for what this code would produce.
If you are unclear why I advise against the use of
$ in model formula, read Predict() - Maybe I'm not understanding it.
A side notice for other readers
OP has a dataset where
x is sorted. If your
x is not sorted, make sure you sort it first. See Messy plot when plotting predictions of a polynomial regression using lm() in R for more.