Change in Objective function value vs. removal of individuals.

A plot showing the most and least influential individuals in determining a drop in OFV between two models.

Usage

dOFV.vs.id(
  xpdb1,
  xpdb2,
  sig.drop = -3.84,
  decrease.label.number = 3,
  increase.label.number = 3,
  id.lab.cex = 0.6,
  id.lab.pos = 2,
  type = "o",
  xlb = "Number of subjects removed",
  ylb = expression(paste(Delta, "OFV")),
  main = "Default",
  sig.line.col = "red",
  sig.line.lty = "dotted",
  tot.line.col = "grey",
  tot.line.lty = "dashed",
  key = list(columns = 1, lines = list(pch = c(super.sym$pch[1:2], NA, NA), type =
    list("o", "o", "l", "l"), col = c(super.sym$col[1:2], sig.line.col, tot.line.col),
    lty = c(super.sym$lty[1:2], sig.line.lty, tot.line.lty)), text =
    list(c(expression(paste(Delta, OFV[i] < 0)), expression(paste(Delta, OFV[i] > 0)),
    expression(paste("Significant  ", Delta, OFV)), expression(paste("Total  ", Delta,
    OFV)))), corner = c(0.95, 0.5), border = T),
  ...
)

Arguments

xpdb1

Xpose data object for first NONMEM run ("new" run)

xpdb2

Xpose data object for Second NONMEM run ("reference" run)

sig.drop

What is a significant drop of OFV?

decrease.label.number

How many points should bw labeled with ID values for those IDs with a drop in iOFV?

increase.label.number

How many points should bw labeled with ID values for those IDs with an increase in iOFV?

id.lab.cex

Size of ID labels.

id.lab.pos

ID label position.

type

Type of lines.

xlb

X-axis label.

ylb

Y-axis label.

main

Title of plot.

sig.line.col

Significant OFV drop line color.

sig.line.lty

Significant OFV drop line type.

tot.line.col

Total OFV drop line color.

tot.line.lty

Total OFV drop line type.

key

Legend for plot.

...

Additional arguments to function.

See also

Other specific functions: absval.cwres.vs.cov.bw(), absval.cwres.vs.pred(), absval.cwres.vs.pred.by.cov(), absval.iwres.cwres.vs.ipred.pred(), absval.iwres.vs.cov.bw(), absval.iwres.vs.idv(), absval.iwres.vs.ipred(), absval.iwres.vs.ipred.by.cov(), absval.iwres.vs.pred(), absval.wres.vs.cov.bw(), absval.wres.vs.idv(), absval.wres.vs.pred(), absval.wres.vs.pred.by.cov(), absval_delta_vs_cov_model_comp, addit.gof(), autocorr.cwres(), autocorr.iwres(), autocorr.wres(), basic.gof(), basic.model.comp(), cat.dv.vs.idv.sb(), cat.pc(), cov.splom(), cwres.dist.hist(), cwres.dist.qq(), cwres.vs.cov(), cwres.vs.idv(), cwres.vs.idv.bw(), cwres.vs.pred(), cwres.vs.pred.bw(), cwres.wres.vs.idv(), cwres.wres.vs.pred(), dOFV.vs.cov(), dOFV1.vs.dOFV2(), data.checkout(), dv.preds.vs.idv(), dv.vs.idv(), dv.vs.ipred(), dv.vs.ipred.by.cov(), dv.vs.ipred.by.idv(), dv.vs.pred(), dv.vs.pred.by.cov(), dv.vs.pred.by.idv(), dv.vs.pred.ipred(), gof(), ind.plots(), ind.plots.cwres.hist(), ind.plots.cwres.qq(), ipred.vs.idv(), iwres.dist.hist(), iwres.dist.qq(), iwres.vs.idv(), kaplan.plot(), par_cov_hist, par_cov_qq, parm.vs.cov(), parm.vs.parm(), pred.vs.idv(), ranpar.vs.cov(), runsum(), wres.dist.hist(), wres.dist.qq(), wres.vs.idv(), wres.vs.idv.bw(), wres.vs.pred(), wres.vs.pred.bw(), xpose.VPC(), xpose.VPC.both(), xpose.VPC.categorical(), xpose4-package

Author

Andrew C. Hooker

Examples

if (FALSE) {
library(xpose4)

## first make sure that the iofv values are read into xpose
cur.dir <- getwd()
setwd(paste(cur.dir,"/LAG_TIME",sep=""))
xpdb1 <- xpose.data(1)
setwd(paste(cur.dir,"/TRANSIT_MODEL",sep=""))
xpdb2 <- xpose.data(1)
setwd(cur.dir)

## then make the plot
dOFV.vs.id(xpdb1,xpdb2)
}