Check through the source dataset to detect problems
This function graphically "checks out" the dataset to identify errors or inconsistencies.
data.checkout( obj = NULL, datafile = ".ask.", hlin = -99, dotcol = "black", dotpch = 16, dotcex = 1, idlab = "ID", csv = NULL, main = "Default", ... )
obj |
NULL or an xpose.data object. |
datafile |
A data file, suitable for import by
|
hlin |
An integer, specifying the line number on which the column headers appear. |
dotcol |
Colour for the dots in the dotplot. If obj is an xpose data object then the default is to use the same value as defined for box-and-whisker plots. |
dotpch |
Plotting character for the dots in the dotplot. If obj is an xpose data object then the default is to use the same value as defined for box-and-whisker plots. |
dotcex |
Relative scaling for the dots in the dotplot. If obj is an xpose data object then the default is to use the same value as defined for box-and-whisker plots. |
idlab |
The ID column label in the dataset. Input as a text string. |
csv |
Is the data file in CSV format (comma separated values)? If the
value is |
main |
The title to the plot. "default" means that Xpose creates a title. |
... |
Other arguments passed to |
This function creates a series of dotplots, one for each variable in
the dataset, against individual ID. Outliers and clusters may easily be
detected in this manner.
A stack of dotplots.
Niclas Jonsson, Andrew Hooker & Justin Wilkins
Other data functions:
add_transformed_columns,
change_graphical_parameters,
change_misc_parameters,
compute.cwres(),
data_extract_or_assign,
db.names(),
export.graph.par(),
export.variable.definitions(),
import.graph.par(),
import.variable.definitions(),
make.sb.data(),
nsim(),
par_cov_summary,
read.TTE.sim.data(),
read.nm.tables(),
read_NM_output,
read_nm_table(),
simprazExample(),
tabulate.parameters(),
xlabel(),
xpose.data,
xpose.print(),
xpose4-package,
xsubset()
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(),
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: ## We expect to find the required NONMEM run, table and data files for run ## 5 in the current working directory xpdb5 <- xpose.data(5) data.checkout(xpdb5, datafile = "mydata.dta") data.checkout(datafile = "mydata.dta") ## End(Not run)
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