How to feed data into ode while doing optimisation - r

Question

I'm new to R. I found very useful code, which I've tried to use for my purposes. however, I get an error:
Error in func(time, state, parms, ...) : object 'k4' not found and Error in func(time, state, parms, ...) : object 'E' not found
I don't know where the problem is as I can see all parameters and data.frame is correct as well.
Thank you everyone for taking time to look at this. I've tried to reduce the number of parameters to3 (k10, k11,k12), and using estimated values for the remaining (embeded values in the code). However, I still get an error message, the E value from data.frame is not passed into rxnrate function and as result ode can't use it. I've tried to use events and forcing functions but it doesn't seem to work. Thank you for spotting P4, it was a typo, should be P, I've corrected already.
Editors note: This was crossposted to Rhelp and that message included the source of this code as a stackoverflow question "r-parameter and initial conditions fitting ODE models with nls.lm."
#set working directory
setwd("~/R/wkspace")
#load libraries
library(ggplot2)
library(reshape2)
library(deSolve)
library(minpack.lm)
time=c(22,23,24,46,47,48)
cE=c(15.92,24.01,25.29,15.92,24.01,25.29)
cP=c(0.3,0.14,0.29,0.3,0.14,0.29)
cL=c(6.13,3.91,38.4,6.13,3.91,38.4)
df<-data.frame(time,cE,cP,cL)
df
names(df)=c("time","cE","cP","cL")
#rate function
rxnrate=function(t,c,parms){
#rate constant passed through a list called
k1=parms$k1
k2=parms$k2
k3=parms$k3
k4=parms$k4
k5=parms$k5
k6=parms$k6
k7=parms$k7
k8=parms$k8
k9=parms$k9
k10=parms$k10
#c is the concentration of species
#derivatives dc/dt are computed below
r=rep(0,length(c))
r[1]=(k1+(k2*E^k10)/(k3^k10+E^k10))/(1+P/k6)-k4* ((1+k5*P)/(1+k7*E))*c["pLH"]; #dRP_LH/dt
r[2]=(1/k8)*k4*((1+k5*P)/(1+k7*E))*c["p"]-k9*c["L"] #dL/dt
return(list(r))
}
ssq=function(myparms){
#initial concentration
cinit=c(pLH=unname(myparms[11]),LH=unname(myparms[12]))
print(cinit)
#time points for which conc is reported
#include the points where data is available
t=c(seq(0,46,2),df$time)
t=sort(unique(t))
#parameters from the parameters estimation
k1=myparms[1]
k2=myparms[2]
k3=myparms[3]
k4=myparms[4]
k5=myparms[5]
k6=myparms[6]
k7=myparms[7]
k8=myparms[8]
k9=myparms[9]
k10=myparms[10]
#solve ODE for a given set of parameters
out=ode(y=cinit,times=t,func=rxnrate,parms=list(k1=k1,k2=k2,k3=k3,k4=k4,k5=k5,k6=k6,k7=k7,k8=k8,k9=k9,k10=k10,E=cE,P=cP))
#Filter data that contains time points
outdf=data.frame(out)
outdf=outdf[outdf$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 parameters
myparms=c(k1=500, k2=4500, k3=200,k4=2.42, k5=0.26,k6=12.2,k7=0.004,k8=55,k9=24,k10=8,pLH=14.5,LH=3.55)
#fitting
fitval=nls.lm(par=myparms,fn=ssq)
#summary of fit
summary(fitval)
#estimated parameter
parest=as.list(coef(fitval))