[R] non-linear optimisation ODE models

[Jim Lemon]

Hi Malgorzata,
The function "rxnrate" seems to want three values in a list with the
names "k1", "k2" and "k3". If you are passing something with different
names, it is probably going to complain, so the names "A", "B" and "C"
may be your problem. I can't run the example, so this is a guess.

Jim


> On 15 Feb 2017, at 11:32, Malgorzata Wieteska via R-help <r-help at r-project.org> wrote:
>
> Hello,
> I'm new to R, so sorry for this question. I found a piece of code on stack overflow community, title: r-parameter and initial conditions fitting ODE models with nls.lm.
> I've tried to implement a change suggested, but I get an error: Error in unname(myparms[4], B = 0, C = 0) :   unused arguments (B = 0, C = 0)
> I'll appreciate any hint.
> Malgosia
>
> #set working directorysetwd("~/R/wkspace")#load librarieslibrary(ggplot2)library(reshape2)library(deSolve)library(minpack.lm)time=c(0,0.263,0.526,0.789,1.053,1.316,1.579,1.842,2.105,2.368,2.632,2.895,3.158,3.421,3.684,3.947,4.211,4.474,4.737,5)ca=c(0.957,0.557,0.342,0.224,0.123,0.079,0.035,0.029,0.025,0.017,-0.002,0.009,-0.023,0.006,0.016,0.014,-0.009,-0.03,0.004,-0.024)cb=c(-0.031,0.33,0.512,0.499,0.428,0.396,0.303,0.287,0.221,0.148,0.182,0.116,0.079,0.078,0.059,0.036,0.014,0.036,0.036,0.028)cc=c(-0.015,0.044,0.156,0.31,0.454,0.556,0.651,0.658,0.75,0.854,0.845,0.893,0.942,0.899,0.942,0.991,0.988,0.941,0.971,0.985)df<-data.frame(time,ca,cb,cc)dfnames(df)=c("time","ca","cb","cc")#plot datatmp=melt(df,id.vars=c("time"),variable.name="species",value.name="conc")ggplot(data=tmp,aes(x=time,y=conc,color=species))+geom_point(size=3)#rate functionrxnrate=function(t,c,parms){  #rate constant passed through a list called  k1=parms$k1  k2=parms$k2  k3=parms$k3  #c is the concentratio!
>  n of species  #derivatives dc/dt are computed below  r=rep(0,length(c))  r[1]=-k1*c["A"] #dcA/dt  r[2]=k1*c["A"]-k2*c["B"]+k3*c["C"] #dcB/dt  r[3]=k2*c["B"]-k3*c["C"] #dcC/dt  return(list(r))}# predicted concentration for a given parametercinit=c(A=1,B=0,C=0)t=df$timeparms=list(k1=2, k2=1, k3=3)out=ode(y=cinit,times=t,func=rxnrate,parms=list(k1=k1,k2=k2,k3=k3))head(out)
>> ssq=function(myparms){  #initial concentration  cinit=c(A=myparms[4],B=0,C=0)  cinit=c(A=unname(myparms[4],B=0,C=0))  print(cinit)  #time points for which conc is reported  #include the points where data is available  t=c(seq(0,5,0.1),df$time)  t=sort(unique(t))  #parameters from the parameters estimation  k1=myparms[1]  k2=myparms[2]  k3=myparms[3]  #solve ODE for a given set of parameters  out=ode(y=cinit,times=t,func=rxnrate,parms=list(k1=k1,k2=k2,k3=k3))  #Filter data that contains time points  outdf=data.frame(out)  outdf=outdf[outdf$time%in% df$time,]  #Evaluate predicted vs experimental residual  preddf=melt(outdf,id.var="time",variable.name="species",value.name="conc")  expdf=melt(df,id.var="time",variable.name="species",value.name="conc")  ssqres=preddf$conc-expdf$conc  return(ssqres)}# parameter fitting using levenberg marquart#initial guess for parametersmyparms=c(k1=0.5,k2=0.5,k3=3,1)cinit=c(A=unname(myparms[4],B=0,C=0))print(cinit)#fittingfitval=nls.lm(par=par!
>  ms,fn=ssq)#summary of fitsummary(fitval)
>
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