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Probabilities of superior performance and stability

Source: R/prob_sup.R
prob_sup.Rd

This function estimates the probabilities of superior performance and stability across environments, and probabilities of superior performance within environments.

Usage

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

Arguments

extr

An object of class extr, obtained from the extr_outs function

int

A numeric representing the selection intensity (between 0 and 1)

increase

Logical.TRUE (default) if the selection is for increasing the trait value, FALSE otherwise.

save.df

Logical. Should the data frames be saved in the work directory? TRUE for saving, FALSE (default) otherwise.

verbose

A logical value. If TRUE, the function will indicate the completed steps. Defaults to FALSE.

Value

The function returns an object of class probsup, which contains two lists, one with the across-environments probabilities, and another with the within-environments probabilities. If an entry-mean model was used in ProbBreed::bayes_met, only the across list will be available.

The across list has the following elements:

  • g_hpd: Highest posterior density (HPD) of the posterior genotypic main effects.

  • perfo: the probabilities of superior performance.

  • pair_perfo: the pairwise probabilities of superior performance.

  • stabi: a list with the probabilities of superior stability. It contains the data frames gl, gm (when reg is not NULL) and gt (when year is not NULL). Unavailable if an entry-mean model was used in bayes_met.

  • pair_stabi: a list with the pairwise probabilities of superior stability. It contains the data frames gl, gm (when reg is not NULL) and gt (when year is not NULL). Unavailable if an entry-mean model was used in bayes_met.

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

The within list has the following elements:

  • perfo: a list of data frames containing the probabilities of superior performance within locations (gl), regions (gm) and years (gt).

  • pair_perfo: lists with the pairwise probabilities of superior performance within locations (gl), regions (gm) and years (gt).

Details

Probabilities provide the risk of recommending a selection candidate for a target population of environments or for a specific environment. prob_sup computes the probabilities of superior performance and the probabilities of superior stability:

  • Probability of superior performance

Let \(\Omega\) represent the subset of selected genotypes based on their performance across environments. A given genotype \(j\) will belong to \(\Omega\) if its genotypic marginal value (\(\hat{g}_j\)) is high or low enough compared to its peers. prob_sup leverages the Monte Carlo discretized sampling from the posterior distribution to emulate the occurrence of \(S\) trials. Then, the probability of the \(j^{th}\) genotype belonging to \(\Omega\) is the ratio of success (\(\hat{g}_j \in \Omega\)) events and the total number of sampled events, as follows:

$$Pr\left(\hat{g}_j \in \Omega \vert y \right) = \frac{1}{S}\sum_{s=1}^S{I\left(\hat{g}_j^{(s)} \in \Omega \vert y\right)}$$

where \(S\) is the total number of samples \(\left(s = 1, 2, ..., S \right)\), and \(I\left(g_j^{(s)} \in \Omega \vert y\right)\) is an indicator variable that can assume two values: (1) if \(\hat{g}_j^{(s)} \in \Omega\) in the \(s^{th}\) sample, and (0) otherwise. \(S\) is conditioned to the number of iterations and chains previously set at bayes_met.

Similarly, the within-environment probability of superior performance can be applied to individual environments. Let \(\Omega_k\) represent the subset of superior genotypes in the \(k^{th}\) environment, so that the probability of the \(j^{th} \in \Omega_k\) can calculated as follows:

$$Pr\left(\hat{g}_{jk} \in \Omega_k \vert y\right) = \frac{1}{S} \sum_{s=1}^S I\left(\hat{g}_{jk}^{(s)} \in \Omega_k \vert y\right)$$

where \(I\left(\hat{g}_{jk}^{(s)} \in \Omega_k \vert y\right)\) is an indicator variable mapping success (1) if \(\hat{g}_{jk}^{(s)}\) exists in \(\Omega_k\), and failure (0) otherwise, and \(\hat{g}_{jk}^{(s)} = \hat{g}_j^{(s)} + \widehat{ge}_{jk}^{(s)}\). Note that when computing within-environment probabilities, we are accounting for the interaction of the \(j^{th}\) genotype with the \(k^{th}\) environment.

The pairwise probabilities of superior performance can also be calculated across or within environments. This metric assesses the probability of the \(j^{th}\) genotype being superior to another experimental genotype or a commercial check. The calculations are as follows, across and within environments, respectively:

$$Pr\left(\hat{g}_{j} > \hat{g}_{j^\prime} \vert y\right) = \frac{1}{S} \sum_{s=1}^S I\left(\hat{g}_{j}^{(s)} > \hat{g}_{j^\prime}^{(s)} \vert y\right)$$

or

$$Pr\left(\hat{g}_{jk} > \hat{g}_{j^\prime k} \vert y\right) = \frac{1}{S} \sum_{s=1}^S I\left(\hat{g}_{jk}^{(s)} > \hat{g}_{j^\prime k}^{(s)} \vert y\right)$$

These equations are set for when the selection direction is positive. If increase = FALSE, \(>\) is simply switched by \(<\).

  • Probability of superior stability

This probability makes a direct analogy with the method of Shukla (1972): a stable genotype is the one that has a low genotype-by-environment interaction variance \([var(\widehat{ge})]\). Using the same probability principles previously described, the probability of superior stability is given as follows:

$$Pr \left[var \left(\widehat{ge}_{jk}\right) \in \Omega \vert y \right] = \frac{1}{S} \sum_{s=1}^S I\left[var \left(\widehat{ge}_{jk}^{(s)} \right) \in \Omega \vert y \right]$$

where \(I\left[var \left(\widehat{ge}_{jk}^{(s)} \right) \in \Omega \vert y \right]\) indicates if \(var\left(\widehat{ge}_{jk}^{(s)}\right)\) exists in \(\Omega\) (1) or not (0). Pairwise probabilities of superior stability are also possible in this context:

$$Pr \left[var \left(\widehat{ge}_{jk} \right) < var\left(\widehat{ge}_{j^\prime k} \right) \vert y \right] = \frac{1}{S} \sum_{s=1}^S I \left[var \left(\widehat{ge}_{jk} \right)^{(s)} < var \left(\widehat{ge}_{j^\prime k} \right)^{(s)} \vert y \right]$$

Note that \(j\) will be superior to \(j^\prime\) if it has a lower variance of the genotype-by-environment interaction effect. This is true regardless if increase is set to TRUE or FALSE.

The joint probability independent events is the product of the individual probabilities. The estimated genotypic main effects and the variances of GEI effects are independent by design, thus the joint probability of superior performance and stability as follows:

$$Pr \left[\hat{g}_j \in \Omega \cap var \left(\widehat{ge}_{jk} \right) \in \Omega \right] = Pr \left(\hat{g}_j \in \Omega \right) \times Pr \left[var \left(\widehat{ge}_{jk} \right) \in \Omega \right]$$

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

  • 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?

More details about the usage of prob_sup, as well as the other function of the ProbBreed package can be found at https://saulo-chaves.github.io/ProbBreed_site/.

References

Dias, K. O. G, Santos J. P. R., Krause, M. D., Piepho H. -P., Guimarães, L. J. M., Pastina, M. M., and Garcia, A. A. F. (2022). Leveraging probability concepts for cultivar recommendation in multi-environment trials. Theoretical and Applied Genetics, 133(2):443-455. doi:10.1007/s00122-022-04041-y

Shukla, G. K. (1972) Some statistical aspects of partioning genotype environmental componentes of variability. Heredity, 29:237-245. doi:10.1038/hdy.1972.87

See also

plot.probsup

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 2 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 1.18, 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:
#> 0 of 4000 iterations ended with a divergence.
#> 
#> Tree depth:
#> 0 of 4000 iterations saturated the maximum tree depth of 10.
#> 
#> Energy:
#> E-BFMI indicated possible pathological behavior:
#>   Chain 2: E-BFMI = 0.161
#>   Chain 4: E-BFMI = 0.155
#> E-BFMI below 0.2 indicates you may need to reparameterize your model.

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