waas | R Documentation |
Compute the Weighted Average of Absolute Scores for AMMI analysis (Olivoto et al., 2019).
This function compute the weighted average of absolute scores, estimated 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; \mjseqnIPCA_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, considering p the number of significant
PCAs, or a declared number of PCAs. For example if prob = 0.05
, all
axis that are significant considering this probability level are used. The
number of axis can be also informed by declaring naxis = x
. This will
override the number of significant axes according to the argument codeprob.
waas( .data, env, gen, rep, resp, block = NULL, mresp = NULL, wresp = NULL, prob = 0.05, naxis = NULL, ind_anova = FALSE, 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. |
resp |
The response variable(s). To analyze multiple variables in a
single procedure a vector of variables may be used. For example |
block |
Defaults to |
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. By default, all variables in |
prob |
The p-value for considering an interaction principal component axis significant. |
naxis |
The number of IPCAs to be used for computing the WAAS index.
Default is |
ind_anova |
Logical argument set to |
verbose |
Logical argument. If |
An object of class waas
with the following items for each
variable:
individual A within-environments ANOVA considering a fixed-effect model.
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.
MeansGxE The means of genotypes in the environments
PCA Principal Component Analysis.
ANOVA Joint analysis of variance for the main effects and Principal Component analysis of the interaction effect.
Details A list summarizing the results. The following information
are showed. WgtResponse
, the weight for the response variable in
estimating WAASB, WgtWAAS
the weight for stability, Ngen
the
number of genotypes, Nenv
the number of environments, OVmean
the overall mean, Min
the minimum observed (returning the genotype and
environment), Max
the maximum observed, Max
the maximum
observed, MinENV
the environment with the lower mean, MaxENV
the environment with the larger mean observed, MinGEN
the genotype
with the lower mean, MaxGEN
the genotype with the larger.
augment: Information about each observation in the dataset. This
includes predicted values in the fitted
column, residuals in the
resid
column, standardized residuals in the stdres
column,
the diagonal of the 'hat' matrix in the hat
, and standard errors for
the fitted values in the se.fit
column.
probint The p-value for the genotype-vs-environment interaction.
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 multi-environment trials I: Combining features of AMMI and BLUP techniques. Agron. J. 111:2949-2960. doi: 10.2134/agronj2019.03.0220
waas_means()
waasb()
get_model_data()
library(metan) #===============================================================# # Example 1: Analyzing all numeric variables considering p-value# # <= 0.05 to compute the WAAS. # #===============================================================# model <- waas(data_ge, env = ENV, gen = GEN, rep = REP, resp = everything()) # Residual plot (first variable) plot(model) # Get the WAAS index get_model_data(model, "WAAS") # Plot WAAS and response variable plot_scores(model, type = 3) #===============================================================# # Example 2: Declaring the number of axis to be used for # # computing WAAS and assigning a larger weight for the response # # variable when computing the WAASBY index. # #===============================================================# model2 <- waas(data_ge, env = ENV, gen = GEN, rep = REP, resp = everything(), naxis = 1, # Only to compare with PC1 wresp = 60) # Get the WAAS index (it will be |PC1|) get_model_data(model2) # Get values for IPCA1 get_model_data(model2, "PC1") #===============================================================# # Example 3: Analyzing GY and HM assuming a random-effect model.# # Smaller values for HM and higher values for GY are better. # # To estimate WAASBY, higher weight for the GY (60%) and lower # # weight for HM (40%) are considered for mean performance. # #===============================================================# model3 <- waas(data_ge, env = ENV, gen = GEN, rep = REP, resp = c(GY, HM), mresp = c("h, l"), wresp = c(60, 40)) # Get the ranks for the WAASY index get_model_data(model3, what = "OrWAASY")
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