Description Usage Arguments Value Author(s) References See Also Examples
Compute the Weighted Average of Absolute Scores (Olivoto et al., 2019) based on means for genotypeenvironment data as follows: \loadmathjax \mjsdeqnWAAS_i = \sum_k = 1^p IPCA_ik \times EP_k/ \sum_k = 1^pEP_k
where \mjseqnWAAS_i is the weighted average of absolute scores of the
ith genotype; \mjseqnPCA_ik is the score of the ith genotype
in the kth IPCA; and \mjseqnEP_k is the explained variance of the kth
IPCA for k = 1,2,..,p, where p is the number of IPCAs that
explain at least an amount of the genotypeinteraction variance declared in
the argument min_expl_var
.
1 2 3 4 5 6 7 8 9 10 11 
.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. 
resp 
The response variable(s). To analyze multiple variables in a
single procedure a vector of variables may be used. For example 
mresp 
The new maximum value after rescaling the response variable. By
default, all variables in 
wresp 
The weight for the response variable(s) for computing the WAASBY
index. Must be a numeric vector of the same length of 
min_expl_var 
The minimum explained variance. Defaults to 85.
Interaction Principal Compoment Axis are iteractively retained up to the
explained variance (eigenvalues in the singular value decomposition of the
matrix with the interaction effects) be greather than or equal to

verbose 
Logical argument. If 
... 
Arguments passed to the function

An object of class waas_means
with the following items for each
variable:
model A data frame with the response variable, the scores of all Principal Components, the estimates of Weighted Average of Absolute Scores, and WAASY (the index that consider the weights for stability and productivity in the genotype ranking.
ge_means A tbl_df containing the genotypeenvironment means.
ge_eff A gxe matrix containing the genotypeenvironment effects.
eigenvalues The eigenvalues from the singular value decomposition of the matrix withe the genotypeenvironment interaction effects.
proportion The proportion of the variance explained by each IPCA.
cum_proportion The cumulative proportion of the variance explained.
Tiago Olivoto tiagoolivoto@gmail.com
Olivoto, T., A.D.C. L\'ucio, J.A.G. da silva, V.S. Marchioro, V.Q. de Souza, and E. Jost. 2019a. Mean performance and stability in multienvironment trials I: Combining features of AMMI and BLUP techniques. Agron. J. 111:29492960. doi: 10.2134/agronj2019.03.0220
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  library(metan)
# Data with replicates
model < waas(data_ge,
env = ENV,
gen = GEN,
rep = REP,
resp = everything())
# Based on means of genotypeenvironment data
data_means < means_by(data_ge, ENV, GEN)
model2 < waas_means(data_ge,
env = ENV,
gen = GEN,
resp = everything())
# The index WAAS
get_model_data(model, what = "OrWAAS")
get_model_data(model2, what = "OrWAAS")

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