Plots for the bpsi object

Build plots using the outputs stored in the bpsi object.

Usage

# S3 method for class 'bpsi'
plot(x, ..., category = "BPSI")

Arguments

x

An object of class bpsi.

...

currently not used

category

A string indicating which plot to build. There are currently two types of visualizations. Set "Ranks" for bar plots along each trait and "BPSI" (default) for circular bar plots multitrait.

References

Chagas, J. T. B., Dias, K. O. das G., Quintão Carneiro, V., de Oliveira, L. M. C., Nunes, N. X., Júnior, J. D. P., Carneiro, P. C. S., & Carneiro, J. E. de S. (2025). Bayesian probabilistic selection index in the selection of common bean families. Crop Science, 65(3).doi:10.1002/CSC2.70072

See also

bpsi

Author

José Tiago Barroso Chagas

Examples

# \donttest{
met_df <-
read.table("https://raw.githubusercontent.com/tiagobchagas/BPSI/refs/heads/main/Data/blues_long.txt",header = TRUE)

mod = bayes_met(data = met_df,
                gen = "gen",
                loc = "env",
                repl = NULL,
                trait = "PH",
                reg = NULL,
                year = NULL,
                res.het = TRUE,
                iter = 2000, cores = 2, chain = 4)
#> Warning: There were 1 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


mod2 = bayes_met(data = met_df,
                 gen = "gen",
                 loc = "env",
                 repl = NULL,
                 trait = "GY",
                 reg = NULL,
                 year = NULL,
                 res.het = TRUE,
                 iter = 2000, cores = 2, chain = 4)
#> 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

mod3 = bayes_met(data = met_df,
                 gen = "gen",
                 loc = "env",
                 repl =  NULL,
                 trait = "NDM",
                 reg = NULL,
                 year = NULL,
                 res.het = TRUE,
                 iter = 2000, cores = 2, chain = 4)
#> 2:                47.356 seconds (Total)
#> Chain 2: 
#> Warning: There were 133 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: 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



models=list(mod,mod2,mod3)
names(models) <- c("PH","GY","NDM")
increase = c(FALSE,TRUE,FALSE)
names(increase) <- names(models)

probs = list()
for (i in names(models)) {
  outs <- extr_outs(model = models[[i]],
                    probs = c(0.05, 0.95),
                    verbose = TRUE)
  probs[[i]] <- prob_sup(
    extr = outs,
    int = .2,
    increase = increase[[i]],
    save.df = FALSE,
    verbose = TRUE
  )

}
#> -> Posterior effects extracted
#> -> Variances extracted
#> -> Maximum posterior values extracted
#> -> Posterior predictive checks computed
#> 1 of 4000 iterations ended with a divergence (0.025%).
#> Try increasing 'adapt_delta' to remove the divergences.
#> 0 of 4000 iterations saturated the maximum tree depth of 10.
#> E-BFMI indicated no pathological behavior.
#> -> Probability of superior performance estimated
#> -> Pairwise probability of superior performance estimated
#> Process completed!
#> -> Posterior effects extracted
#> -> Variances extracted
#> -> Maximum posterior values extracted
#> -> Posterior predictive checks computed
#> 0 of 4000 iterations ended with a divergence.
#> 0 of 4000 iterations saturated the maximum tree depth of 10.
#> E-BFMI indicated no pathological behavior.
#> -> Probability of superior performance estimated
#> -> Pairwise probability of superior performance estimated
#> Process completed!
#> -> Posterior effects extracted
#> -> Variances extracted
#> -> Maximum posterior values extracted
#> -> Posterior predictive checks computed
#> 133 of 4000 iterations ended with a divergence (3.325%).
#> Try increasing 'adapt_delta' to remove the divergences.
#> 0 of 4000 iterations saturated the maximum tree depth of 10.
#> E-BFMI indicated no pathological behavior.
#> -> Probability of superior performance estimated
#> -> Pairwise probability of superior performance estimated
#> Process completed!

index = bpsi(
  problist = probs,
  increase = increase,
  int = 0.1,
  lambda = c(1, 2, 1),
  save.df = FALSE
)

plot(index, category = "BPSI")

plot(index, category = "Ranks")

# }