Cross-validation for estimation of AMMI models
THe original dataset is split into two datasets: training set and validation
set. The 'training' set has all combinations (genotype x environment) with
N-1 replications. The 'validation' set has the remaining replication. The
splitting of the dataset into modeling and validation sets depends on the
design informed. For Completely Randomized Block Design (default), and
alpha-lattice design (declaring block
arguments), complete replicates
are selected within environments. The remained replicate serves as validation
data. If design = 'RCD'
is informed, completely randomly samples are
made for each genotype-by-environment combination (Olivoto et al. 2019). The
estimated values considering naxis
-Interaction Principal Component
Axis are compared with the 'validation' data. The Root Mean Square Prediction
Difference (RMSPD) is computed. At the end of boots, a list is returned.
IMPORTANT: If the data set is unbalanced (i.e., any genotype missing in any environment) the function will return an error. An error is also observed if any combination of genotype-environment has a different number of replications than observed in the trial.
cv_ammi(
.data,
env,
gen,
rep,
resp,
block = NULL,
naxis = 2,
nboot = 200,
design = "RCBD",
verbose = TRUE
)
An object of class cv_ammi
with the following items: *
RMSPD: A vector with nboot-estimates of the Root Mean Squared
Prediction Difference between predicted and validating data.
RMSPDmean: The mean of RMSPDmean estimates.
Estimated: A data frame that contain the values (predicted, observed, validation) of the last loop.
Modeling: The dataset used as modeling data in the last loop
Testing: The dataset used as testing data in the last loop.
The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s).
The name of the column that contains the levels of the environments.
The name of the column that contains the levels of the genotypes.
The name of the column that contains the levels of the replications/blocks. AT LEAST THREE REPLICATES ARE REQUIRED TO PERFORM THE CROSS-VALIDATION.
The response variable.
Defaults to NULL
. In this case, a randomized complete
block design is considered. If block is informed, then a resolvable
alpha-lattice design (Patterson and Williams, 1976) is employed.
All effects, except the error, are assumed to be fixed.
The number of axis to be considered for estimation of GE effects.
The number of resamples to be used in the cross-validation. Defaults to 200.
The experimental design. Defaults to RCBD
(Randomized
complete Block Design). For Completely Randomized Designs inform
design = 'CRD'
.
A logical argument to define if a progress bar is shown.
Default is TRUE
.
Tiago Olivoto tiagoolivoto@gmail.com
Olivoto, T., A.D.C. Lúcio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019. Mean performance and stability in multi-environment trials I: Combining features of AMMI and BLUP techniques. Agron. J. 111:2949-2960. tools:::Rd_expr_doi("10.2134/agronj2019.03.0220")
Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92.
cv_ammif()
, cv_blup()
# \donttest{
library(metan)
model <- cv_ammi(data_ge,
env = ENV,
gen = GEN,
rep = REP,
resp = GY,
nboot = 5,
naxis = 2)
# }
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