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