Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance, Mirko. -- Dr. M. Luedde <Mirko.Luedde at CellControl.De> CellControl Biomedical Laboratories AG Am Klopferspitz 19, 82152 Martinsried +49-89-895275-0 +49-179-5252064 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
L?dde Mirko <mirko.luedde at cellcontrol.de> writes:> I'm quite new to R (and statistics), > and I like it (both)! > But I'm a bit lost in all these packages, > so could someone please give me a hint > whether there exists a package for fitting > exponential curves (of the type > t --> \sum_i a_i \exp( - b_i t)) > on a noisy signal? > In fact monoexponential decay + polynomial growth > is what I'd like to try.See the example for the biexponential model, SSbiexp, in the nls package for a start.> example(SSbiexp, package = "nls")SSbixp> data(Indometh) SSbixp> Indo.1 <- Indometh[Indometh$Subject == 1, ] SSbixp> fm1 <- nls(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = Indo.1) SSbixp> summary(fm1) Formula: conc ~ SSbiexp(time, A1, lrc1, A2, lrc2) Parameters: Estimate Std. Error t value Pr(>|t|) A1 2.0293 0.1099 18.464 3.39e-07 *** lrc1 0.5794 0.1247 4.648 0.00235 ** A2 0.1915 0.1106 1.731 0.12698 lrc2 -1.7878 0.7871 -2.271 0.05737 . --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: 0.04103 on 7 degrees of freedom Correlation of Parameter Estimates: A1 lrc1 A2 lrc1 0.002546 A2 -0.424384 0.8771 lrc2 -0.455538 0.7708 0.939 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Thanks! I get the impression that, since in SSbiexp the exponential constants are provided via their logarithms, both constants are assumed negative, i.e. the exponentials are decaying? Is it possible to have a linear combination of a decaying and a growing exponential? Cheers, Mirko.> -----Urspr?ngliche Nachricht----- > Von: Douglas Bates [mailto:bates at stat.wisc.edu] > Gesendet: Mittwoch, 23. Mai 2001 14:51 > An: L?dde Mirko > Cc: 'r-help at stat.math.ethz.ch'; Ralph Schwarzwald (E-Mail) > Betreff: Re: [R] help: exponential fit? > > > L?dde Mirko <mirko.luedde at cellcontrol.de> writes: > > > I'm quite new to R (and statistics), > > and I like it (both)! > > But I'm a bit lost in all these packages, > > so could someone please give me a hint > > whether there exists a package for fitting > > exponential curves (of the type > > t --> \sum_i a_i \exp( - b_i t)) > > on a noisy signal? > > In fact monoexponential decay + polynomial growth > > is what I'd like to try. > > See the example for the biexponential model, SSbiexp, in the nls > package for a start. > > > example(SSbiexp, package = "nls") > > SSbixp> data(Indometh) > > SSbixp> Indo.1 <- Indometh[Indometh$Subject == 1, ] > > SSbixp> fm1 <- nls(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), > data = Indo.1) > > SSbixp> summary(fm1) > > Formula: conc ~ SSbiexp(time, A1, lrc1, A2, lrc2) > > Parameters: > Estimate Std. Error t value Pr(>|t|) > A1 2.0293 0.1099 18.464 3.39e-07 *** > lrc1 0.5794 0.1247 4.648 0.00235 ** > A2 0.1915 0.1106 1.731 0.12698 > lrc2 -1.7878 0.7871 -2.271 0.05737 . > --- > Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' > 0.1 ` ' 1 > > Residual standard error: 0.04103 on 7 degrees of freedom > > Correlation of Parameter Estimates: > A1 lrc1 A2 > lrc1 0.002546 > A2 -0.424384 0.8771 > lrc2 -0.455538 0.7708 0.939 > >-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._