In R I use nls to do a nonlinear least-squares fit. How then do I plot the model function using the values of the coefficients that the fit provided?
(Yes, this is a very naive question from an R relative newbie.)
Answer
Using the first example from ?nls and following the example I pointed you to line by line achieves the following:
#This is just our data frame
DNase1 <- subset(DNase, Run == 1)
DNase1$lconc <- log(DNase1$conc)
#Fit the model
fm1DNase1 <- nls(density ~ SSlogis(lconc, Asym, xmid, scal), DNase1)
#Plot the original points
# first argument is the x values, second is the y values
plot(DNase1$lconc,DNase1$density)
#This adds to the already created plot a line
# once again, first argument is x values, second is y values
lines(DNase1$lconc,predict(fm1DNase1))
The predict method for a nls argument is automatically returning the fitted y values. Alternatively, you add a step and do
yFitted <- predict(fm1DNase1)
and pass yFitted in the second argument to lines instead. The result looks like this:
Or if you want a "smooth" curve, what you do is to simply repeat this but evaluate the function at more points:
r <- range(DNase1$lconc)
xNew <- seq(r[1],r[2],length.out = 200)
yNew <- predict(fm1DNase1,list(lconc = xNew))
plot(DNase1$lconc,DNase1$density)
lines(xNew,yNew)