biplots function

Biplots

biplots

## Default S3 method:
biplots(
  model,
  burnin = 0.3,
  thin = 0.2,
  pb = 0.05,
  plot_stable = TRUE,
  plot_unstable = TRUE,
  ncolors = 5
)

Arguments

• model: Output from bayes_ammi(). This should contain the results of the Bayesian AMMI model, including all sampled iterations.
• burnin: Numeric. Percentage of iterations to discard as burn-in to avoid the effects of random initializations during sampling. For example, burnin = 0.1 removes the first 10% of iterations.
• thin: Numeric. Proportion of sampled iterations to retain for analysis. For example, thin = 0.2 keeps 20% of the iterations, selecting 1 out of every 5 iterations.
• pb: Numeric. Significance levels for the contours in the plot. Smaller values of pb result in wider contours, while higher values create smaller, more specific contours.
• plot_stable: Logical. If TRUE, stable instances are highlighted in the output plot.
• plot_unstable: Logical. If TRUE, unstable instances are highlighted in the output plot.
• ncolors: Integer. Specifies the number of distinct colors to use in the plot. Adjust this to control the visual differentiation of elements in the plot.

Returns

A list with the following components:

• plot: A plot displaying the contours and final biplot values.
• contour_data: A data.frame containing the data used to create the contours.
• biplot_data: A data.frame containing the data used to recreate the final biplot values.

Examples

## Not run:

data(Maiz)

fm1 <-
  bayes_ammi(
    .data = Maiz,
    .y = y,
    .gen = entry,
    .env = site,
    .rep = rep,
    .nIter = 200
  )

library(ggplot2)

output_05 <- biplots(model = fm1, plot_stable = TRUE, plot_unstable = TRUE, pb = 0.05)
output_05

output_95 <- biplots(model = fm1, plot_stable = TRUE, plot_unstable = TRUE, pb = 0.95)
output_95
## End(Not run)

References

Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G., and Cornelius, P.L. (2011) Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model Crop Science, 51 , 1458–1469. (doi: 10.2135/cropsci2010.06.0343)

Author(s)

1. Julian Garcia Abadillo Velasco (garciaabadillo.j@ufl.edu )
2. Diego Jarquin (diego.jarquin@gmail.com )