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Pmetrics validation object

Source: R/PM_valid.R
PM_valid.Rd

[Stable]

Contains results of internal validation by simulation to permit generation of visual predictive checks (VPCs), prediction corrected visual predictive checks, (pcVPCs), normalized prediction distribution errors (NPDE), and numerical predictive checks. This is typically a field in a PM_result

Details

The PM_valid object is both a data field within a PM_result, and itself an R6 object comprising data fields and associated methods suitable for analysis and plotting of observed vs. population or individual predicted outputs.

Because PM_valid objects are automatically added to the PM_result by calling the $validate() method of a PM_result after a successful run, it is generally not necessary for users to generate PM_valid objects themselves.

See also

PM_result

Author

Michael Neely

Public fields

simdata

Simulated data created in the validation process

timeBinMedian

Median times for cluster bins

tadBinMedian

Median times after previous doses for cluster bins

opDF

Observed-predicted data frame

npde

Data for Normalized Prediction Distribution Error

npde_tad

Data for Normalized Prediction Distribution Error using Time After Dose if available

Methods

Public methods

Method new()

[Stable]

This function will create an object suitable for plotting visual predictive checks (VPCs) and prediction-corrected visual predictive checks (pcVPCs).

Usage

PM_valid$new(result, tad = FALSE, binCov, doseC, timeC, tadC, limits, ...)

Arguments

result

The result of a prior run, usually supplied by calling the $validate() method of a PM_result at the end of a run, or later loaded with PM_load.

tad

Boolean operator to use time after dose rather than time after start. Default is FALSE.

binCov

A character vector of the names of covariates which are included in the model, i.e. in the model equations and which need to be binned. For example binCov='wt' if "wt" is included in a model equation like V=V0*wt, or binCov=c( 'wt', 'crcl') if both "wt" and "crcl" are included in model equations.

doseC

An integer with the number of dose/covariate bins to cluster, if known from a previous run of this function. Including this value will skip the clustering portion for doses/covariates.

timeC

An integer with the number of observation time bins to cluster, if known from a previous run of this function. Including this value will skip the clustering portion for observation times.

tadC

An integer with the number of time after dose bins to cluster, if known from a previous run of this function. Including this value will skip the clustering portion for time after dose. This argument will be ignored if tad=FALSE.

limits

Limits on simulated parameters. See PM_sim.

...

Other parameters to be passed to PM_sim, especially limits.

Details

The function will guide the user through appropriate clustering of doses, covariates and sample times for prediction correction using the methods of Bergstrand et al (2011). NOTE: Including tad is only valid if steady state conditions exist for each patient. This means that dosing is stable and regular for each patient, without changes in amount or timing, and that sampling occurs after the average concentrations are the same from dose to dose. Otherwise observations are NOT superimposable and tad should NOT be used, i.e. should be set to FALSE.

Returns

An R6 object of class PM_valid, which is a list with the following.

  • simdata The combined, simulated files for all subjects using the population mean values and each subject as a template. This object will be automatically saved to the run, to be loaded with PM_load next time.

  • timeBinMedian A data frame with the median times for each cluster bin.

  • tadBinMedian A data frame with the median time after dose (tad) for each cluster bin. This will be NA if tad = FALSE.

  • opDF A data frame with observations, predicitons, and bin-corrected predictions for each subject.

  • ndpe An object with results of normalized distrubition of prediction errors analysis.

  • npde_tad NPDE with time after dose rather than absolute time, if tad = TRUE

Examples

\dontrun{
valid <- NPex$validate(limits = c(0, 3))
}

Method plot()

Plot method. Calls plot.PM_valid.

Usage

PM_valid$plot(...)

Arguments

...

Arguments to pass to [plot.PM_valid].

Method clone()

The objects of this class are cloneable with this method.

Usage

PM_valid$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `PM_valid$new`
## ------------------------------------------------

if (FALSE) { # \dontrun{
valid <- NPex$validate(limits = c(0, 3))
} # }