[R] Fitting data with optim or nls--different time scales

From: Leslie Chavez <leslou_at_ctbp.ucsd.edu>
Date: Wed 09 Aug 2006 - 04:22:06 EST


I have a system of ODE's I can solve with lsoda.


    #parameter definitions
    lambda=parms[1]; beta=parms[2];
    d = parms[3]; delta = parms[4];

     p=parms[5];    c=parms[6]
      xdot[1] = lambda - (d*x[1])- (beta*x[3]*x[1])
      xdot[2] = (beta*x[3]*x[1]) - (delta*x[2])
      xdot[3] = (p*x[2]) - (c*x[3])


I want to fit the output out[,4] to experimental data that is only available on days 0, 7, 12, 14, 17, and 20. I don't know how to set up optim or nls so that it takes out[,4] on the appropriate day, but still runs lsoda on a time scale of 0.01 day.

Below is the function I've been using to run 'optim', at the course-grained time scale:

Modelfit=function(s) {

	parms[1:4]=s[1:4]; times=c(0,7,12,14,17,20,25)
#	cat(times)


#parms(lambda, beta, d, delta, p, c)

s0=c(49994,8456,6.16E-8,0.012) #initial values


Right now, lsoda is being run on too course-grained a time scale in the function Modelfit. Most examples of optim and nls I have found compare two data sets at the same times, and run lsoda on the time scale the data is available at, but I would like to run lsoda at a finer scale, and only compare the appropriate time points with the experiment. I have also tried using nls, but I have the same problem. Does anyone have suggestions?

Thank you very much,


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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. Received on Wed Aug 09 04:39:51 2006

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