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",
...
)
NULL or an xpose.data object.
A data file, suitable for import by
read.table
.
An integer, specifying the line number on which the column headers appear.
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.
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.
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.
The ID column label in the dataset. Input as a text string.
Is the data file in CSV format (comma separated values)? If the
value is NULL
then the user is asked at the command line. If
supplied to the function the value can be TRUE/FALSE
.
The title to the plot. "default" means that Xpose creates a title.
Other arguments passed to link[lattice]{dotplot}
.
A stack of dotplots.
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.
dotplot
, xpose.prefs-class
,
read.table
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 {
# }
# 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")
# }
# NOT RUN {
# }
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