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.
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")
::install_github("saulo-chaves/ProbBreed") devtools
library(ProbBreed)
Currently, ProbBreed
has nine available models
implemented in the bayes_met
function. See
?bayes_met
for more details. An examples using the
maize
example dataset is described below:
= bayes_met(data = maize,
mod gen = "Hybrid",
loc = "Location",
repl = c("Rep","Block"),
trait = "GY",
reg = "Region",
year = NULL,
res.het = TRUE,
iter = 4000, cores = 4, chain = 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 maize
has no multi-year information, so year = NULL
.
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:
= extr_outs(model = mod,
outs 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:
$variances
outs#> effect var sd naive.se HPD_0.05 HPD_0.95
#> 1 Rep 0.036 0.040 0.000 0.001 0.108
#> 2 Block 0.214 0.049 0.001 0.137 0.298
#> 3 Hybrid 0.217 0.075 0.001 0.118 0.353
#> 4 Location 7.995 3.864 0.043 3.773 15.124
#> 5 Hybrid:Location 0.369 0.068 0.001 0.260 0.485
#> 6 Region 4.712 18.433 0.206 0.013 17.110
#> 7 Hybrid:Region 0.056 0.041 0.000 0.002 0.130
#> 8 error_env1 0.879 0.200 0.002 0.595 1.236
#> 9 error_env2 0.945 0.257 0.003 0.593 1.418
#> 10 error_env3 1.398 0.315 0.004 0.961 1.965
#> 11 error_env4 0.591 0.147 0.002 0.388 0.862
#> 12 error_env5 1.062 0.240 0.003 0.732 1.494
#> 13 error_env6 1.453 0.327 0.004 0.980 2.039
#> 14 error_env7 0.285 0.073 0.001 0.186 0.420
#> 15 error_env8 2.000 0.476 0.005 1.311 2.847
#> 16 error_env9 0.581 0.165 0.002 0.362 0.884
#> 17 error_env10 0.628 0.150 0.002 0.423 0.905
#> 18 error_env11 1.254 0.288 0.003 0.851 1.779
#> 19 error_env12 0.456 0.116 0.001 0.296 0.670
#> 20 error_env13 0.732 0.179 0.002 0.484 1.062
#> 21 error_env14 1.834 0.401 0.004 1.264 2.556
#> 22 error_env15 0.826 0.189 0.002 0.567 1.172
#> 23 error_env16 1.813 0.422 0.005 1.221 2.584
$ppcheck
outs#> Diagnostics
#> p.val_max 0.3390
#> p.val_min 0.3222
#> p.val_median 0.5479
#> p.val_mean 0.5011
#> p.val_sd 0.5294
#> Eff_No_parameters 184.3898
#> WAIC2 3924.8912
#> mean_Rhat 1.0005
#> Eff_sample_size 0.8484
You can also the plot
S3 method for some useful
visualizations. For e.g., the comparison between the empirical and
sampled phenotype illustrates the model’s convergence:
plot(outs)
See ?plot.extr
for more details and further options.
After these two steps, everything is set to compute the
probabilities. This can be done using the function
prob_sup
:
A basic workflow using the available data is:
= prob_sup(extr = outs,
results int = .2,
increase = TRUE,
save.df = FALSE,
verbose = TRUE)
This function generates an object of class probsup
,
which contains two lists: across
and within
.
As their names suggest, the across
list has the
across-environments probabilities, and is suitable for a broader
recommendation. Conversely, the within
results are more
appropriate to specific recommendations. For example, here are some
probability of superior performances across and within environments:
head(results$across$perfo)
#> ID prob
#> 36 G9 0.997500
#> 1 G1 0.920000
#> 22 G29 0.788500
#> 24 G30 0.629375
#> 5 G13 0.616250
#> 35 G8 0.569000
head(results$within$perfo$gl)
#> gen E1 E10 E11 E12 E13 E14 E15 E16
#> 1 G1 0.984000 0.277875 0.386375 0.434125 0.262625 0.953625 0.311625 0.972625
#> 2 G10 0.010250 0.000875 0.001750 0.002500 0.026500 0.068000 0.000125 0.043000
#> 3 G11 0.006250 0.100125 0.110625 0.004875 0.608000 0.111875 0.745375 0.094750
#> 4 G12 0.153875 0.093000 0.045125 0.058750 0.064375 0.302125 0.173250 0.569250
#> 5 G13 0.202875 0.194625 0.136375 0.134125 0.070375 0.595250 0.807125 0.068500
#> 6 G14 0.000000 0.006750 0.111375 0.156500 0.030375 0.070875 0.000375 0.023625
#> E2 E3 E4 E5 E6 E7 E8 E9
#> 1 0.899000 0.619250 0.907125 0.522125 0.302500 0.005500 0.863875 0.385625
#> 2 0.093000 0.199250 0.001625 0.042875 0.024375 0.001750 0.000250 0.044000
#> 3 0.020625 0.031625 0.014250 0.110000 0.340375 0.017375 0.049625 0.229500
#> 4 0.413625 0.602125 0.193125 0.389125 0.430500 0.819750 0.160375 0.313000
#> 5 0.234375 0.705375 0.684750 0.606250 0.825625 0.467125 0.118750 0.537375
#> 6 0.001625 0.048000 0.122625 0.114750 0.631000 0.025000 0.037875 0.065000
The S3 method plot
is also available for
probsup
objects. Here are some of them:
plot(results)
plot(results, category = "pair_perfo", level = "across")
plot(results, category = "stabi")
plot(results, category = "perfo", level = "within")
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/.
Thank you for using ProbBreed
! Please, do not forget to
cite:
citation('ProbBreed')