Stewart Schultz <schultz at fig.cox.miami.edu> writes:
How would I plot several (24) fitted curves (nonlinear least squares)
within a single plot?
Are you sure you want to do that? Overlaying 24 curves on a single
plot tends to make for a busy plot. You may want to consider a
trellis type of plot instead. See xyplot from the lattice package.
To get the parameters I use:
resff <- list()
for (s in levels(PairID)) {
resff[[s]] <- nls(Photo ~ SSasymp(Ci, Asym, lrc, c0),
subset = (PairID == s))
}
An alternative would be to use the nlsList function from the nlme
package.
If you really do want to plot all the fitted nls models on a single
plot, it is best to use predict on the fitted model with a closely
spaced set of Ci values that cover the range of the observed data. It
could look like
plot(Ci, Photo, type = "n") # sets up the plotting scales
crange <- range(Ci)
cseq <- seq(crange[1], crange[2], len = 100)
for (s in levels(PairID)) {
lines(cseq, predict(resff[[s]], list(Ci = cseq)))
}
Is it also possible to plot two different groups (male, female) of curves
in the same plot, with different style lines?
Yes.
From your questions it seems that you may want to consider some of the
techniques available in the nlme package. The book
@Book{pinh:bate:2000,
author = {Jos\'{e} C. Pinheiro and Douglas M. Bates},
title = {Mixed-Effects Models in \textsf{S} and \textsf{S-PLUS}},
publisher = {Springer},
year = 2000,
series = {Statistics and Computing}
}
has several examples of these techniques.
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