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Memorias de investigación
Artículos en revistas:
Sensitivity analysis of Repast computational ecology models with R/Repast
Año:2016
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual-based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities or populations due to individual variability. In addition, being a bottom up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in silico experimental setup. In this paper we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.
Internacional
Si
JCR del ISI
Si
Título de la revista
Ecology And Evolution
ISSN
2045-7758
Factor de impacto JCR
2,537
Información de impacto
Datos JCR del año 2015
Volumen
6
DOI
10.1002/ece3.2580
Número de revista
24
Desde la página
8811
Hasta la página
8831
Mes
DICIEMBRE
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Antonio Prestes Garcia (UPM)
  • Autor: Alfonso Vicente Rodriguez-Paton Aradas (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Inteligencia Artificial (LIA)
  • Departamento: Inteligencia Artificial
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