Package index • xpose4

General information and simple examples
`xpose4-package` `xpose`	The Xpose Package
`simprazExample()`	Function to create files for the simulated prazosin example in Xpose
`simpraz.xpdb`	Simulated prazosin Xpose database.
Classic interface Xpose has a text based menu interface to make it simple for the user to invoke the Xpose specific functions. This interface is called Xpose Classic. Given the limitations a text based interface imposes, Xpose Classic is not very flexible but may be useful for quick assessment of a model and for learning to use Xpose.
`xpose4()`	Classic menu system for Xpose 4
Data import and database manipulation Functions for managing the inporting of data, visualization of that data and manipulating the resulting Xpose database.
`xpose.data()`	Create an Xpose data object
`data.checkout()`	Check through the source dataset to detect problems
`xvardef()` `xvardef<-`()	Extract and set Xpose variable definitions.
`change.xvardef()` `change.xvardef<-`()	Change Xpose variable definitions.
`change.parm()`	Change parameter scope.
`change.var.name()`	Changes the name of an Xpose data item
`change.xlabel()`	Changes the label of an Xpose data item
`add.absval()` `add.dichot()` `add.exp()` `add.log()` `add.tad()`	Column-transformation functions for Xpose 4
`change.ab.graph.par()` `change.bw.graph.par()` `change.cond.graph.par()` `change.dil.graph.par()` `change.label.par()` `change.lm.graph.par()` `change.misc.graph.par()` `change.pi.graph.par()` `change.smooth.graph.par()`	Functions changing variable definitions in Xpose 4
`change.cat.cont()` `change.cat.cont<-`() `change.cat.levels()` `change.cat.levels<-`() `change.dv.cat.levels()` `change.dv.cat.levels<-`() `change.miss()` `change.subset()` `get.doc()` `set.doc()`	Functions changing miscellaneous parameter settings in Xpose 4
`compute.cwres()` `xpose.calculate.cwres()`	Compute the Conditional Weighted Residuals
`Data()` `Data<-`() `SData()` `SData<-`()	Extract or assign data from an xpose.data object.
`db.names()`	Prints the contents of an Xpose data object
`export.graph.par()` `xpose.write()`	Exports Xpose graphics settings to a file.
`export.variable.definitions()`	Exports Xpose variable definitions to a file from an Xpose data object.
`import.graph.par()`	Imports Xpose graphics settings from a file to an Xpose data object.
`import.variable.definitions()`	Imports Xpose variable definitions from a file to an Xpose data object.
`make.sb.data()`	Make stacked bar data set.
`nsim()`	Extract or set the value of the Nsim slot.
`cov.summary()` `parm.summary()`	Summarize individual parameter values and covariates
`read.TTE.sim.data()`	Read (repeated) time-to-event simulation data files.
`read.nm.tables()`	Reading NONMEM table files
`calc.npar()` `create.parameter.list()` `read.lst()`	Read NONMEM output files into Xpose 4
`read_nm_table()`	Read NONMEM table files produced from simulation.
`simprazExample()`	Function to create files for the simulated prazosin example in Xpose
`tabulate.parameters()`	Tabulate the population parameter estimates
`xlabel()` `xlabel<-`()	Extract and set labels for Xpose data items.
`xpose.print()`	Summarize an xpose database
`xsubset()` `xsubset<-`()	Extract or set the value of the Subset slot.
Data visulalization
`dv.vs.idv()`	Observations (DV) plotted against the independent variable (IDV) for Xpose 4
`pred.vs.idv()`	Population predictions (PRED) plotted against the independent variable (IDV) for Xpose 4
`ipred.vs.idv()`	Individual predictions (IPRED) plotted against the independent variable (IDV) for Xpose 4
`cov.hist()` `parm.hist()` `ranpar.hist()`	Plot the parameter or covariate distributions using a histogram
`cov.qq()` `parm.qq()` `ranpar.qq()`	Plot the parameter or covariate distributions using quantile-quantile (Q-Q) plots
`cov.splom()` `parm.splom()` `ranpar.splom()`	Plot scatterplot matrices of parameters, random parameters or covariates
`cov.summary()` `parm.summary()`	Summarize individual parameter values and covariates
`cat.dv.vs.idv.sb()`	Categorical observations vs. independent variable using stacked bars.
`kaplan.plot()`	Kaplan-Meier plots of (repeated) time-to-event data
Overall goodness of fit assessment
`dv.preds.vs.idv()`	Observations (DV), individual predictions (IPRED) and population predictions (IPRED) plotted against the independent variable (IDV), for Xpose 4
`ind.plots()`	Observations (DV), individual predictions (IPRED) and population predictions (PRED) are plotted against the independent variable for every individual in the dataset, for Xpose 4
`basic.gof()`	Basic goodness-of-fit plots, for Xpose 4
`addit.gof()`	Additional goodness-of-fit plots, for Xpose 4
`runsum()`	Print run summary in Xpose 4
Structural model diagnostics
`dv.vs.pred()`	Observations (DV) plotted against population predictions (PRED) for Xpose 4
`dv.vs.pred.by.cov()`	Dependent variable vs population predictions, conditioned on covariates, for Xpose 4
`dv.vs.pred.by.idv()`	Dependent variable vs population predictions, conditioned on independent variable, for Xpose 4
`dv.vs.pred.ipred()`	Observations (DV) are plotted against individual predictions (IPRED) and population predictions (PRED), for Xpose 4
`dv.vs.ipred()`	Observations (DV) plotted against individual predictions (IPRED) for Xpose 4
`dv.vs.ipred.by.cov()`	Dependent variable vs individual predictions, conditioned on covariates, for Xpose 4
`dv.vs.ipred.by.idv()`	Dependent variable vs individual predictions, conditioned on independent variable, for Xpose 4
`cwres.vs.idv()`	Population conditional weighted residuals (CWRES) plotted against the independent variable (IDV) for Xpose 4
`cwres.vs.idv.bw()`	Box-and-whisker plot of conditional weighted residuals vs the independent variable for Xpose 4
`wres.vs.idv()`	Population weighted residuals (WRES) plotted against the independent variable (IDV) for Xpose 4
`wres.vs.idv.bw()`	Box-and-whisker plot of weighted residuals vs the independent variable for Xpose 4
`iwres.vs.idv()`	Individual weighted residuals (IWRES) plotted against the independent variable (IDV) for Xpose 4
`cwres.wres.vs.idv()`	Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the independent variable (IDV)
`cwres.wres.vs.pred()`	Weighted residuals (WRES) and conditional WRES (CWRES) plotted against the population predictions (PRED)
`cwres.vs.pred()`	Population conditional weighted residuals (CWRES) plotted against population predictions (PRED) for Xpose 4
`cwres.vs.pred.bw()`	Box-and-whisker plot of conditional weighted residuals vs population predictions for Xpose 4
`cwres.vs.cov()`	Conditional Weighted residuals (CWRES) plotted against covariates, for Xpose 4
`wres.vs.pred()`	Population weighted residuals (WRES) plotted against population predictions (PRED) for Xpose 4
`wres.vs.pred.bw()`	Box-and-whisker plot of weighted residuals vs population predictions for Xpose 4
`wres.vs.cov()`	Weighted residuals (WRES) plotted against covariates, for Xpose 4
Residual model diagnostics
`absval.cwres.vs.cov.bw()`	Absolute conditional weighted residuals vs covariates for Xpose 4
`absval.cwres.vs.pred()`	Absolute population conditional weighted residuals vs population predictions for Xpose 4
`absval.cwres.vs.pred.by.cov()`	Absolute value of the conditional weighted residuals vs. population predictions, conditioned on covariates, for Xpose 4
`absval.wres.vs.cov.bw()`	Absolute weighted residuals vs covariates for Xpose 4
`absval.wres.vs.idv()`	Absolute value of (C)WRES vs. independent variable plot in Xpose4.
`absval.wres.vs.pred()`	Absolute population weighted residuals vs population predictions for Xpose 4
`absval.wres.vs.pred.by.cov()`	Absolute population weighted residuals vs population predictions, conditioned on covariates, for Xpose 4
`absval.iwres.cwres.vs.ipred.pred()` `absval.iwres.wres.vs.ipred.pred()`	Absolute population weighted residuals vs population predictions, and absolute individual weighted residuals vs individual predictions, for Xpose 4
`absval.iwres.vs.cov.bw()`	box and whisker plots of the absolute value of the individual weighted residuals vs. covariates
`absval.iwres.vs.idv()`	absolute value of the individual weighted residuals vs. the independent variable
`absval.iwres.vs.ipred()`	Absolute individual weighted residuals vs individual predictions for Xpose 4
`absval.iwres.vs.ipred.by.cov()`	Absolute individual weighted residuals vs individual predictions, conditioned on covariates, for Xpose 4
`absval.iwres.vs.pred()`	Absolute individual weighted residuals vs population predictions or independent variable for Xpose 4
`cwres.dist.hist()`	Histogram of conditional weighted residuals (CWRES), for Xpose 4
`wres.dist.hist()`	Histogram of weighted residuals (WRES), for Xpose 4
`iwres.dist.hist()`	Histogram of individual weighted residuals (IWRES), for Xpose 4
`cwres.dist.qq()`	Quantile-quantile plot of conditional weighted residuals (CWRES), for Xpose 4
`wres.dist.qq()`	Quantile-quantile plot of weighted residuals (WRES), for Xpose 4
`iwres.dist.qq()`	Quantile-quantile plot of individual weighted residuals (IWRES), for Xpose 4
`ind.plots.cwres.hist()` `ind.plots.wres.hist()`	Histograms of weighted residuals for each individual in an Xpose data object, for Xpose 4
`ind.plots.cwres.qq()` `ind.plots.wres.qq()`	Quantile-quantile plots of weighted residuals for each individual in an Xpose data object, for Xpose 4
`autocorr.cwres()`	Autocorrelation of conditional weighted residuals for Xpose 4
`autocorr.iwres()`	autocorrelation of the individual weighted residuals
`autocorr.wres()`	Autocorrelation of weighted residuals for Xpose 4
Parameter distribution diagnostics
`cov.hist()` `parm.hist()` `ranpar.hist()`	Plot the parameter or covariate distributions using a histogram
`cov.qq()` `parm.qq()` `ranpar.qq()`	Plot the parameter or covariate distributions using quantile-quantile (Q-Q) plots
`cov.splom()` `parm.splom()` `ranpar.splom()`	Plot scatterplot matrices of parameters, random parameters or covariates
`parm.vs.parm()`	Plot parameters vs other parameters
`tabulate.parameters()`	Tabulate the population parameter estimates
`cov.summary()` `parm.summary()`	Summarize individual parameter values and covariates
Model comparison
`basic.model.comp()`	Basic model comparison plots, for Xpose 4
`add.model.comp()`	Additional model comparison plots, for Xpose 4
`absval.dcwres.vs.cov.model.comp()` `absval.dipred.vs.cov.model.comp()` `absval.diwres.vs.cov.model.comp()` `absval.dpred.vs.cov.model.comp()` `absval.dwres.vs.cov.model.comp()`	Model comparison plots, of absolute differences in goodness-of-fit predictors against covariates, for Xpose 4
`dOFV.vs.cov()`	Change in individual objective function value vs. covariate value.
`dOFV.vs.id()`	Change in Objective function value vs. removal of individuals.
`dOFV1.vs.dOFV2()`	Change in individual objective function value 1 vs. individual objective function value 2.
Covariate model development
`parm.vs.cov()`	Parameters plotted against covariates, for Xpose 4
`ranpar.vs.cov()`	Random parameters plotted against covariates, for Xpose 4
GAM functions Functions take an Xpose object and performs a generalized additive model (GAM) stepwise search for influential covariates on a single model parameter.
`xpose.gam()`	Stepwise GAM search for covariates on a parameter (Xpose 4)
`xp.akaike.plot()` `xp.cook()` `xp.ind.inf.fit()` `xp.ind.inf.terms()` `xp.ind.stud.res()` `xp.plot()` `xp.summary()`	GAM functions for Xpose 4
`xp.get.disp()`	Default function for calculating dispersion in `xpose.gam`.
`xp.scope3()`	Define a scope for the gam. Used as default input to the `scope` argument in `xpose.gam`
`xpose.bootgam()`	Title
`bootgam.print()`	Print summary information for a bootgam or bootscm
`xp.distr.mod.size()`	Plot of model size distribution for a bootgam or bootscm
`xp.inc.cond.stab.cov()`	Trace plots for conditional indices
`xp.inc.ind.cond.stab.cov()`	Trace plots for conditional indices rper replicate number
`xp.inc.prob()`	Inclusion frequency plot
`xp.inc.prob.comb.2()`	Inclusion frequency plot for combination of covariates.
`xp.inc.stab.cov()`	Inclusion stability plot A plot of the inclusion frequency of covariates vs bootgam/bootscm iteration number. This plot can be used to evaluate whether sufficient iterations have been performed.
`xp.incl.index.cov()`	Plot of inclusion index of covariates.
`xp.incl.index.cov.comp()`	Inclusion index individuals, compare between covariates.
`xp.incl.index.cov.ind()`	Individual inclusion index
VPC and NPC functions
`xpose.VPC()`	Visual Predictive Check (VPC) using XPOSE
`xpose.VPC.both()`	Xpose Visual Predictive Check (VPC) for both continuous and Limit of Quantification data.
`xpose.VPC.categorical()`	Xpose visual predictive check for categorical data.
`npc.coverage()`	Function to plot the coverage of the Numerical Predictive Check
`kaplan.plot()`	Kaplan-Meier plots of (repeated) time-to-event data
`cat.pc()`	Categorical (visual) predictive check.
`read.npc.vpc.results()`	Read the results file from a Numerical or Visual Predictive Check run in PsN
`read.vpctab()`	Read the vpctab file from PsN into Xpose
PsN functions These functions are the interface between Xpose and PsN, i.e. they do not post-process NONMEM output but rather PsN output.
`boot.hist()`	Function to create histograms of results from the `bootstrap` tool in PsN
`bootscm.import()`	Import bootscm data into R/Xpose
`randtest.hist()`	Function to create a histogram of results from the randomization test tool (`randtest`) in PsN
`bootgam.print()`	Print summary information for a bootgam or bootscm
`xp.boot.par.est()`	Compare parameter estimates for covariate coefficients
`xp.boot.par.est.corr()`	Correlations between covariate coefficients
`xp.daic.npar.plot()`	Distribution of difference in AIC
`xp.distr.mod.size()`	Plot of model size distribution for a bootgam or bootscm
`xp.dofv.npar.plot()`	Distribution of difference in OFV
`xp.dofv.plot()`	OFV difference (optimism) plot.
`xp.inc.cond.stab.cov()`	Trace plots for conditional indices
`xp.inc.ind.cond.stab.cov()`	Trace plots for conditional indices rper replicate number
`xp.inc.prob()`	Inclusion frequency plot
`xp.inc.prob.comb.2()`	Inclusion frequency plot for combination of covariates.
`xp.inc.stab.cov()`	Inclusion stability plot A plot of the inclusion frequency of covariates vs bootgam/bootscm iteration number. This plot can be used to evaluate whether sufficient iterations have been performed.
`xp.incl.index.cov()`	Plot of inclusion index of covariates.
`xp.incl.index.cov.comp()`	Inclusion index individuals, compare between covariates.
`xp.incl.index.cov.ind()`	Individual inclusion index
Generic functions Generic wrapper functions around the lattice functions. These functions can be invoked by the user but require quite detailed instructions to generate the desired output.
`gof()`	Structured goodness of fit diagnostics.
`xpose.multiple.plot()`	Create and object with class "xpose.multiple.plot".
`xpose.multiple.plot.default()`	Xpose 4 generic function for plotting multiple lattice objects on one page
`xpose.panel.bw()`	Default box-and-whisker panel function for Xpose 4
`xpose.panel.default()`	Default panel function for Xpose 4
`xpose.panel.histogram()`	Default histogram panel function for Xpose 4
`xpose.panel.qq()`	Default QQ panel function for Xpose 4
`xpose.panel.splom()`	Scatterplot matrix panel function for Xpose 4
`xpose.plot.bw()`	The generic Xpose functions for box-and-whisker plots
`xpose.plot.default()`	The Xpose 4 generic functions for continuous y-variables.
`xpose.plot.histogram()`	The Xpose 4 generic functions for continuous y-variables.
`xpose.plot.qq()`	The generic Xpose functions for QQ plots
`xpose.plot.splom()`	The Xpose 4 generic functions for scatterplot matrices.
See also
`add.grid.table()`	Print tables or text in a grid object
`create.xpose.plot.classes()`	Create xpose.multiple.plot class.
`createXposeClasses()`	This function creates the Xpose data classes ("xpose.data" and "xpose.prefs")
`print(<xpose.multiple.plot>)`	Print an Xpose multiple plot object.
`reset.graph.par()`	Resets Xpose variable definitions to factory settings
`xpose.data-class` `numeric_or_NULL-class` `data.frame_or_NULL-class`	Class xpose.data
`xpose.license.citation()`	Displays the Xpose license and citation information
`xpose.multiple.plot-class` `list_or_NULL-class` `logical_or_numeric-class` `character_or_NULL-class`	Class for creating multiple plots in xpose
`xpose.prefs-class` `character_or_numeric-class`	Class "xpose.prefs"
`xpose.string.print()`	Print a pretty string.
`xpose.logTicks()` `xpose.yscale.components.log10()` `xpose.xscale.components.log10()`	Functions to create nice looking axes when using Log scales.

Reference

General information and simple examples

Classic interface

Data import and database manipulation

Data visulalization

Overall goodness of fit assessment

Structural model diagnostics

Residual model diagnostics

Parameter distribution diagnostics

Model comparison

Covariate model development

GAM functions

VPC and NPC functions

PsN functions

Generic functions

See also