Fitting data with optim or nls--different time scales
<see in line>
Leslie Chavez wrote:
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:
SG: What about the following:
Modelfit=function(s) {
parms[1:4]=s[1:4];
times=c(0,7,12,14,17,20,25)
lsodaTimes <- seq(min(times),max(times), by=0.01)
out=lsoda(x0,lsodaTimes,Model,parms)
obsTimes <- (100*times-1)
mse=mean((log10(out[obsTimes,4])-log10(i(times)))^2)
# cat(times)
return(mse)
}
Your example is not self contained, so obviously I haven't tried this
with it. However, something of this nature should work fine, I believe.
Something similar but different should also work, I believe, with
'nls'; this would give you access to many helper functions (see
"methods(class='nls')"). If 'nls' bombed on me, I'd then try 'optim' as
it is less brittle. Then I might use the output of 'optim' as initial
values for 'nls' to get confidence intervals etc.
hope this helps.
Spencer Graves
#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
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