dOFV.vs.id: Change in Objective function value vs. removal of...

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Change in Objective function value vs. removal of individuals.

Description

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.

Author(s)

Andrew C. Hooker

See Also

Other specific functions: absval.cwres.vs.cov.bw(), absval.cwres.vs.pred.by.cov(), absval.cwres.vs.pred(), absval.iwres.cwres.vs.ipred.pred(), absval.iwres.vs.cov.bw(), absval.iwres.vs.idv(), absval.iwres.vs.ipred.by.cov(), absval.iwres.vs.ipred(), absval.iwres.vs.pred(), absval.wres.vs.cov.bw(), absval.wres.vs.idv(), absval.wres.vs.pred.by.cov(), absval.wres.vs.pred(), 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.bw(), cwres.vs.idv(), cwres.vs.pred.bw(), cwres.vs.pred(), 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.by.cov(), dv.vs.ipred.by.idv(), dv.vs.ipred(), dv.vs.pred.by.cov(), dv.vs.pred.by.idv(), dv.vs.pred.ipred(), dv.vs.pred(), gof(), ind.plots.cwres.hist(), ind.plots.cwres.qq(), ind.plots(), 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.bw(), wres.vs.idv(), wres.vs.pred.bw(), wres.vs.pred(), xpose.VPC.both(), xpose.VPC.categorical(), xpose.VPC(), xpose4-package

Examples

## Not run: 
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)

## End(Not run)

xpose4 documentation built on May 31, 2022, 5:07 p.m.