cv_ammif function

Cross-validation procedure

Cross-validation for estimation of all AMMI-family models

cv_ammif provides a complete cross-validation of replicate-based data using AMMI-family models. By default, the first validation is carried out considering the AMMIF (all possible axis used). Considering this model, the original dataset is split up 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 for each member of the AMMI-family model 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_ammif(
  .data,
  env,
  gen,
  rep,
  resp,
  nboot = 200,
  block,
  design = "RCBD",
  verbose = TRUE
)

Arguments

• .data: The dataset containing the columns related to Environments, Genotypes, replication/block and response variable(s).
• env: The name of the column that contains the levels of the environments.
• gen: The name of the column that contains the levels of the genotypes.
• rep: The name of the column that contains the levels of the replications/blocks. AT LEAST THREE REPLICATES ARE REQUIRED TO PERFORM THE CROSS-VALIDATION .
• resp: The response variable.
• nboot: The number of resamples to be used in the cross-validation. Defaults to 200.
• block: 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.
• design: The experimental design used in each environment. Defaults to RCBD (Randomized complete Block Design). For Completely Randomized Designs inform design = 'CRD'.
• verbose: A logical argument to define if a progress bar is shown. Default is TRUE.

Returns

An object of class cv_ammif 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.

Examples

library(metan)
model <- cv_ammif(data_ge2,
                  env = ENV,
                  gen = GEN,
                  rep = REP,
                  resp = EH,
                  nboot = 5)
plot(model)

References

Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92.

See Also

cv_ammi(), cv_blup()

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com