> # using this test data > set.seed(1) > x <- 1:20/20 > y <- exp(2 + 3 * x) + rnorm(20) > > # if its ok to fit logs so that its linear > exp(fitted(lm(log(y) ~ x))) 1 2 3 4 5 6 7 8 8.55615 9.94692 11.56376 13.44340 15.62857 18.16894 21.12223 24.55557 9 10 11 12 13 14 15 16 28.54699 33.18720 38.58165 44.85295 52.14363 60.61938 70.47284 81.92793 17 18 19 20 95.24501 110.72673 128.72494 149.64869 > > # or to do it on original scale use linear coefs as starting values > cc <- coef(lm(log(y) ~ x)) > fitted(nls(y ~ exp(a + b*x), start = list(a = cc[1], b = cc[2]))) [1] 8.592270 9.984536 11.602401 13.482421 15.667073 18.205720 [7] 21.155722 24.583734 28.567211 33.196159 38.575168 44.825776 [13] 52.089214 60.529599 70.337640 81.734946 94.979039 110.369167 [19] 128.253066 149.034820 attr(,"label") [1] "Fitted values"
On 9/27/06, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote: > Hi, > > I would like to fit some experimental points by a exponential function. > I ignore the parameters of this exponential and what I would like is to > ask R to calculate the best fitting curve an the associated parameters (as > the linear model function (lm) does for linear models). > Is it possible ? > Do anyone have an idea about how to do that ? > > Thanks by advance > > Jessica Gervais > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.