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.
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(), ... )
vpc.info |
Name of PSN file to use. File will come from |
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. |
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.
|
add.args.cat |
Additional arguments to the categorical plot.
|
... |
Additional arguments to both plots. |
Andrew C. Hooker
Other PsN functions:
boot.hist(),
bootscm.import(),
npc.coverage(),
randtest.hist(),
read.npc.vpc.results(),
read.vpctab(),
xpose.VPC.categorical(),
xpose.VPC(),
xpose4-package
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.categorical(),
xpose.VPC(),
xpose4-package
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.