plot.probsup: Plots for the 'probsup' object

In ProbBreed: Probability Theory for Selecting Candidates in Plant Breeding

Plots for the probsup object

Description

Build plots using the outputs stored in the probsup object.

Usage

## S3 method for class 'probsup'
plot(x, ..., category = "perfo", level = "across")

Arguments

x	An object of class probsup.
...	currently not used
category	A string indicating which plot to build. See options in the Details section.
level	A string indicating the information level to be used for building the plots. Options are "across" for focusing on the probabilities across environments, or "within" to focus on the within-environment effects. Defaults to "across".

Details

The available options are:

• hpd : a caterpillar plot representing the marginal genotypic value of each genotype, and their respective highest posterior density interval (95% represented by the thick line, and 97.5% represented by the thin line). Available only if level = "across".

• perfo : if level = "across", a lollipop plot illustrating the probabilities of superior performance. If level = "within", a heatmap with the probabilities of superior performance within environments. If a model with reg and/or year is fitted, multiple plots are produced.

• stabi: a lollipop plot with the probabilities of superior stability. If a model with reg and/or year is fitted, multiple plots are produced. Available only if level = "across". Unavailable if an entry-mean model was used in bayes_met.

• pair_perfo : if level = "across", a heatmap representing the pairwise probability of superior performance (the probability of genotypes at the x-axis being superior. to those on the y-axis). If level = "within", a list of heatmaps representing the pairwise probability of superior performance within environments. If a model with reg and/or year is fitted, multiple plots (and multiple lists) are produced. Should this option is set, it is mandatory to store the outputs in an object. (e.g., pl <- plot(obj, category = "pair_perfo", level = "within")) so they can be visualized one at a time. The option level = "within" is unavailable if an entry-mean model was used in bayes_met.

• pair_stabi: a heatmap with the pairwise probabilities of superior stability (the probability of genotypes at the x-axis being more stable than those on the y-axis). If a model with reg and/or year is fitted, multiple plots are produced. Available only if level = "across". Unavailable if an entry-mean model was used in bayes_met.

• joint: a lollipop plot with the joint probabilities of superior performance and stability. Unavailable if an entry-mean model was used in bayes_met.

See Also

prob_sup

Examples

mod = bayes_met(data = maize,
                gen = "Hybrid",
                loc = "Location",
                repl = c("Rep","Block"),
                trait = "GY",
                reg = "Region",
                year = NULL,
                res.het = TRUE,
                iter = 2000, cores = 2, chain = 4)

outs = extr_outs(model = mod,
                 probs = c(0.05, 0.95),
                 verbose = TRUE)

results = prob_sup(extr = outs,
                   int = .2,
                   increase = TRUE,
                   save.df = FALSE,
                   verbose = FALSE)

plot(results, category = "hpd")
plot(results, category = "perfo", level = "across")
plot(results, category = "perfo", level = "within")
plot(results, category = "stabi")
plot(results, category = "pair_perfo", level = "across")
plwithin = plot(results, category = "pair_perfo", level = "within")
plot(results, category = "pair_stabi")
plot(results, category = "joint")

ProbBreed documentation built on April 4, 2025, 5:07 a.m.