cv_blup: Cross-validation procedure

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/cv_blup.R

Description

[Stable]

Cross-validation for blup prediction.

This function provides a cross-validation procedure for mixed models using replicate-based data. By default, complete blocks are randomly selected within each environment. In each iteration, the original dataset is split up into two datasets: training and validation data. The 'training' set has all combinations (genotype x environment) with R - 1 replications. The 'validation' set has the remaining replication. The estimated values are compared with the 'validation' data and the Root Means Square Prediction Difference (Olivoto et al. 2019) is computed. At the end of boots, a list is returned.

Usage

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cv_blup(
  .data,
  env,
  gen,
  rep,
  resp,
  block = NULL,
  nboot = 200,
  random = "gen",
  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.

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. See how fixed and random effects are considered, see the section Details.

nboot

The number of resamples to be used in the cross-validation. Defaults to 200

random

The effects of the model assumed to be random. See Details for more information.

verbose

A logical argument to define if a progress bar is shown. Default is TRUE.

Details

Six models may be fitted depending upon the values in block and random arguments.

IMPORTANT: An error is returned if any combination of genotype-environment has a different number of replications than observed in the trial.

Value

An object of class cv_blup 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.

Author(s)

Tiago Olivoto tiagoolivoto@gmail.com

References

Olivoto, T., A.D.C. L\'ucio, 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. 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.

Mohring, J., E. Williams, and H.-P. Piepho. 2015. Inter-block information: to recover or not to recover it? TAG. Theor. Appl. Genet. 128:1541-54. doi: 10.1007/s00122-015-2530-0

See Also

cv_ammi(), cv_ammif()

Examples

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library(metan)
model <- cv_blup(data_ge,
                 env = ENV,
                 gen = GEN,
                 rep = REP,
                 resp = GY,
                 nboot = 5)

metan documentation built on Nov. 10, 2021, 9:11 a.m.