vignettes/vignette_cv_xgb_indica.Rmd
vignette_cv_xgb_indica.RmdFirst, we create an object of class with the function create_METData().
The user must provide as input data genotypic and phenotypic data, as
well as basic information about the field experiments (e.g. longitude,
latitude data at least), and possibly environmental covariates (if
available). These input data are checked and warning messages are given
as output if the data are not correctly formatted.
In this example, we use an indica rice dataset from Monteverde et
al. (2019), which is implemented in the package as a “toy dataset”. From
this study, a multi-year dataset of rice trials containing phenotypic
data (four traits), genotypic and environmental data for a panel of
indica genotypes across three years in a single location is available.
(more information on the datasets with
?pheno_indica,?geno_indica,?map_indica,?climate_variables_indica,?info_environments_indica).
In this case, environmental covariates by growth stage are directly
available and can be used in predictions. These data should be provided
as input in [create_METData()] using the argument
climate_variables. Hence, there is no need to retrieve with the
package any daily weather data (hence compute_climatic_ECs
argument set as FALSE).
Do not forget to indicate where the plots of clustering analyses should
be saved using the argument path_to_save.
library(learnMET)
data("geno_indica")
data("map_indica")
data("pheno_indica")
data("info_environments_indica")
data("climate_variables_indica")
METdata_indica <-
create_METData(
geno = geno_indica,
pheno = pheno_indica,
climate_variables = climate_variables_indica,
compute_climatic_ECs = F,
info_environments = info_environments_indica,
map = map_indica,
path_to_save = '~/learnMET_analyses/indica/xgb'
)
# No soil covariates provided by the user.
# Clustering of env. data starts.
# Clustering of env. data done.The goal of predict_trait_MET_cv() is to assess a given
prediction method using a certain type of cross-validation (CV) scenario
on the complete training set. The CV schemes covered by the package
correspond to those generally evaluated in related literature on MET
(Jarquı́n et al. (2014);
Jarquı́n et al. (2017);Costa-Neto,
Fritsche-Neto, and Crossa (2021)).
Here, we will use the CV2: predicting the performance of genotypes in
incomplete field trials, meaning that one can use phenotypic information
in the training set from other genotypes tested in the same environment,
or from the same genotype evalauted in other environments. We also
define the percentage of phentoypic observations which should be
included in the training set, as well as the number of
repetitions.
The function predict_trait_MET_cv() also allows to
specify a specific subset of environmental variables from the
METData$env_data object to be used in model fitting and predictions via
the argument list_env_predictors.
How does predict_trait_MET_cv() works?
When predict_trait_MET_cv() is executed, a list of
training/test splits is constructed according to the CV scheme chosen by
the user. Each training set in each sub-element of this list is
processed (e.g. standardization and removal of predictors with null
variance.
The function applies a nested CV to obtain an unbiased generalization
performance estimate, implying an inner loop CV nested in an outer CV.
The inner loop is used for model selection, i.e. hyperparameter tuning
with Bayesian optimization, while the outer loop is responsible for
evaluation of model performance. Additionnally, there is a possibility
for the user to specify a seed to allow reproducibility of analyses. If
not provided, a random one is generated and provided in the results
file.
In predict_trait_MET_cv(), predictive ability is always
calculated within the same environment (location–year combination),
regardless of how the test sets are defined according to the different
CV schemes.
Let’s use XGBoost algorithm (Chen and Guestrin
(2016))
to predict a phenotypic trait!
We recommend to use xgb_reg_1 or
xgb_reg_2 as prediction method, as these methods use
principal component as genomic features instead of all SNPs as predictor
variables. Hence, the model utilizes less features as input data and is
much faster.
Here we also indicate the number of principal components used as
genomic predictor variables, specified via the argument
num_pcs. All of environmental predictors will be used
(list_env_predictors is NULL by default.)
rescv0_1 <- predict_trait_MET_cv(
METData = METdata_indica,
trait = 'GC',
prediction_method = 'xgb_reg_1',
use_selected_markers = F,
num_pcs = 80,
lat_lon_included = F,
year_included = F,
cv_type = 'cv2',
nb_folds_cv2 = 4,
repeats_cv2 = 10,
include_env_predictors = T,
save_processing = T,
seed = 100,
path_folder = '~/INDICA/xgb/cv2'
)Note that many methods for processing data based on user-defined
parameters and machine learning-based methods are using functions from
the tidymodels (https://www.tidymodels.org/) collection of R packages
(Kuhn and Wickham (2020)).