A plot showing the most and least influential individuals in determining a drop in OFV between two models.
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),
...
)
Xpose data object for first NONMEM run ("new" run)
Xpose data object for Second NONMEM run ("reference" run)
What is a significant drop of OFV?
How many points should bw labeled with ID values for those IDs with a drop in iOFV?
How many points should bw labeled with ID values for those IDs with an increase in iOFV?
Size of ID labels.
ID label position.
Type of lines.
X-axis label.
Y-axis label.
Title of plot.
Significant OFV drop line color.
Significant OFV drop line type.
Total OFV drop line color.
Total OFV drop line type.
Legend for plot.
Additional arguments to function.
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
# NOT RUN {
# }
# 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)
# }
# NOT RUN {
# }
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