ZA.AMMI | R Documentation |
ZA.AMMI
computes the Absolute Value of the Relative Contribution of
IPCs to the Interaction (\mjseqn\textrmZ_\textrma)
\insertCitezali_evaluation_2012ammistability considering all significant
interaction principal components (IPCs) in the AMMI model. Using
\mjseqn\textrmZ_\textrma, the Simultaneous Selection Index for Yield
and Stability (SSI) is also calculated according to the argument
ssi.method
. \loadmathjax
ZA.AMMI(model, n, alpha = 0.05, ssi.method = c("farshadfar", "rao"), a = 1)
model |
The AMMI model (An object of class |
n |
The number of principal components to be considered for computation. The default value is the number of significant IPCs. |
alpha |
Type I error probability (Significance level) to be considered to identify the number of significant IPCs. |
ssi.method |
The method for the computation of simultaneous selection
index. Either |
a |
The ratio of the weights given to the stability components for
computation of SSI when |
The Absolute Value of the Relative Contribution of IPCs to the Interaction (\mjseqnZa) \insertCitezali_evaluation_2012ammistability is computed as follows:
\mjsdeqnZa = \sum_i=1^N'\left | \theta_n\gamma_in \right |
Where, \mjseqnN' is the number of significant IPCAs (number of IPC that were retained in the AMMI model via F tests); \mjseqn\gamma_in is the eigenvector value for \mjseqnith genotype; and \mjseqn\theta_n is the percentage sum of squares explained by the \mjseqnnth principal component interaction effect..
A data frame with the following columns:
Za |
The Za values. |
SSI |
The computed values of simultaneous selection index for yield and stability. |
rZa |
The ranks of Za values. |
rY |
The ranks of the mean yield of genotypes. |
means |
The mean yield of the genotypes. |
The names of the genotypes are indicated as the row names of the data frame.
AMMI
, SSI
library(agricolae)
data(plrv)
# AMMI model
model <- with(plrv, AMMI(Locality, Genotype, Rep, Yield, console = FALSE))
# ANOVA
model$ANOVA
# IPC F test
model$analysis
# Mean yield and IPC scores
model$biplot
# G*E matrix (deviations from mean)
array(model$genXenv, dim(model$genXenv), dimnames(model$genXenv))
# With default n (N') and default ssi.method (farshadfar)
ZA.AMMI(model)
# With n = 4 and default ssi.method (farshadfar)
ZA.AMMI(model, n = 4)
# With default n (N') and ssi.method = "rao"
ZA.AMMI(model, ssi.method = "rao")
# Changing the ratio of weights for Rao's SSI
ZA.AMMI(model, ssi.method = "rao", a = 0.43)
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