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Resolución bayesiana de modelos lineales mixtos aplicados a muestras grandes

Authors: López Álvarez, María;

Resolución bayesiana de modelos lineales mixtos aplicados a muestras grandes

Abstract

Los modelos lineales mixtos para la mejora genética, como estimar la influencia del sexo y los progenitores de un individuo en el peso al nacimiento, se pueden resolver mediante métodos Markov Chain Monte Carlo (MCMC). Existen diferentes formas de abordar su implementación, y se trabajará con una base de datos real (931 689 animales) tratando de encontrar la más adecuada. En un primer análisis descriptivo se observa como los machos tendrán un mayor peso al nacimiento que las hembras, sin embargo, poco se puede decir de los valores mejorantes o efectos genéticos. Por tanto, es necesario utilizar los métodos MCMC para obtener resultados más completos. En una primera implementación “rudimentaria”, desarrollada por el usuario con el software R, se puede llegar a trabajar con una pequeña parte de la base de datos que apenas llega al 0.5% del total. Es decir, esta primera opción no será suficiente para abordar problemas reales. Estimar los pesos al nacimiento con paquetes de R parece ser una solución viable a la hora de aumentar el número de individuos con los que se trabaja. Con rstan se aumenta la cantidad de animales con los que se puede trabajar en un tiempo razonable, aunque se sigue sin obtener soluciones para más del 1% de individuos de la población.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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