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Videos de Conceptos Relacionados

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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Two-Way ANOVA01:17

Two-Way ANOVA

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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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Video Experimental Relacionado

Updated: Sep 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Modelado de efectos de interacción latente dentro de los niveles en modelos vectoriales autorregresivos de varios

Jana Holtmann1, Kenneth Koslowski2

  • 1Wilhelm-Wundt Institute for Psychology, Leipzig University, Neumarkt 9-19, 04109, Leipzig, Germany. jana.holtmann@uni-leipzig.de.

Behavior research methods
|September 5, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce modelos avanzados de series temporales latentes de varios niveles para capturar cómo la dinámica interna de la persona cambia con el tiempo, teniendo en cuenta los moderadores que varían en el tiempo. Estos modelos ofrecen una comprensión más matizada de los procesos psicológicos complejos.

Palabras clave:
Modelado dinámico de ecuaciones estructuralesDatos longitudinales intensivosInteracción latenteLa moderaciónAnálisis de las series temporales

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Área de la Ciencia:

  • Ciencias Psicológicas
  • Psicología cuantitativa
  • Análisis longitudinal de los datos

Sus antecedentes:

  • Los modelos de series temporales de varios niveles (latentes) se utilizan cada vez más para la dinámica interna.
  • Los modelos actuales a menudo pasan por alto los moderadores que varían en el tiempo e influyen en las relaciones longitudinales.
  • Esto limita la comprensión de cómo los procesos dinámicos dentro de la persona se ven afectados por factores cambiantes.

Objetivo del estudio:

  • Ampliar los modelos de series temporales latentes de varios niveles mediante la incorporación de efectos de interacción latente a nivel interno.
  • Proporcionar un tutorial para la aplicación de estos modelos mejorados utilizando la estimación bayesiana.
  • Investigar la dinámica temporal del afecto negativo, la rumia y la atención consciente.

Principales métodos:

  • Extensión de los modelos de series temporales latentes de varios niveles para incluir efectos de interacción latente.
  • Estimación bayesiana a través de las técnicas de cadena de Markov Monte Carlo (MCMC).
  • Estudios de simulación para evaluar el rendimiento y la complejidad del modelo.

Principales resultados:

  • Se ha demostrado la incorporación exitosa de efectos de interacción latente en análisis dinámicos interpersonales.
  • Proporcionó ejemplos empíricos usando afecto negativo, rumiación y atención consciente.
  • Recomendaciones sobre la complejidad del modelo y los requisitos de tamaño de la muestra basadas en simulaciones.

Conclusiones:

  • Los modelos mejorados proporcionan un enfoque más amplio para estudiar la dinámica interna dependiente del tiempo.
  • Los investigadores aplicados pueden utilizar estos modelos para explorar relaciones longitudinales matizadas.
  • Los tamaños de muestra adecuados (por ejemplo, 100 puntos de tiempo para 100 personas) son cruciales para los modelos complejos de efectos aleatorios.