xpose.VPC.categorical: Xpose visual predictive check for categorical data.

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Xpose visual predictive check for categorical data.

Description

Xpose visual predictive check for categorical data (binary, ordered categorical and count data).

Usage

xpose.VPC.categorical(
  vpc.info = "vpc_results.csv",
  vpctab = dir(pattern = "^vpctab")[1],
  object = NULL,
  subset = NULL,
  main = "Default",
  main.sub = "Default",
  main.sub.cex = 0.85,
  real.col = 4,
  real.lty = "b",
  real.cex = 1,
  real.lwd = 1,
  median.line = FALSE,
  median.col = "darkgrey",
  median.lty = 1,
  ci.lines = FALSE,
  ci.col = "blue",
  ci.lines.col = "darkblue",
  ci.lines.lty = 3,
  xlb = "Default",
  ylb = "Proportion of Total",
  force.x.continuous = FALSE,
  level.to.plot = NULL,
  max.plots.per.page = 1,
  rug = TRUE,
  rug.col = "orange",
  censored = FALSE,
  ...
)

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").
main.sub.cex	Size of main.sub
real.col	Color of real line.
real.lty	Real line type.
real.cex	Size of real line.
real.lwd	Width of real line.
median.line	Dray a median line?
median.col	Color of median line.
median.lty	median line type.
ci.lines	Lines marking confidence interval?
ci.col	Color of CI area.
ci.lines.col	Color of CI lines.
ci.lines.lty	Type of CI lines.
xlb	X-axis label. If other than "default"" passed directly to xyplot.
ylb	Y-axis label. Passed directly to xyplot.
force.x.continuous	For the x variable to be continuous.
level.to.plot	Which levels of the variable to plot. Smallest level is 1, largest is number_of_levels. For example, with 4 levels, the largest level would be 4, the smallest would be 1.
max.plots.per.page	The number of plots per page.
rug	Should there be markings on the plot showing where the intervals for the VPC are?
rug.col	Color of the rug.
censored	Is this censored data? Censored data can be both below and above the limit of quantification.
...	Additional information passed to function.

Author(s)

Andrew C. Hooker

See Also

xpose.VPC.both.

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

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

Examples

## Not run: 
library(xpose4)

## move to the directory where results from PsN
## are found
cur.dir <- getwd()
setwd(paste(cur.dir,"/binary/vpc_36",sep=""))

xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4)
xpose.VPC.categorical(level.to.plot=1,max.plots.per.page=4,by="DOSE")

## ordered categorical plots
setwd(paste(cur.dir,"/ordered_cat/vpc_45",sep=""))
xpose.VPC.categorical()


## count
setwd(paste(cur.dir,"/count/vpc65b",sep=""))
xpose.VPC.categorical()

setwd(paste(cur.dir,"/count/vpc65a",sep=""))
xpose.VPC.categorical()


## End(Not run)

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