ammiBayes.mean.plot {ammiBayes}

Plot ammiBayes object

Description

Plot the means for the ammiBayes object

Usage

ammiBayes.mean.plot(model, pars.gen=NULL, pars.env=NULL,
              gen.labels=NULL, env.labels=NULL, 
              col.text.gen="darkgreen", col.text.env="red",
              ylim=NULL, xlim=NULL, cex.env=1, cex.gen=1,
              xlab,	ylab, col.grid="grey", lty.grid=2, lwd.grid=1)

Arguments

model

An object of the ammiBayes class

pars.gen

An optional character vector of genotype names. If pars is omitted all genotypes are included.

pars.env

An optional character vector of environment names. If pars is omitted all environments are included.

gen.labels

Optional vector for the name of the genotypes

env.labels

Optional vector for the name of the environments

col.text.gen

Define the color of genotype names

col.text.env

Define the color of environment names

ylim

Define the limites applied to the y-axis

xlim

Define the limites applied to the x-axis

cex.env

Scale for the font size of the environment names. Default is 1

cex.gen

Scale for the font size of the genotype names. Default is 1

xlab

Label for the x-axis

ylab

Label for the y-axis

col.grid

Define the color for the grid. Default is "grey"

lty.grid

Line type of grid

lwd.grid

Line width of grid

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.

See Also

lattice

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)

ammiBayes.mean.plot(model)