Plots for the probsup object

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

# \donttest{
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)
#> Warning: There were 6 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 1.06, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

outs = extr_outs(model = mod,
                 probs = c(0.05, 0.95),
                 verbose = TRUE)
#> -> Posterior effects extracted
#> -> Variances extracted
#> -> Maximum posterior values extracted
#> -> Posterior predictive checks computed
#> 
#> Divergences:
#> 6 of 4000 iterations ended with a divergence (0.15%).
#> Try increasing 'adapt_delta' to remove the divergences.
#> 
#> Tree depth:
#> 0 of 4000 iterations saturated the maximum tree depth of 10.
#> 
#> Energy:
#> E-BFMI indicated no pathological behavior.

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")
#> Are you using an object to store the outputs of this function? Enter Y/n: 
plot(results, category = "pair_stabi")

plot(results, category = "joint")

# }