Turn off scatter plot and print only regression line - r

Question

I'm trying to fit a regression curve to my data. My code generates the plot and curve that I want, however, I don't need the scatter plot--only the line. If I comment out the plot, my code fails. Is there a way to (bypass, turn-off, hide) the scatter plot?
Ultimately, I will need to compare multiple regression curves on my graph and the scatter charts become distracting. Also, my R2 shows NULL. Is there a coefficient for R2?
Code below.
# get underlying plot
y <- dataset$'Fuel Consumed 24h'
x <-dataset$'Performance Speed'
plot(x, y, xlab = "Performance Speed", ylab = "24h Fuel Consumption")
# polynomial
f <- function(x,a,b,d) {(a*x^2) + (b*x) + d}
fit <- nls(y ~ f(x,a,b,d), start = c(a=1, b=1, d=1))
co <- round(coef(fit), 2)
r2 <- format(summary(fit)$r.squared, digits = 3)
curve(f(x, a=co[1], b=co[2], d=co[3]), add = TRUE, col="red", lwd=2)
eq <- paste0("Fuel = ", co[1], "PS^2 ", ifelse(sign(co[2]) == 1, " + ", " - "), abs(co[2]), "PS +", co[3], " R2 = ", r2)
# print equation
mtext(eq, 3, line =-2)
mylabel = bquote(italic(R)^2 == .(format(r2, digits = 3)))
text(x = 1, y = 2.5, r2)

Answer 1

Here is an example with cars data
Fit:
data(cars)
f <- function(x,a,b,d) {(a*x^2) + (b*x) + d}
fit <- nls(dist ~ f(speed,a,b,d), data = cars, start = c(a=1, b=1, d=1))
goodness of fit: (as user20650 pointed out R2 makes little sense for a non linear model, perhaps a better metric is RMSE)
rmse <- function(error)
{
sqrt(mean(error^2))
}
error <- predict(fit, cars) - cars$dist
rms <- rmse(error)
eq <- paste0("Fuel = ", co[1], "PS^2 ", ifelse(sign(co[2]) == 1, " + ", " - "), abs(co[2]), "PS +", co[3], " RMSE = ", round(rms, 2))
plot (no need to call plot at all - add other curves with add = T):
curve(f(x, a=co[1], b=co[2], d=co[3]), col="red", lwd=2, from = min(cars$speed), to = max(cars$speed))
mtext(eq, 3, line =-2)
to add another curve:
f2 = function(x, a, b) {a + b*x}
co2 = coef(lm(dist ~ speed, data = cars))
curve(f2(x, a = co2[1], b = co2[2]), col="blue", lwd=2, add = T)
EDIT: as per user20650 suggestion (the nls is really not needed since the poly and nls curves are the same)
co3 = coef(lm(dist ~ poly(speed, 2, raw=TRUE), data = cars))
curve(f3(x, a = co3[1], b = co3[2], c = co3[3]), col="grey", lty = 2, lwd=2, add = T)
legend("topleft", legend = c("nls", "lm", "poly"), col = c("red", "blue", "grey"), lty =c(1,1,2))