xpose.VPC.both: Xpose Visual Predictive Check (VPC) for both continuous and...

In UUPharmacometrics/xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Xpose Visual Predictive Check (VPC) for both continuous and Limit of Quantification data.

Description

Xpose Visual Predictive Check (VPC) for both continuous and Below or Above Limit of Quantification (BLQ or ALQ) data.

Usage

xpose.VPC.both(
  vpc.info = "vpc_results.csv",
  vpctab = dir(pattern = "^vpctab")[1],
  object = NULL,
  subset = NULL,
  main = "Default",
  main.sub = NULL,
  inclZeroWRES = FALSE,
  cont.logy = F,
  hline = "default",
  add.args.cont = list(),
  add.args.cat = list(),
  ...
)

Arguments

vpc.info	Name of PSN file to use. File will come from VPC command in PsN.
vpctab	Name of vpctab file produced from PsN.
object	Xpose data object.
subset	Subset of data to look at.
main	Title for plot.
main.sub	Used for names above each plot when using multiple plots. Should be a vector, e.g. c("title 1","title 2").
inclZeroWRES	Include WRES=0 rows in the computations for these plots?
cont.logy	Should the continuous plot y-axis be on the log scale?
hline	Horizontal line marking the limits of quantification. If they are defined, they must be a vector of values.
add.args.cont	Additional arguments to the continuous plot. xpose.VPC.
add.args.cat	Additional arguments to the categorical plot. xpose.VPC.categorical.
...	Additional arguments to both plots.

Author(s)

Andrew C. Hooker

See Also

xpose.VPC, xpose.VPC.categorical.

Other PsN functions: boot.hist(), bootscm.import(), npc.coverage(), randtest.hist(), read.npc.vpc.results(), read.vpctab(), xpose.VPC(), xpose.VPC.categorical(), xpose4-package

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(), dOFV.vs.id(), 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.categorical(), xpose4-package

Examples

## Not run: 
library(xpose4)

## move to the directory where results from PsN
## are found
cur.dir <- getwd()
setwd(paste(cur.dir,"/vpc_cont_LLOQ/",sep=""))

xpose.VPC()
xpose.VPC.categorical(censored=T)

xpose.VPC.both()

xpose.VPC.both(subset="DV>1.75")

xpose.VPC.both(add.args.cont=list(ylim=c(0,80)))

xpose.VPC.both(add.args.cont = list(ylim = c(0.01, 80)), xlim = c(0, 
    40), add.args.cat = list(ylim = c(0, 0.4)), cont.logy = T)

xpose.VPC.both(cont.logy=T)

## End(Not run)

UUPharmacometrics/xpose4 documentation built on Feb. 22, 2024, 5:02 p.m.