- -

GSEVM v.2: MCMC software to analyze genetically structured environmental variance models

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

GSEVM v.2: MCMC software to analyze genetically structured environmental variance models

Mostrar el registro sencillo del ítem

Ficheros en el ítem

[Cerrado]
Nombre: Ibáñez-Escriche;G ...
Tamaño: 92.32Kb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Ibáñez-Escriche, Noelia es_ES
dc.contributor.author Garcia, M. es_ES
dc.contributor.author Sorensen, D. es_ES
dc.date.accessioned 2020-03-12T06:51:54Z
dc.date.available 2020-03-12T06:51:54Z
dc.date.issued 2010-06 es_ES
dc.identifier.issn 0931-2668 es_ES
dc.identifier.uri http://hdl.handle.net/10251/138746
dc.description.abstract [EN] This note provides a description of software that allows to fit Bayesian genetically structured variance models using Markov chain Monte Carlo (MCMC). The gsevm v.2 program was written in Fortran 90. The DOS and Unix executable programs, the user's guide, and some example files are freely available for research purposes at http://www.bdporc.irta.es/estudis.jsp. The main feature of the program is to compute Monte Carlo estimates of marginal posterior distributions of parameters of interest. The program is quite flexible, allowing the user to fit a variety of linear models at the level of the mean and the logvariance. es_ES
dc.description.sponsorship The authors are grateful to Rasmus Waagepetersen for computational and statistical input over the years. es_ES
dc.language Inglés es_ES
dc.publisher Blackwell Publishing es_ES
dc.relation.ispartof Journal of Animal Breeding and Genetics es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Bayesian analysis es_ES
dc.subject Heterogeneous residual variance es_ES
dc.subject MCMC software es_ES
dc.subject.classification PRODUCCION ANIMAL es_ES
dc.title GSEVM v.2: MCMC software to analyze genetically structured environmental variance models es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1111/j.1439-0388.2009.00846.x es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal es_ES
dc.description.bibliographicCitation Ibáñez-Escriche, N.; Garcia, M.; Sorensen, D. (2010). GSEVM v.2: MCMC software to analyze genetically structured environmental variance models. Journal of Animal Breeding and Genetics. 127(3):249-251. https://doi.org/10.1111/j.1439-0388.2009.00846.x es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1111/j.1439-0388.2009.00846.x es_ES
dc.description.upvformatpinicio 249 es_ES
dc.description.upvformatpfin 251 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 127 es_ES
dc.description.issue 3 es_ES
dc.relation.pasarela S\392953 es_ES
dc.description.references Gutiérrez, J., Nieto, B., Piqueras, P., Ibáñez, N., & Salgado, C. (2006). Genetic parameters for canalisation analysis of litter size and litter weight traits at birth in mice. Genetics Selection Evolution, 38(5), 445. doi:10.1186/1297-9686-38-5-445 es_ES
dc.description.references Henderson, C. R. (1976). A Simple Method for Computing the Inverse of a Numerator Relationship Matrix Used in Prediction of Breeding Values. Biometrics, 32(1), 69. doi:10.2307/2529339 es_ES
dc.description.references Ibáñez-Escriche, N., Sorensen, D., Waagepetersen, R., & Blasco, A. (2008). Selection for Environmental Variation: A Statistical Analysis and Power Calculations to Detect Response. Genetics, 180(4), 2209-2226. doi:10.1534/genetics.108.091678 es_ES
dc.description.references SanCristobal-Gaudy, M., Elsen, J.-M., Bodin, L., & Chevalet, C. (1998). Prediction of the response to a selection for canalisation of a continuous trait in animal breeding. Genetics Selection Evolution, 30(5), 423. doi:10.1186/1297-9686-30-5-423 es_ES
dc.description.references Sorensen, D., & Gianola, D. (2002). Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics. Statistics for Biology and Health. doi:10.1007/b98952 es_ES
dc.description.references SORENSEN, D., & WAAGEPETERSEN, R. (2003). Normal linear models with genetically structured residual variance heterogeneity: a case study. Genetical Research, 82(3), 207-222. doi:10.1017/s0016672303006426 es_ES
dc.description.references Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639. doi:10.1111/1467-9868.00353 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem