diagnosis.ammiBayes {ammiBayes}

Bayesian AMMI for ordinal data

Description

Extract the MCMC chain for diagnosis

Usage

diagnosis.ammiBayes(x, pars=NULL)

Arguments

x

An object of class ammiBayes

pars

It should be set, such as "Genotype", "Rep", "L", "Gen.PC1", "Gen.PC2", "Env.PC1", "Env.PC2", "Comp.var". See details

Details

The output is compatible for diagnosis with the coda and bayesplot packages. Examples can be seen on the website: bayesplot

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

# Not run
library(ammiBayes)
library(bayesplot)
library(ggpubr)

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=1000, burn=10, jump=2, chains=2)

gen.diagnosis <- diagnosis.ammiBayes(model, pars="Genotype")


mcmc_trace(gen.diagnosis)
mcmc_dens_overlay(gen.diagnosis)
mcmc_areas(gen.diagnosis)

dens <- bayesplot::mcmc_dens_overlay(gen.diagnosis)
trac <- bayesplot::mcmc_trace(gen.diagnosis, facet_args=list(ncol=1))

ggpubr::ggarrange(trac,dens, common.legend=TRUE)