npde: Normalized Prediction Distribution Errors

In tidyvpc: VPC Percentiles and Prediction Intervals

Normalized Prediction Distribution Errors

Description

Normalized Prediction Distribution Errors

Usage

npde(o, ...)

## S3 method for class 'tidyvpcobj'
npde(o, id, data = o$data, smooth = FALSE, ...)

Arguments

o	A tidyvpcobj.
...	Additional arguments.
id	A vector of IDs. Used to associate observations (y) that originate from the same individual. Evaluated in the data.frame data.
data	A data.frame.
smooth	Should a uniform random perturbation be used to smooth the pd/pde values?

References

Brendel, K., Comets, E., Laffont, C., Laveille, C. & Mentrée, F. Metrics for external model evaluation with an application to the population pharmacokinetics of gliclazide. Pharm. Res. (2006) 23(9), 2036–2049.

Nguyen, T.H.T., et al. Model evaluation of continuous data pharmacometric models: metrics and graphics. CPT Pharmacometrics Syst. Pharmacol. (2017) 6(2), 87–109; doi:10.1002/psp4.12161.

Examples

require(magrittr)
require(ggplot2)

obs <- obs_data[MDV==0]
sim <- sim_data[MDV==0]

npde <- observed(obs, x=NULL, y=DV) %>%
    simulated(sim, y=DV) %>%
    npde(id=ID)

vpc <- observed(npde$npdeobs, x=epred, y=npde) %>%
    simulated(npde$npdesim, y=npde) %>%
    binning("eqcut", nbins=10) %>%
    vpcstats()

plot(vpc) + 
labs(x="Simulation-based Population Prediction", y="Normalized Prediction Distribution Error")

tidyvpc documentation built on May 29, 2024, 8:29 a.m.