ammiBayes: Bayesian AMMI for continuous data

In ammiBayes: Bayesian Ammi Model for Continuous Data

Bayesian AMMI for continuous data

Description

Run the AMMI Bayesian model for continuous data.

Usage

ammiBayes(Y=Y, Gen=Gen, Env=Env, Rep=Rep, 
          iterations=3000, jump=2, burn=500,
          Var.error=0.5, Var.env=0.5, Var.gen=0.5,
          chains=2)

Arguments

Y	Response variable vector
Gen	Genotype effects vector. Must be defined as factor
Env	Environmental effects vector. Must be defined as factor
Rep	Repetition vector. Must be defined as factor
iterations	Total of iterations after burnin and jumo
jump	Jump of iterations
burn	Initial burn
Var.error	Priori for the variance of error. Default is 0.5
Var.env	Priori for the variance of environment. Default is 0.5
Var.gen	Priori for the variance of genotype. Default is 0.5
chains	Number of chains. See details.

Details

The code is run in parallel for linux SO. If you are using Windows, the execution of the code will be serially.

Author(s)

Luciano A. Oliveira
Carlos P. Silva
Cristian T. E. Mendes
Alessandra Q. Silva
Joel J. Nuvunga
Marcio Balestre
Julio S. S. Bueno-Filho
Fabio M. Correa

References

OLIVEIRA,L.A.; SILVA, C. P.; NUVUNGA, J. J.; SILVA, A. Q.; BALESTRE, M. Credible Intervals for Scores in the AMMI with Random Effects for Genotype. Crop Science, v. 55, p. 465-476, 2015. doi: https://doi.org/10.2135/cropsci2014.05.0369

SILVA, C. P.; OLIVEIRA, L. A.; NUVUNGA, J. J.; PAMPLONA, A. K. A.; BALESTRE, M. A Bayesian Shrinkage Approach for AMMI Models. Plos One, v. 10, p. e0131414, 2015. doi: https://doi.org/10.1371/journal.pone.0131414.

Examples

library(ammiBayes)
data(ammiData)

Env  <- factor(ammiData$amb)
Rep <- factor(ammiData$rep)
Gen  <- factor(ammiData$gen)
Y  <- ammiData$prod

model <- ammiBayes(Y=Y, Gen=Gen, Env=Env, Rep=Rep, iter=10, 
									 burn=1, jump=2, chains=2)

summary(model)

ammiBayes documentation built on June 8, 2025, 10:02 a.m.