In qPharmetra/qpToolkit: qPharmetra R Tools

Parameter Estimates

#run is set in the parent document
# parameter estimate table
outPar = nm.params.table(run,path = nm.path)
outPar.tab = if(sum(isNumeric(outPar$SE))!=0){
  (outPar[isNumeric(outPar$SE), ])
  } else (outPar[outPar$Estimate!=0,])
kable(outPar.tab,row.names=F)

load NONMEM output tables

run.out = get.xpose.tables(run=run,path=nm.path)

# make a plot data set
myPlotData = run.out[order(run.out$ID,run.out$TIME), ]

CWRES vs elapsed time and population prediction

xyplot(CWRES ~ value | paste(variable)
       , data = melt(myPlotData[myPlotData$EVID==0, ], measure.vars = c("TIME","PRED"))
       , groups = ID
       , panel = function(x,y,...)
       {
         panel.xyplot(x,y,... , type = 'l',col = gray[5])
         panel.loess(x,y,..., col = blue[7], lwd=2)
         panel.abline(h=c(-2,0,2),lty = 2, col = gray[7])
         panel.xyplot(x,y,... , col = 1, type = 'p', pch = "+", cex = 0.5)
       }
       , scales = list(x = list(relation = "free"))
       , aspect = 1
       , layout = c(2,1)
)  

DV vs PRED or IPRED

xyplot(CONC ~ value | paste(variable)
       , data = melt(myPlotData[myPlotData$EVID==0, ], measure.vars = c("IPRED","PRED"))
       , groups = ID
       , panel = panel.residual
       , scales = list(log = 10)
       , xscale.components = xscale.components.log10.3
       , yscale.components = yscale.components.log10.3
       , aspect = 1
) 

QQ Plot of CWRES

ggplot(myPlotData[myPlotData$EVID==0, ],aes(sample=CWRES))+stat_qq()+geom_abline(slope=1)+coord_fixed()

qPharmetra/qpToolkit documentation built on May 24, 2023, 8:52 a.m.