PKPDmodels {nlmeODE} | R Documentation |
Library of common PK/PD models.
######################################## ### Pharmacokinetics of Theophylline ### ######################################## data(Theoph) TheophODE <- Theoph TheophODE$Dose[TheophODE$Time!=0] <- 0 TheophODE$Cmt <- rep(1,dim(TheophODE)[1]) OneComp <- list(DiffEq=list( dy1dt = ~ -ka*y1 , dy2dt = ~ ka*y1-ke*y2), ObsEq=list( c1 = ~ 0, c2 = ~ y2/CL*ke), Parms=c("ka","ke","CL"), States=c("y1","y2"), Init=list(0,0)) TheophModel <- nlmeODE(OneComp,TheophODE) ## Not run: Theoph.nlme <- nlme(conc ~ TheophModel(ka,ke,CL,Time,Subject), data = TheophODE, fixed=ka+ke+CL~1, random = pdDiag(ka+CL~1), start=c(ka=0.5,ke=-2.5,CL=-3.2), control=list(returnObject=TRUE,msVerbose=TRUE), verbose=TRUE) plot(augPred(Theoph.nlme,level=0:1)) ## End(Not run) ######################################### ### Pharmacokinetics of Indomethacine ### ######################################### data(Indometh) TwoComp <- list(DiffEq=list( dy1dt = ~ -(k12+k10)*y1+k21*y2 , dy2dt = ~ -k21*y2 + k12*y1), ObsEq=list( c1 = ~ y1, c2 = ~ 0), States=c("y1","y2"), Parms=c("k12","k21","k10","start"), Init=list("start",0)) IndomethModel <- nlmeODE(TwoComp,Indometh) ## Not run: Indometh.nlme <- nlme(conc ~ IndomethModel(k12,k21,k10,start,time,Subject), data = Indometh, fixed=k12+k21+k10+start~1, random = pdDiag(start+k12+k10~1), start=c(k12=-0.05,k21=-0.15,k10=-0.10,start=0.70), control=list(msVerbose=TRUE), verbose=TRUE) plot(augPred(Indometh.nlme,level=0:1)) ## End(Not run) ################################################################# ### Absorption model with estimation of time/rate of infusion ### ################################################################# OneCompAbs <- list(DiffEq=list( dA1dt = ~ -ka*A1, dA2dt = ~ ka*A1 - CL/V1*A2), ObsEq=list( SC= ~0, C = ~ A2/V1), States=c("A1","A2"), Parms=c("ka","CL","V1","F1"), Init=list(0,0)) ID <- rep(seq(1:18),each=11) Time <- rep(seq(0,100,by=10),18) Dose <- c(rep(c(100,0,0,100,0,0,0,0,0,0,0),6),rep(c(100,0,0,0,0,0,0,100,0,0,0),6),rep(c(100,0,0,0,0,0,0,0,0,0,0),6)) Rate <- c(rep(rep(0,11),6),rep(c(5,rep(0,10)),6),rep(rep(0,11),6)) Cmt <- c(rep(1,6*11),rep(c(2,0,0,0,0,0,0,1,0,0,0),6),rep(2,6*11)) Conc <- rep(0,18*11) Data <- as.data.frame(list(ID=ID,Time=Time,Dose=Dose,Rate=Rate,Cmt=Cmt,Conc=Conc)) SimData <- groupedData( Conc ~ Time | ID, data = Data, labels = list( x = "Time", y = "Concentration")) OneCompAbsModel <- nlmeODE(OneCompAbs,SimData) kaSim <- rep(log(rep(0.05,18))+0.3*rnorm(18),each=11) CLSim <- rep(log(rep(0.5,18))+0.2*rnorm(18),each=11) V1Sim <- rep(log(rep(10,18))+0.1*rnorm(18),each=11) F1Sim <- rep(log(0.8),18*11) SimData$Sim <- OneCompAbsModel(kaSim,CLSim,V1Sim,F1Sim,SimData$Time,SimData$ID) SimData$Conc <- SimData$Sim + 0.3*rnorm(dim(SimData)[1]) Data <- groupedData( Conc ~ Time | ID, data = SimData, labels = list( x = "Time", y = "Concentration")) plot(Data,aspect=1/1) #Estimation of model parameters OneCompAbsModel <- nlmeODE(OneCompAbs,Data) ## Not run: fit1 <- nlme(Conc ~ OneCompAbsModel(ka,CL,V1,F1,Time,ID), data = Data, fixed=ka+CL+V1+F1~1, random = pdDiag(ka+CL+V1~1), start=c(ka=log(0.05),CL=log(0.5),V1=log(10.0),F1=log(0.8)), control=list(msVerbose=TRUE,pnlsTol=1), verbose=TRUE) plot(augPred(fit1,level=0:1,length.out=300),aspect=1/1) ## End(Not run) #Estimation of rate of infusion Data$Rate[Data$Rate==5] <- -1 OneCompAbs <- list(DiffEq=list( dA1dt = ~ -ka*A1, dA2dt = ~ ka*A1 - CL/V1*A2), ObsEq=list( SC= ~0, C = ~ A2/V1), States=c("A1","A2"), Parms=c("ka","CL","V1","F1","Rate"), Init=list(0,0)) OneCompAbsModel <- nlmeODE(OneCompAbs,Data) ## Not run: fit2 <- nlme(Conc ~ OneCompAbsModel(ka,CL,V1,F1,Rate,Time,ID), data = Data, fixed=ka+CL+V1+F1+Rate~1, random = pdDiag(ka+CL+V1~1), start=c(ka=log(0.05),CL=log(0.5),V1=log(10.0),F1=log(0.8),Rate=log(5)), control=list(msVerbose=TRUE,pnlsTol=1), verbose=TRUE) plot(augPred(fit2,level=0:1,length.out=300),aspect=1/1) ## End(Not run) #Estimation of length of infusion Data$Rate[Data$Rate==-1] <- -2 OneCompAbs <- list(DiffEq=list( dA1dt = ~ -ka*A1, dA2dt = ~ ka*A1 - CL/V1*A2), ObsEq=list( SC= ~0, C = ~ A2/V1), States=c("A1","A2"), Parms=c("ka","CL","V1","F1","Tcrit"), Init=list(0,0)) OneCompAbsModel <- nlmeODE(OneCompAbs,Data) ## Not run: fit3 <- nlme(Conc ~ OneCompAbsModel(ka,CL,V1,F1,Tcrit,Time,ID), data = Data, fixed=ka+CL+V1+F1+Tcrit~1, random = pdDiag(ka+CL+V1~1), start=c(ka=log(0.05),CL=log(0.5),V1=log(10.0),F1=log(0.8),Tcrit=log(20)), control=list(msVerbose=TRUE,pnlsTol=1), verbose=TRUE) plot(augPred(fit3,level=0:1,length.out=300),aspect=1/1) ## End(Not run) ############################################################ ### Simulation and simultaneous estimation of PK/PD data ### ############################################################ PoolModel <- list( DiffEq=list( dy1dt = ~ -ke*y1, dy2dt = ~ krel * (1-Emax*(y1/Vd)**gamma/(EC50**gamma+(y1/Vd)**gamma)) * y3 - kout * y2, dy3dt = ~ Kin - krel * (1-Emax*(y1/Vd)**gamma/(EC50**gamma+(y1/Vd)**gamma))*y3), ObsEq=list( PK = ~ y1/Vd, PD = ~ y2, Pool = ~ 0), States=c("y1","y2","y3"), Parms=c("ke","Vd","Kin","kout","krel","Emax","EC50","gamma"), Init=list(0,"Kin/kout","Kin/krel")) ID <- rep(seq(1:12),each=2*12) Time <- rep(rep(c(0,0.25,0.5,0.75,1,2,4,6,8,10,12,24),each=2),12) Dose <- rep(c(100,rep(0,23)),12) Cmt <- rep(rep(c(1,2),12),12) Type <- rep(rep(c(1,2),12),12) Conc <- rep(0,2*12*12) Data <- as.data.frame(list(ID=ID,Time=Time,Dose=Dose,Cmt=Cmt,Type=Type,Conc=Conc)) SimData <- groupedData( Conc ~ Time | ID/Type, data = Data, labels = list( x = "Time", y = "Concentration")) PKPDpoolModel <- nlmeODE(PoolModel,SimData) keSim <- rep(log(rep(0.05,12))+0.1*rnorm(12),each=2*12) VdSim <- rep(log(rep(10,12))+0.01*rnorm(12),each=2*12) EC50Sim <- rep(log(rep(5,12))+0.1*rnorm(12),each=2*12) KinSim <- rep(log(5),2*12*12) koutSim <- rep(log(0.5),2*12*12) krelSim <- rep(log(2),2*12*12) EmaxSim <- rep(log(1),2*12*12) gammaSim <- rep(log(3),2*12*12) SimData$Sim <- PKPDpoolModel(keSim,VdSim,KinSim,koutSim,krelSim,EmaxSim,EC50Sim,gammaSim,SimData$Time,SimData$ID,SimData$Type) SimData$Conc[SimData$Type==1] <- SimData$Sim[SimData$Type==1]*(1 + 0.1*rnorm(length(SimData[SimData$Type==1,1]))) SimData$Conc[SimData$Type==2] <- SimData$Sim[SimData$Type==2]*(1 + 0.05*rnorm(length(SimData[SimData$Type==2,1]))) Data <- groupedData( Conc ~ Time | ID/Type, data = SimData, labels = list( x = "Time", y = "Concentration")) plot(Data,display=1,aspect=1/1) #Fixed parameters Data$Emax <- rep(log(1),dim(Data)[1]) #Estimation of model parameters PKPDpoolModel <- nlmeODE(PoolModel,Data) ## Not run: PKPDpool.nlme <- nlme(log(Conc) ~ log(PKPDpoolModel(ke,Vd,Kin,kout,krel,Emax,EC50,gamma,Time,ID,Type)), data = Data, fixed=ke+Vd+Kin+kout+krel+EC50+gamma~1, random = pdDiag(ke+Vd+EC50~1), groups=~ID, weights=varIdent(form=~1|Type), start=c(ke=log(0.05),Vd=log(10),Kin=log(5),kout=log(0.5),krel=log(2),EC50=log(5),gamma=log(3)), control=list(msVerbose=TRUE,msMaxIter=20,pnlsMaxIter=20,pnlsTol=1), verbose=TRUE) PKPDpool.nlme exp(fixef(PKPDpool.nlme)) #Plot results ni <- 100 TimeSim <- seq(from=0,to=24,length=ni) TimeSim <- rep(rep(TimeSim,each=2),12) SubjectSim <- rep(1:12,each=2*ni) TypeSim <- rep(rep(c(1,2),ni),12) IndCoef <- coef(PKPDpool.nlme) IpredSim <- PKPDpoolModel( rep(IndCoef[,1],each=2*ni), rep(IndCoef[,2],each=2*ni), rep(IndCoef[,3],each=2*ni), rep(IndCoef[,4],each=2*ni), rep(IndCoef[,5],each=2*ni), rep(rep(log(1),12),each=2*ni), rep(IndCoef[,6],each=2*ni), rep(IndCoef[,7],each=2*ni), TimeSim,SubjectSim,TypeSim) PopCoef <- fixef(PKPDpool.nlme) PredSim <- PKPDpoolModel( rep(rep(PopCoef[1],12),each=2*ni), rep(rep(PopCoef[2],12),each=2*ni), rep(rep(PopCoef[3],12),each=2*ni), rep(rep(PopCoef[4],12),each=2*ni), rep(rep(PopCoef[5],12),each=2*ni), rep(rep(log(1),12),each=2*ni), rep(rep(PopCoef[6],12),each=2*ni), rep(rep(PopCoef[7],12),each=2*ni), TimeSim,SubjectSim,TypeSim) plotPool <- as.data.frame(rbind(cbind(TimeSim,SubjectSim,PredSim,TypeSim,rep("Pred",2400)), cbind(TimeSim,SubjectSim,IpredSim,TypeSim,rep("Ipred",2400)), cbind(Data$Time,Data$ID,Data$Conc,Data$Type,rep("Obs",288)) )) names(plotPool) <- c("Time","Subject","Conc","Type","Flag") plotPool$Subject <- as.factor(as.numeric(as.character(plotPool$Subject))) plotPool$Type <- as.factor(plotPool$Type) plotPool$Flag <- as.factor(plotPool$Flag) plotPool$Conc <- as.numeric(as.character(plotPool$Conc)) plotPool$Time <- as.numeric(as.character(plotPool$Time)) plotPoolPK <- subset(plotPool,Type==1) plotPoolPD <- subset(plotPool,Type==2) get(getOption("device"))(record=TRUE,width=9,height=9) xyplot (Conc~Time | Subject, data=plotPoolPK, layout=c(4,3), aspect=1/1, groups=Flag, xlab="Time since drug administration (hr)", ylab="PK concentration (ng/mL)", key=list(x=0,y=1,corner=c(0,0),transparent=TRUE, text = list(c("Population", "Individual","Observed")), lines = list(type=c("l","l","p"), pch=16, col=c(1,"red",1), lty=c(2,1,1)),columns=3), strip = function(...) strip.default(..., strip.names=c(FALSE,TRUE), style=1), panel = function(x, y, groups,...) { panel.grid(h=3,v=3,col="lightgray",lwd=0.7,...) panel.superpose.2(x,y,groups,type=c("l","p","l"), col=c("red",1,1), lty=c(1,1,2),pch=16, lwd=1.4,...)}, par.strip.text=list(cex=1.0)) xyplot (Conc~Time | Subject, data=plotPoolPD, layout=c(4,3), aspect=1/1, groups=Flag, xlab="Time since drug administration (hr)", ylab="PD concentration (ng/mL)", key=list(x=0,y=1,corner=c(0,0),transparent=TRUE, text = list(c("Population", "Individual","Observed")), lines = list(type=c("l","l","p"), pch=16, col=c(1,"red",1), lty=c(2,1,1)),columns=3), strip = function(...) strip.default(..., strip.names=c(FALSE,TRUE), style=1), panel = function(x, y, groups,...) { panel.grid(h=3,v=3,col="lightgray",lwd=0.7,...) panel.superpose.2(x,y,groups,type=c("l","p","l"), col=c("red",1,1), lty=c(1,1,2),pch=16, lwd=1.4,...)}, par.strip.text=list(cex=1.0)) ## End(Not run) ############################################################ ### Minimal Model of Glucose and Insulin ### ############################################################ MMmodel <- list( DiffEq=list( dgdt = ~ Sg*Gb - (Sg+x)*g, dxdt = ~ -p2*(x-Si*(i-Ib)), didt = ~ -n*(i-Ib)+gamma*(g-h)*t), ObsEq=list( gc= ~ g, xc= ~ 0, ic= ~ i), States=c("g","x","i"), Parms=c("Sg","p2","Si","n","gamma","h","Gb","Ib","G0","I0"), Init=list("G0",0,"I0") ) id <- rep(seq(1:12),each=2*29) time <- rep(rep(c(0,2,3,4,5,6,8,10,12,14,16,19,22,24,25,27,30,35,40,50,60,70,80,90,100,120,140,160,180),each=2),12) type <- rep(rep(c(1,2),29),12) conc <- rep(0,2*12*29) data <-as.data.frame(list(id=id,time=time,type=type,conc=conc)) MMData <- groupedData(conc~time|id/type,data=data, labels=list(x="Time",y="Concentration")) Sgsim <-rep(rep(log(0.025),12)+0.2*rnorm(12),each=2*29) p2sim<-rep(rep(log(0.007),12)+0*rnorm(12),each=2*29) Sisim<-rep(rep(log(0.001),12)+0.3*rnorm(12),each=2*29) nsim<-rep(rep(log(0.15),12)+0*rnorm(12),each=2*29) gammasim<-rep(rep(log(0.001),12)+0*rnorm(12),each=2*29) hsim<-rep(rep(log(65),12)+0*rnorm(12),each=2*29) Gbsim<-rep(rep(log(100),12)+0*rnorm(12),each=2*29) Ibsim<-rep(rep(log(10),12)+0*rnorm(12),each=2*29) G0sim<-rep(rep(log(250),12)+0.2*rnorm(12),each=2*29) I0sim<-rep(rep(log(120),12)+0*rnorm(12),each=2*29) MinModel <-nlmeODE(MMmodel,MMData) data$sim<-MinModel(Sgsim,p2sim,Sisim,nsim,gammasim,hsim,Gbsim,Ibsim,G0sim,I0sim,data$time,data$id,data$type) data$conc[data$type==1] <- data$sim[data$type==1]*(1+0.2*rnorm(length(data[data$type==1,1]))) data$conc[data$type==2] <- data$sim[data$type==2]*(1+0.2*rnorm(length(data[data$type==2,1]))) data$Gb <- Gbsim data$Ib <- Ibsim MMData <- groupedData( conc ~ time | id/type, data = data, labels = list( x = "Time", y = "Concentration")) plot(MMData,display=1,aspect=1/1) MinModel <- nlmeODE(MMmodel,MMData) ## Not run: MM.nlme <-nlme(conc~MinModel(Sg,p2,Si,n,gamma,h,Gb,Ib,G0,I0,time,id,type), data=MMData, fixed=Sg+p2+Si+n+gamma+h+G0+I0~1, groups=~id, weights=varExp(0.2,form=~fitted(.)|type), random=pdDiag(Sg+Si+G0~1), start=c(Sg=log(0.025),p2=log(0.007),Si=log(0.001),n=log(0.15),gamma=log(0.001),h=log(65),G0=log(250),I0=log(120)), control=list(returnObject=TRUE,msVerbose=TRUE,msMaxIter=20,pnlsMaxIter=20,pnlsTol=1), verbose=TRUE) MM.nlme exp(fixef(MM.nlme)) #Plot results ni <- 100 TimeSim <- seq(from=0,to=180,length=ni) TimeSim <- rep(rep(TimeSim,each=2),12) SubjectSim <- rep(1:12,each=2*ni) TypeSim <- rep(rep(c(1,2),ni),12) IndCoef <- coef(MM.nlme) IpredSim <- MinModel( rep(IndCoef[,1],each=2*ni), rep(IndCoef[,2],each=2*ni), rep(IndCoef[,3],each=2*ni), rep(IndCoef[,4],each=2*ni), rep(IndCoef[,5],each=2*ni), rep(IndCoef[,6],each=2*ni), rep(rep(unique(MMData$Gb),12),each=2*ni), rep(rep(unique(MMData$Ib),12),each=2*ni), rep(IndCoef[,7],each=2*ni), rep(IndCoef[,8],each=2*ni), TimeSim,SubjectSim,TypeSim) PopCoef <- fixef(MM.nlme) PredSim <- MinModel( rep(rep(PopCoef[1],12),each=2*ni), rep(rep(PopCoef[2],12),each=2*ni), rep(rep(PopCoef[3],12),each=2*ni), rep(rep(PopCoef[4],12),each=2*ni), rep(rep(PopCoef[5],12),each=2*ni), rep(rep(PopCoef[6],12),each=2*ni), rep(rep(unique(MMData$Gb),12),each=2*ni), rep(rep(unique(MMData$Ib),12),each=2*ni), rep(rep(PopCoef[7],12),each=2*ni), rep(rep(PopCoef[8],12),each=2*ni), TimeSim,SubjectSim,TypeSim) plotMM <- as.data.frame(rbind(cbind(TimeSim,SubjectSim,PredSim,TypeSim,rep("Pred",2400)), cbind(TimeSim,SubjectSim,IpredSim,TypeSim,rep("Ipred",2400)), cbind(MMData$time,MMData$id,MMData$conc,MMData$type,rep("Obs",696)) )) names(plotMM) <- c("Time","Subject","Conc","Type","Flag") plotMM$Subject <- as.factor(as.numeric(as.character(plotMM$Subject))) plotMM$Type <- as.factor(plotMM$Type) plotMM$Flag <- as.factor(plotMM$Flag) plotMM$Conc <- as.numeric(as.character(plotMM$Conc)) plotMM$Time <- as.numeric(as.character(plotMM$Time)) plotMMG <- subset(plotMM,Type==1) plotMMI <- subset(plotMM,Type==2) get(getOption("device"))(record=TRUE,width=9,height=9) xyplot (Conc~Time | Subject, data=plotMMG, layout=c(4,3), aspect=1/1, groups=Flag, xlab="Time (hr)", ylab="Glucose concentration", key=list(x=0,y=1,corner=c(0,0),transparent=TRUE, text = list(c("Population", "Individual","Observed")), lines = list(type=c("l","l","p"), lwd=3, pch=16, col=c(1,"red",1), lty=c(1,1,1)),columns=3), strip = function(...) strip.default(..., strip.names=c(FALSE,TRUE), style=1), panel = function(x, y, groups,...) { panel.abline(h=c(100,200,300,400),col="lightgray",lwd=0.7,...) panel.abline(v=c(0,50,100,150,200),col="lightgray",lwd=0.7,...) panel.superpose.2(x,y,groups,type=c("l","p","l"), col=c("red",1,1), lty=c(1,1,1),pch=16,cex=.5, lwd=2,...)}, par.strip.text=list(cex=1.0)) xyplot (Conc~Time | Subject, data=plotMMI, layout=c(4,3), aspect=1/1, groups=Flag, xlab="Time (hr)", ylab="Insulin concentration", key=list(x=0,y=1,corner=c(0,0),transparent=TRUE, text = list(c("Population", "Individual","Observed")), lines = list(type=c("l","l","p"), lwd=3, pch=16, col=c(1,"red",1), lty=c(1,1,1)),columns=3), strip = function(...) strip.default(..., strip.names=c(FALSE,TRUE), style=1), panel = function(x, y, groups,...) { panel.abline(h=c(100,200,300,400),col="lightgray",lwd=0.7,...) panel.abline(v=c(0,50,100,150,200),col="lightgray",lwd=0.7,...) panel.superpose.2(x,y,groups,type=c("l","p","l"), col=c("red",1,1), lty=c(1,1,1),pch=16,cex=.5, lwd=2,...)}, par.strip.text=list(cex=1.0)) ## End(Not run) ############################################################################ ### Minimal Model of Glucose using observed insulin as forcing function ### ############################################################################ idata <- data$conc[data$type==2] data <- data[data$type==1,] data$i <- idata MMmodel <- list( DiffEq=list( dgdt = ~ Sg*Gb - (Sg+x)*g, dxdt = ~ -p2*(x-Si*(Insulin(t,id)-Ib))), ObsEq=list( gc= ~ g, xc= ~ 0), States=c("g","x"), Parms=c("Sg","p2","Si","Gb","Ib","id","G0"), Init=list("G0",0) ) Insulin <- function(t,subject){ subject <- as.integer(log(subject)) dT <- MMData$time[MMData$id==subject & MMData$time>t][1]-rev(MMData$time[MMData$id==subject & MMData$time<=t])[1] dInsulin <- MMData$i[MMData$id==subject & MMData$time>t][1]-rev(MMData$i[MMData$id==subject & MMData$time<=t])[1] if(t>=max(MMData$time[MMData$id==subject])){ conc <- rev(MMData$i[MMData$id==subject & MMData$time<=t])[1] }else{ conc <- rev(MMData$i[MMData$id==subject & MMData$time<=t])[1] + dInsulin/dT*(t-rev(MMData$time[MMData$id==subject & MMData$time<=t])[1]) } names(conc) <- NULL return(conc) } MMData <- groupedData( conc ~ time | id, data = data, labels = list( x = "Time", y = "Concentration")) plot(MMData,aspect=1/1) MinModel <- nlmeODE(MMmodel,MMData) ## Not run: MMglucose.nlme <-nlme(log(conc)~log(MinModel(Sg,p2,Si,Gb,Ib,id,G0,time,id)), data=MMData, fixed=Sg+p2+Si+G0~1, random=pdDiag(Sg+Si+G0~1), start=c(Sg=log(0.03),p2=log(0.01),Si=log(0.001),G0=log(250)), control=list(returnObject=TRUE,msVerbose=TRUE,msMaxIter=20,pnlsMaxIter=20,pnlsTol=1), verbose=TRUE) MMglucose.nlme exp(fixef(MMglucose.nlme)) #Plot results ni <- 100 TimeSim <- seq(from=0,to=180,length=ni) TimeSim <- rep(rep(TimeSim,each=2),12) SubjectSim <- rep(1:12,each=2*ni) IndCoef <- coef(MMglucose.nlme) IpredSim <- MinModel( rep(IndCoef[,1],each=2*ni), rep(IndCoef[,2],each=2*ni), rep(IndCoef[,3],each=2*ni), rep(rep(unique(MMData$Gb),12),each=2*ni), rep(rep(unique(MMData$Ib),12),each=2*ni), SubjectSim, rep(IndCoef[,4],each=2*ni), TimeSim,SubjectSim) PopCoef <- fixef(MMglucose.nlme) PredSim <- MinModel( rep(rep(PopCoef[1],12),each=2*ni), rep(rep(PopCoef[2],12),each=2*ni), rep(rep(PopCoef[3],12),each=2*ni), rep(rep(unique(MMData$Gb),12),each=2*ni), rep(rep(unique(MMData$Ib),12),each=2*ni), SubjectSim, rep(rep(PopCoef[4],12),each=2*ni), TimeSim,SubjectSim) plotMM <- as.data.frame(rbind(cbind(TimeSim,SubjectSim,PredSim,rep("Pred",2400)), cbind(TimeSim,SubjectSim,IpredSim,rep("Ipred",2400)), cbind(MMData$time,MMData$id,MMData$conc,rep("Obs",348)) )) names(plotMM) <- c("Time","Subject","Conc","Flag") plotMM$Subject <- as.factor(as.numeric(as.character(plotMM$Subject))) plotMM$Flag <- as.factor(plotMM$Flag) plotMM$Conc <- as.numeric(as.character(plotMM$Conc)) plotMM$Time <- as.numeric(as.character(plotMM$Time)) xyplot (Conc~Time | Subject, data=plotMMG, layout=c(4,3), aspect=1/1, groups=Flag, xlab="Time (hr)", ylab="Glucose concentration", key=list(x=0,y=1,corner=c(0,0),transparent=TRUE, text = list(c("Population", "Individual","Observed")), lines = list(type=c("l","l","p"), pch=16, lwd=3, col=c(1,"red",1), lty=c(1,1,1)),columns=3), strip = function(...) strip.default(..., strip.names=c(FALSE,TRUE), style=1), panel = function(x, y, groups,...) { panel.grid(h=3,v=3,col="lightgray",lwd=0.7,...) panel.superpose.2(x,y,groups,type=c("l","p","l"), col=c("red",1,1), lty=c(1,1,1),pch=16, cex=.5, lwd=3,...)}, par.strip.text=list(cex=1.0)) ## End(Not run)