getSummaryRanks

<p>matrix with columns scenarios and columns objects names to rank</p>

rankingOverview

names

summaryFunction

<p>internal function to rank by a summary function over criterio</p>

internal


Find optimal microsampling designs for non-compartmental pharacokinetic analysis using a  general simulation methodology:
Algorithm III of Barnett, Helen, Helena Geys, Tom Jacobs, and Thomas Jaki. (2017) "Optimal Designs for Non-Compartmental
Analysis of Pharmacokinetic Studies. (currently unpublished)"
This methodology consist of (1) specifying a pharmacokinetic model
including variability among animals; (2) generating possible sampling times; (3)
evaluating performance of each time point choice on simulated data; (4)
generating possible schemes given a time point choice and additional constraints
and finally (5) evaluating scheme performance on simulated data. The default
settings differ from the article of Barnett and others, in the default pharmacokinetic model used and
the parameterization of variability among animals. Details can be found in the package vignette. A 'shiny'
web application is included, which guides users from model parametrization to
optimal microsampling scheme.

Adriaan Blommaert

microsamplingDesign

Finding Optimal Microsampling Designs for Non-Compartmental
Pharmacokinetic Analysis

Daan Seynaeve

Helen Barnett

Helena Geys

Tom Jacobs

Fetene Tekle

Thomas Jaki

getSummaryRanks function

internal function to rank by a summary function over criterio — getSummaryRanks

internal function to rank by a summary function over criterio

getSummaryRanks: internal function to rank by a summary function over criterio

Description

Usage

Arguments

Value