Pascale,
If you do want an nls fit with the associated error structure
assumptions, check ?SSlogis.
fm <- nls(y ~ SSlogis(x, Asy, xmid, scal))
summary(fm)
xx <- seq(123, 248, length = 101)
yy <- predict(fm, list(x = xx))
plot(x, y)
lines(xx, yy)
-Peter Ehlers
Gabor Grothendieck wrote:
A simple y vs log(x) fit seems to work pretty well here:
fit <- lm(y ~ log(x))
summary(fit)
plot(y ~ log(x))
abline(fit)
On Fri, Dec 4, 2009 at 9:06 AM, Pascale Weber <pascale.we...@wsl.ch> wrote:
Hi to all
This is the first time I am quoting a question and I hope, my question is
not too basic...
For the following data, I wish to draw a fitted curve.
x <- c(123,129,141,144,144,145,149,150,158,159,163,174,183,187,242,248)
y <-
c(14.42,26.96,31.3,19.95,36.36,15.4,24.76,35.39,28.07,40.97,26.23,42.83,46.53,14.79,49.18,48.08)
If I plot the data, it looks somehow that a logistic function would render
good results.
My questions are:
How do I use
nls and/or SSlogis (or other)
to fit the curve?
How can I see the summary statistics of the fit?
How do I finally draw the line to my x,y (untransformed data) plot?
Any help would be highly appreciated.
Thank you and cheers
Pascale
--
____________________________________..___________________
Dr. Pascale Weber
Swiss Federal Research Institute WSL
Zuercherstrasse 111
CH-8903 Birmensdorf
Switzerland
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
