Hi, I have one more question. I am running a VPC and I can’t find the plot of DV vs VAR over all individuals with the shaded areas which are the liimits. Can you please help me? Thanks, Georgia
Dear Georgia Can you share the project? Best Serge
The project I am working on is about a POPPK model of cyclophosphamide in populations with Acute Kidney Disease. So, now I want to check the VPC, but as I am used to working with nonmem, I am a little bit lost with Phoenix. Next stage of my project is to add the metabolite model. Georgia
Georgia, we don’t natively support shading in Phoenix plots yet so I think the equivalent plots would be; 
however you can also use the work sheet produced as a source to replot this in R if you want; #PCDATA ← read.csv (“PredCheck_ObsQ_SimQCI.csv”) let me check if I can post this project Simon 
Well, I don’t know why, but my POP PredCheck look like this, while my Individual Pred check look fine. I really can’t figure out what is wrong and my POP PredCheck look like that. Thanks, Georgia [file name=Individual_PredCheck.png size=44831]Certara | Drug Development Solutions 
Maybe I got the previous Overall PredCheck because I didn’t use Binning? If I check Binning, then I get this. Thanks, Georgia 
of course binning is very important. Phoenix has optimal built in binning using K-means algorithm and more options are upcoming. I wrote additional scripts that help visualize the graphs in R lattice or GGPLOT with automatic stratification: ################################################# code starts # The user is responsible to edit the code to make sure it works for his model # make sure you have all required pacakges #Samer Mouksassi attach(PCDATA) #WNL_IN Scenario Strat IVAR DV0 DV ObsName QI QE# #PCDATA ← read.csv (“PredCheck_ObsQ_SimQCI.csv”) Stratname ← “Sex” StratLevels ← c(“Women”,“Men”) Transparencysetting ← 0.2 require(lattice) require(reshape2) require(ggplot2) ########################################################## PCDATA$PK ← with(PCDATA,ifelse( is.na(DV),DV0,DV)) PCDATA<- PCDATA[, c(“Strat”,“IVAR”,“PK”,“QI”,“QE”)] PCDATAm ← melt(PCDATA,id=c(“Strat”,“IVAR”,“QE”,“QI”)) PCDATAm ← dcast ( PCDATAm , IVAR +QI +Strat ~ QE +variable) #PCDATAm $Strat ← ifelse ( PCDATAm $Strat==1,“Men”,“Women”) # this line above if you want to change the labels ########################################################## png(file = “vpcplotggplot.png”, bg = “transparent”) ggplot(PCDATAm ,aes(IVAR,--_PK,ymin=05%_PK,ymax=95%_PK,fill=QI,col=QI))+ geom_ribbon(alpha=0.1,col=NA ) + scale_fill_manual(name=“Observerd (lines)\nPrediction Intervals (ribbons)”, breaks=c(“05%”, “50%”, “95%”), values=c(“red”,“blue”,“red”))+ geom_line() + facet_grid(~Strat, labeller = label_value,scales=“free_x”) + scale_colour_manual(name=“Observerd (lines)\nPrediction Intervals (ribbons)”, breaks=c(“05%”, “50%”, “95%”), values=c(“red”,“blue”,“red”))+ ylab(“Simulated and Observed Quantiles of\n Concentrations (pg/mL)”)+ xlab(“Time After Dose (h)”)+ theme(legend.position=“bottom”) dev.off() ########## the same but with lattice panel.bands ← function(x, y, upper, lower, fill, col, subscripts, …, font, fontface) { upper ← upper[subscripts] lower ← lower[subscripts] panel.polygon(c(x, rev(x)), c(upper, rev(lower)), col = fill, border = FALSE, …) } png(file = “vpcplotlattice.png”, bg = “transparent”) xyplot( --_PK ~IVAR |Strat,data=PCDATAm, scales=list(x=list(relation=“free”)), ylim=c(min (PCDATAm[,4:7] ,na.rm=T), max (PCDATAm[,4:7],na.rm=T)), ylab = “Simulated and Observed Quantiles of\n Concentrations (pg/mL)”, xlab = “Time After Dose (h)”, group=PCDATAm$QI,lower= PCDATAm[,5] ,upper = PCDATAm[,7] , panel= function(x,y,subscript=T,group,ylow=ylow,yup=yup,…){ panel.superpose(x, y, panel.groups = ‘panel.bands’,fill=c(“red”,“blue”,“red”),alpha=0.2, …) panel.superpose(x,y,type=“l”,col=c(“red”,“blue”,“red”),…) } , key=list(space=“bottom”,columns=2, text =list(lab=c(“SIM 95% PI”,“SIM Median”,“SIM 05% PI”,“OBS 95% PI”,“OBS Median”,“OBS 05% PI”)), rectangles=list(col=c(“red”,“blue”,“red”,“transparent”,“transparent”,“transparent”),alpha=0.2,border=“transparent”) ,lines=list(lty=c(0,0,0,1,1,1),lwd=1,col=c(“transparent”,“transparent”,“transparent”,“red”,“blue”,“red”)) ) ) dev.off() = list(cex = 1.2) ,var.name=Stratname ,factor.levels=StratLevels) ) ,more=F) dev.off()