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ProbBreed

ProbBreed employs Bayesian statistics to analyse multi-environment trials’ data, and uses its outputs to estimate the marginal and pairwise probabilities of superior performance and superior stability of the genotypes, as well as their conditional probability of superior performance. The method is thoroughly described at https://doi.org/10.1007/s00122-022-04041-y.

Installation

You can install the CRAN version of ProbBreed using the following command:

install.packages("ProbBreed")

Alternatively, you can install the development version of ProbBreed from GitHub with:

# install.packages("devtools")
devtools::install_github("saulo-chaves/ProbBreed")

Usage

library(ProbBreed)

Currently, ProbBreed has nine available models implemented in the bayes_met function. See ?bayes_met for more details. An examples using the soy example dataset is described below:

model = bayes_met(data = soy,
                gen = "Gen",
                loc = "Loc",
                repl = NULL,
                year = NULL,
                reg = NULL,
                res.het = TRUE,
                trait = 'Y',
                iter = 6000, cores = 4, chains = 4)

gen, loc, repl, year and reg are all column names that contain information on genotypes, environments (locations), replicates, years (or seasons) and regions (or mega-environments). The soy dataset has only adjusted means, so only gen and loc have values. res.het indicates if a per-environmental residual variance should be estimated. trait is the column in data that contain the phenotypic observations. The other arguments are specifications for model fitting: the number of iterations, cores and chains. Feel free to customize these and other options according to your necessity.

The output of this function will be an object of class stanfit, which should be used in the extr_outs function for further processing before computing the probabilities per se. This function also provides some useful diagnostics. Here is how to use it:

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

The object of class extr provided by this function contains the effects’ posterior and maximum posterior, the models’ variance components and some posterior predictive checks. Here are them:

outs$variances
#>         effect     var      sd naive.se HPD_0.05 HPD_0.95
#> 1          Gen   3.396   1.299    0.012    1.641    5.785
#> 2          Loc 248.539 127.507    1.164  115.950  481.170
#> 3   error_env1  10.029   2.728    0.025    6.363   14.965
#> 4   error_env2  28.849   7.225    0.066   18.944   42.313
#> 5   error_env3  11.657   3.424    0.031    7.222   18.003
#> 6   error_env4  18.542   4.805    0.044   12.119   27.472
#> 7   error_env5  51.517  12.889    0.118   34.229   74.745
#> 8   error_env6  15.476   4.034    0.037   10.080   23.001
#> 9   error_env7  19.270   4.841    0.044   12.608   28.188
#> 10  error_env8  21.256   5.437    0.050   13.962   31.129
#> 11  error_env9  14.802   3.832    0.035    9.678   21.811
#> 12 error_env10  13.496   5.583    0.051    6.887   23.748
#> 13 error_env11  21.969   8.980    0.082   11.698   37.850
#> 14 error_env12   7.749   3.433    0.031    3.712   14.234
#> 15 error_env13  14.443   5.877    0.054    7.553   25.375
#> 16 error_env14  10.795   4.452    0.041    5.597   19.389
outs$ppcheck
#>                   Diagnostics
#> p.val_max              0.9178
#> p.val_min              0.3604
#> p.val_median           0.7216
#> p.val_mean             0.5008
#> p.val_sd               0.5567
#> Eff_No_parameters     27.5075
#> WAIC2               2715.4563
#> mean_Rhat              1.0003
#> Eff_sample_size        0.6675

You can also the plot S3 method for some useful visualizations. For e.g., the comparison between the empirical and sampled phenotype illustrates the model’s convergence:

plot(outs)

See ?plot.extr for more details and further options.

After these two steps, everything is set to compute the probabilities. This can be done using the function prob_sup:

A basic workflow using the available data is:

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

This function generates an object of class probsup, which contains two lists: across and within. As their names suggest, the across list has the across-environments probabilities, and is suitable for a broader recommendation. Conversely, the within results are more appropriate to specific recommendations. For example, here are some probability of superior performances across and within environments:

head(results$across$perfo)
#>     ID      prob
#> 36 G36 0.9825833
#> 9  G09 0.8340833
#> 20 G20 0.8162500
#> 38 G38 0.7317500
#> 31 G31 0.6750000
#> 1  G01 0.5250833
head(results$within$perfo$gl)
#>   gen         E01         E02         E03         E04         E05         E06
#> 1 G01 0.525083333 0.525083333 0.666333333 0.525083333 0.525083333 0.525083333
#> 2 G02 0.007833333 0.007833333 0.016333333 0.007833333 0.007833333 0.007833333
#> 3 G03 0.163333333 0.163333333 0.248666667 0.163333333 0.163333333 0.163333333
#> 4 G04 0.013750000 0.013750000 0.027000000 0.013750000 0.013750000 0.013750000
#> 5 G05 0.001083333 0.001083333 0.002583333 0.001083333 0.001083333 0.001083333
#> 6 G06 0.194500000 0.194500000 0.286833333 0.194500000 0.194500000 0.194500000
#>           E07         E08         E09       E10       E11       E12       E13
#> 1 0.525083333 0.525083333 0.525083333 0.9579167 0.9579167 0.9579167 0.9579167
#> 2 0.007833333 0.007833333 0.007833333        NA        NA        NA        NA
#> 3 0.163333333 0.163333333 0.163333333        NA        NA        NA        NA
#> 4 0.013750000 0.013750000 0.013750000        NA        NA        NA        NA
#> 5 0.001083333 0.001083333 0.001083333        NA        NA        NA        NA
#> 6 0.194500000 0.194500000 0.194500000        NA        NA        NA        NA
#>         E14
#> 1 0.9579167
#> 2        NA
#> 3        NA
#> 4        NA
#> 5        NA
#> 6        NA

The S3 method plot is also available for probsup objects. Here are some of them:

• Probability of superior performance across environments

plot(results)

• Pairwise probability of superior performance across environments

plot(results, category = "pair_perfo", level = "across")

• Probability of superior stability

plot(results, category = "stabi")

• Probability of superior performance within environments

plot(results, category = "perfo", level = "within")

The grey cells are environments where a given genotype was not assessed. See more options at ?plot.probsup.

The estimation of these probabilities are strictly related to some key questions that constantly arises in plant breeding, like:

• What is the risk of recommending a selection candidate for a target population of environments?

• What is the probability of a given selection candidate having good performance if recommended to a target population of environments? And for a specific environment?

• What is the probability of a given selection candidate having better performance than a cultivar check in the target population of environments? And in specific environments?

• How probable is it that a given selection candidate performs similarly across environments?

• What are the chances that a given selection candidate is more stable than a cultivar check in the target population of environments?

• What is the probability that a given selection candidate having a superior and invariable performance across environments?

For a more detailed tutorial, see https://saulo-chaves.github.io/ProbBreed_site/.

Citation

Thank you for using ProbBreed! Please, do not forget to cite:

citation('ProbBreed')

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.