Check through the source dataset to detect problems

This function graphically "checks out" the dataset to identify errors or inconsistencies.

Usage

data.checkout(
  obj = NULL,
  datafile = ".ask.",
  hlin = -99,
  dotcol = "black",
  dotpch = 16,
  dotcex = 1,
  idlab = "ID",
  csv = NULL,
  main = "Default",
  ...
)

Arguments

obj

NULL or an xpose.data object.

datafile

A data file, suitable for import by read.table.

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 NULL then the user is asked at the command line. If supplied to the function the value can be TRUE/FALSE.

main

The title to the plot. "default" means that Xpose creates a title.

...

Other arguments passed to link[lattice]{dotplot}.

Value

A stack of dotplots.

Details

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.

See also

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(), absval.cwres.vs.pred.by.cov(), absval.iwres.cwres.vs.ipred.pred(), absval.iwres.vs.cov.bw(), absval.iwres.vs.idv(), absval.iwres.vs.ipred(), absval.iwres.vs.ipred.by.cov(), absval.iwres.vs.pred(), absval.wres.vs.cov.bw(), absval.wres.vs.idv(), absval.wres.vs.pred(), absval.wres.vs.pred.by.cov(), 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(), cwres.vs.idv.bw(), cwres.vs.pred(), cwres.vs.pred.bw(), 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(), dv.vs.ipred.by.cov(), dv.vs.ipred.by.idv(), dv.vs.pred(), dv.vs.pred.by.cov(), dv.vs.pred.by.idv(), dv.vs.pred.ipred(), gof(), ind.plots(), ind.plots.cwres.hist(), ind.plots.cwres.qq(), 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(), wres.vs.idv.bw(), wres.vs.pred(), wres.vs.pred.bw(), xpose.VPC(), xpose.VPC.both(), xpose.VPC.categorical(), xpose4-package

Author

Niclas Jonsson, Andrew Hooker & Justin Wilkins

Examples

if (FALSE) {
## 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")
}