Hi, I have a system of ODE's I can solve with lsoda.
Model=function(t,x,parms) { #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]) return(list(xdot)) } 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) out=lsoda(x0,times,Model,parms) mse=mean((log10(out[,4])-log10(i(times)))^2) # cat(times) return(mse) } #x0=c(T0,I0,V0) x0=c(2249,0,1) #parms(lambda, beta, d, delta, p, c) parms[5:6]=c(1.0,23) s0=c(49994,8456,6.16E-8,0.012) #initial values fit=optim(s0,Modelfit) 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, Leslie ______________________________________________ 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.