tidyvpc (version 1.5.2)

npde: Normalized Prediction Distribution Errors

Description

Normalized Prediction Distribution Errors

Usage

npde(o, ...)
# S3 method for 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

# \donttest{
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")
# }