| xpose.VPC(xpose4generic) | R Documentation |
This Function is used to create a VPC in xpose using the output from
the vpc command in Pearl Speaks NONMEM (PsN).
The function reads in the output files created by PsN and creates a
plot from the data. The dependent variable, independent variable and
conditioning variable are
automatically determined from the PsN files.
xpose.VPC(vpc.info = "vpc_results.csv",
vpctab = dir(pattern="^vpctab")[1],
object = NULL,
ids=NULL,
type="p",
by=NULL,
PI="area",
subset=NULL,
main="Default",
inclZeroWRES=FALSE,
...)
vpc.info |
The results file from the vpc command in
PsN. for example ‘vpc_results.csv’, or if the file is in a
separate directory ‘./vpc_dir1/vpc_results.csv’. |
vpctab |
The ‘vpctab’ from the vpc command in
PsN. For example ‘vpctab5’, or if the file is in a
separate directory ‘./vpc_dir1/vpctab5’. Can be NULL.
The default looks in the current working directory and takes the
first file that starts with ‘vpctab’ that it finds. Note that
this default can result in the wrong files being read if there are
multiple ‘vpctab’ files in the directory.
One of object or
vpctab is required. If both are present then the information
from the vpctab will over-ride the xpose data
object object (i.e. the values from the vpctab will replace any
matching values in the object\@Data portion of the xpose data
object). |
object |
An xpose data object. Created from
xpose.data. One of object or
vpctab is required. If both are present then the information
from the vpctab will over-ride the xpose data
object object (i.e. the values from the vpctab will replace any
matching values in the object\@Data portion of the xpose data
object). |
ids |
A logical value indicating whether text ID labels should be
used as plotting symbols (the variable used for these symbols
indicated by the idlab xpose data variable). Can be
NULL or TRUE. |
type |
Character string describing the way the points in the plot
will be displayed. For more details, see
plot. Use type="n" if you don't want
observations in the plot. |
by |
A string or a vector of strings with the name(s) of the
conditioning variables. For example by = c("SEX","WT").
Because the function automatically determines the conditioning
variable from the PsN input file specified in vpc.info, the
by command can control if separate plots are created for each
condition (by=NULL), or if a conditioning plot should be
created (by="WT" for example). If the vpc.info file
has a conditioning variable then by must match that
variable. If there is no conditioning variable in vpc.info
then the PI for each conditioned plot will be the PI
for the entire data set (not only for the conditioning subset). |
PI |
Either "lines", "area" or "both" specifying whether
prediction intervals (as lines, a shaded area or both)
should be added to the plot. NULL means no prediction interval. |
subset |
A string giving the subset expression to be applied to
the data before plotting. See xsubset. |
main |
A string giving the plot title or NULL if
none. "Default" creates a default title. |
inclZeroWRES |
Logical value indicating whether rows with WRES=0 is included in the plot. |
... |
Other arguments passed to
xpose.panel.default,
xpose.plot.default and others. Please see these
functions for more descriptions of what you can do. |
A plot or a list of plots.
Additional graphical elements available in the VPC plots
PI.real = NULL or TRUEPI.mirror = NULL, TRUE or AN.INTEGER.VALUETRUE
takes the first mirror from ‘vpc_results.csv’ and
AN.INTEGER.VALUE can be 1, 2, ... n where n
is the number of mirror's output in the ‘vpc_results.csv’ file.PI.ci = "both", "area" or "lines"PI.real values for model misspecification. Again, as with
the PI.real, note that
with few observations per
bin the CIs will be approximate because the percentiles in each
bin will be approximate. For example,
the 95th percentile of 4 data points will always be the largest of
the 4 data points.PI.limits = c(0.025, 0.975)PI.real, PI.mirror and
PI.ci. However, the confidence interval in PI.ci is
always the one defined in the ‘vpc_results.csv’ file.
Additional options to control the look and feel of the
PI. See See grid.polygon and
plot for more details.
PI.arcolPI
areaPI.up.ltyPI.up.typePI.up.colPI.up.lwdPI.down.ltyPI.down.typePI.down.colPI.down.lwdPI.med.ltyPI.med.typePI.med.colPI.med.lwd
Additional options to control the look and feel of the
PI.ci. See See grid.polygon and
plot for more details.
PI.ci.up.arcolPI.ci.PI.ci.med.arcolPI.ci.PI.ci.down.arcolPI.ci.PI.ci.up.ltyPI.ci.up.typePI.ci.up.colPI.ci.up.lwdPI.ci.down.ltyPI.ci.down.typePI.ci.down.colPI.ci.down.lwdPI.ci.med.ltyPI.ci.med.typePI.ci.med.colPI.ci.med.lwdPI.ci be
smoothed to match the "lines" argument? Allowed values are
TRUE/FALSE. The "area" is set by
default to show
the bins used in the PI.ci computation. By smoothing,
information is lost and, in general, the confidence intervals will
be smaller than they are in reality.
Additional options to control the look and feel of the
PI.real. See See grid.polygon and
plot for more details.
PI.real.up.ltyPI.real.up.typePI.real.up.colPI.real.up.lwdPI.real.down.ltyPI.real.down.typePI.real.down.colPI.real.down.lwdPI.real.med.ltyPI.real.med.typePI.real.med.colPI.real.med.lwd
Additional options to control the look and feel of the
PI.mirror. See See
plot for more details.
PI.mirror.up.ltyPI.mirror.up.typePI.mirror.up.colPI.mirror.up.lwdPI.mirror.down.ltyPI.mirror.down.typePI.mirror.down.colPI.mirror.down.lwdPI.mirror.med.ltyPI.mirror.med.typePI.mirror.med.colPI.mirror.med.lwdAndrew Hooker
read.vpctab
read.npc.vpc.results
xpose.panel.default
xpose.plot.default
## Not run: library(xpose4) xpose.VPC() ## to be more clear about which files should be read in vpc.file <- "vpc_results.csv" vpctab <- "vpctab5" xpose.VPC(vpc.info=vpc.file,vpctab=vpctab) ## with lines and a shaded area for the prediction intervals xpose.VPC(vpc.file,vpctab=vpctab,PI="both") ## with the percentages of the real data xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T) ## with mirrors (if supplied in 'vpc.file') xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.mirror=5) ## with CIs xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci="area") xpose.VPC(vpc.file,vpctab=vpctab,PI.real=T,PI.ci="area",PI=NULL) ## stratification (if 'vpc.file' is stratified) cond.var <- "WT" xpose.VPC(vpc.file,vpctab=vpctab) xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var) xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both",by=cond.var,type="n") ## with no data points in the plot xpose.VPC(vpc.file,vpctab=vpctab,by=cond.var,PI.real=T,PI.ci="area",PI=NULL,type="n") ## with different DV and IDV, just read in new files and plot vpc.file <- "vpc_results.csv" vpctab <- "vpctab5" cond.var <- "WT" xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both",by=cond.var) xpose.VPC(vpctab=vpctab,vpc.info=vpc.file,PI="both") ## to use an xpose data object instead of vpctab ## ## In this example ## we expect to find the required NONMEM run and table files for run ## 5 in the current working directory runnumber <- 5 xpdb <- xpose.data(runnumber) xpose.VPC(vpc.file,object=xpdb) ## to read files in a directory different than the current working directory vpc.file <- "./vpc_strat_WT_4_mirror_5/vpc_results.csv" vpctab <- "./vpc_strat_WT_4_mirror_5/vpctab5" xpose.VPC(vpc.info=vpc.file,vpctab=vpctab) ## End(Not run)