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

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.

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

Author

Andrew C. Hooker

Examples


if (FALSE) { # \dontrun{
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)
} # }

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

Usage

Arguments

See also

Author

Examples