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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Related Experiment Video

Updated: Jun 9, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

New methodological and software tools for probing moderation in intrinsically nonlinear models.

Haley E Yaremych1,2, Kristopher J Preacher3

  • 1Department of Psychology & Human Development, Vanderbilt University, Nashville, TN, USA. haley.yaremych@gmail.com.

Behavior Research Methods
|June 8, 2026
PubMed
Summary

Researchers can now analyze moderation in nonlinear psychological models using new methods and the CurveBuilder application. This extends the Johnson-Neyman technique for better insights into complex relationships.

Keywords:
InteractionIntrinsically nonlinear modelsJohnson-Neyman techniqueModerationNonlinear modelsShiny

Related Experiment Videos

Last Updated: Jun 9, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Psychology
  • Statistics
  • Computational Science

Background:

  • Intrinsically nonlinear models improve psychological research but lack methods for moderation analysis.
  • Existing moderation techniques are limited to linear models, hindering the study of complex psychological processes.
  • Testing, probing, and plotting moderation are crucial for psychological theories.

Purpose of the Study:

  • To develop novel analytical and software tools for examining moderated parameters in intrinsically nonlinear models.
  • To extend the Johnson-Neyman (JN) technique for moderation analysis in nonlinear contexts.
  • To introduce CurveBuilder, a user-friendly application for fitting and visualizing nonlinear models with moderation.

Main Methods:

  • Conceptual and mathematical extensions of the Johnson-Neyman (JN) technique for nonlinear models.
  • Development of a Shiny application, CurveBuilder, for a code-free workflow.
  • Unification of model specification, fitting, visualization, and moderation probing within CurveBuilder.

Main Results:

  • Extended JN technique applicable to any moderated parameter in intrinsically nonlinear models.
  • CurveBuilder provides a unified, code-free environment for complex model analysis.
  • Successful demonstration of CurveBuilder's capabilities in analyzing moderated nonlinear models.

Conclusions:

  • The developed methods and CurveBuilder significantly advance the analysis of moderation in nonlinear psychological models.
  • Researchers can now more effectively test and visualize moderated relationships in complex psychological processes.
  • These tools facilitate deeper theoretical and substantive conclusions in psychological research.