bam_app(
launch.browser = TRUE
)\[ \newcommand{\vy}{\mathbf{y}} \newcommand{\vx}{\mathbf{x}} \newcommand{\vb}{\mathbf{b}} \newcommand{\ve}{\mathbf{e}} \newcommand{\vone}{\mathbf{1}} \newcommand{\vbeta}{\boldsymbol{\beta}} \newcommand{\vmu}{\boldsymbol{\mu}} \newcommand{\mX}{\mathbf{X}} \newcommand{\mZ}{\mathbf{Z}} \newcommand{\mV}{\mathbf{V}} \newcommand{\mI}{\mathbf{I}} \newcommand{\mJ}{\mathbf{J}} \newcommand{\mSigma}{\boldsymbol{\Sigma}} \newcommand{\mY}{\mathbf{Y}} \newcommand{\mK}{\mathbf{K}} \newcommand{\mH}{\mathbf{H}} \newcommand{\va}{\boldsymbol{\alpha}} \newcommand{\vg}{\boldsymbol{\gamma}} \newcommand{\vt}{\boldsymbol{\tau}} \newcommand{\vd}{\boldsymbol{\delta}} \newcommand{\vtheta}{\boldsymbol{\theta}} \newcommand{\vv}{\mathbf{v}} \newcommand{\E}{\mathrm{E}} \newcommand{\Var}{\mathrm{Var}} \newcommand{\Cov}{\mathrm{Cov}} \newcommand{\tr}{\operatorname{tr}} \]
1 Abstract
bam_app() opens an application in the web browser that runs every analysis of the package without code. This vignette describes its pages and how it keeps its results reproducible.
2 Starting the application
The application needs the suggested packages shiny, bslib and DT. magick with WebP support is optional: with it the plots are sent to the browser as lossless WebP images, about half the size of the same PNG.
isTRUE(tryCatch(requireNamespace("magick", quietly = TRUE) && isTRUE(magick::magick_config()$webp),
error = function(e) FALSE))[1] TRUETRUE means WebP is available. A magick installation that no longer loads, because the ImageMagick libraries of the system were removed or upgraded, gives FALSE, and the application then sends PNG images; reinstalling magick restores WebP.
3 The pages
| Page | What it does |
|---|---|
| Home | what the application does and the order of the pages |
| Data | the Maiz example or a CSV file with one row per plot; choice of the response, genotype, environment and replicate columns |
| Model | AMMI or GGE, number of terms, common or environment-specific variances, iterations, burn-in, thinning, chains, seed and priors; then the summary, traces and densities |
| Biplot | the biplot with HPD regions, for genotypes, environments or both |
| Decisions | winners, probabilities of superior performance, ranks, stability and predictions |
| Stability | Bayesian Eberhart-Russell, Finlay-Wilkinson and Shukla models; classical and AMMI-based stability indices |
| Model choice | posterior predictive checks and a comparison of the fits of the session |
Every result page has the same tabs:
-
Interpretation gives the plain-language reading of
bam_interpret(), with a button that copies it, and the sources of the method. - Plot shows the figure, which can be saved as PNG, PDF or WebP.
- Table shows the numbers, rounded, in a sortable table with buttons to copy them or save them as CSV or Excel.
- Output shows the printed result as text, with a button that copies it.
The model page has a further tab, R code, with the code that reproduces the fit outside the application.
The model page after a fit, with the interpretation tab open:
The plot tab of the Bayesian Eberhart-Russell model on the Stability menu:
The screenshots are lossless WebP in the HTML version: 62 KB for the first, against 147 KB for the lossy JPEG of the PDF version.
4 Reproducibility
For the default settings with environment-specific variances, the R code tab shows:
settings <-
list(
.y = "y"
, .gen = "entry"
, .env = "site"
, .rep = "rep"
, .nIter = 3000
, .k = 2
, .burnin = 500
, .thin = 1
, .chains = 2
, .priors = bam_priors(
mu_mean = 0
, mu_var = 1e+8
, tau_mean = 0
, tau_var = 1e+8
, delta_mean = 0
, delta_var = 1e+8
, lambda_mean = 0
, lambda_var = 1e+8
, nu = 1
, s2 = NULL
)
, .level = 0.95
, .seed = 1
, .model = "AMMI"
, .het = TRUE
)
cat(bayesammi:::.bam_app_code(a = settings, source = "maiz"))library(bayesammi)
data(Maiz)
fit <-
bayes_ammi(
.data = Maiz
, .y = y
, .gen = entry
, .env = site
, .rep = rep
, .nIter = 3000
, .k = 2
, .burnin = 500
, .thin = 1
, .chains = 2
, .priors = bam_priors(lambda_var = 1e+08, nu = 1)
, .level = 0.95
, .seed = 1
, .align = TRUE
, .model = "AMMI"
, .het = TRUE
, .waic = TRUE
)
fit
bam_interpret(
x = fit
, higher_better = TRUE
, top = 5
)The seed fixes the draws, so the code returns the same numbers as the application.
5 Deploying
bam_app_object() returns the application object without running it, for a server:
# app.R on a Shiny server
bayesammi::bam_app_object()6 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(4), 1458–1469. doi:10.2135/cropsci2010.06.0343