ammiBayes {ammiBayes}

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)