Change in Objective function value vs. removal of individuals.
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),
...
)xpdb1 |
Xpose data object for first NONMEM run ("new" run) |
xpdb2 |
Xpose data object for Second NONMEM run ("reference" run) |
sig.drop |
What is a significant drop of OFV? |
decrease.label.number |
How many points should bw labeled with ID values for those IDs with a drop in iOFV? |
increase.label.number |
How many points should bw labeled with ID values for those IDs with an increase in iOFV? |
id.lab.cex |
Size of ID labels. |
id.lab.pos |
ID label position. |
type |
Type of lines. |
xlb |
X-axis label. |
ylb |
Y-axis label. |
main |
Title of plot. |
sig.line.col |
Significant OFV drop line color. |
sig.line.lty |
Significant OFV drop line type. |
tot.line.col |
Total OFV drop line color. |
tot.line.lty |
Total OFV drop line type. |
key |
Legend for plot. |
... |
Additional arguments to function. |
Andrew C. Hooker
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: 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) ## End(Not run)
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