This is only a mirror of the VPC package documentation. All credit is given to Ron Keizer for developing and maintaining the VPC package, and associated documentation.
https://vpc.gitbook.io/vpc-docs-1/
The VPC is a widely used diagnostic tool in pharmacometrics (see e.g. here), showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. Historically, these plots are most commonly created using PsN and Xpose, using NONMEM as the simulation engine) or with Monolix. The aim of this vpc package for R is to provide:
- fully R-based computation of the VPC with plotting handled by
ggplot2 - package that is more flexible regarding input (use simulated data from R, NONMEM, Monolix, Stan, or any other simulation tool)
- easier to customize, e.g. request any prediction / confidence interval or binning strategy upon plotting.
- easier to extend / theme
The package is available on CRAN, and is being developed at GitHub. This documentation is still in development, and should be seen as a more convenient overview of core functionality, compared to the more detailed in-package help. please let us know if you feel some essential information is missing.
install.packages("vpc")
library("vpc")
vpc(sim = simple_data$sim, obs = simple_data$obs)
vpc(sim = simple_data$sim, obs = simple_data$obs, lloq = 20)