cv_ammif | R Documentation |
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 )
.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 |
design |
The experimental design used in each environment. Defaults to
|
verbose |
A logical argument to define if a progress bar is shown.
Default is |
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.
Tiago Olivoto tiagoolivoto@gmail.com
Patterson, H.D., and E.R. Williams. 1976. A new class of resolvable incomplete block designs. Biometrika 63:83-92.
cv_ammi()
, cv_blup()
library(metan) model <- cv_ammif(data_ge2, env = ENV, gen = GEN, rep = REP, resp = EH, nboot = 5) plot(model)
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