ZA.AMMI {ammistability} | R Documentation |
Absolute Value of the Relative Contribution of IPCs to the Interaction
Description
ZA.AMMI
computes the Absolute Value of the Relative Contribution of
IPCs to the Interaction (\(\textrm{Z}_{\textrm{a}}\))
(Zali et al. 2012) considering all significant
interaction principal components (IPCs) in the AMMI model. Using
\(\textrm{Z}_{\textrm{a}}\), the Simultaneous Selection Index for Yield
and Stability (SSI) is also calculated according to the argument
ssi.method
.
Usage
ZA.AMMI(model, n, alpha = 0.05, ssi.method = c("farshadfar", "rao"), a = 1)
Arguments
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 |
Details
The Absolute Value of the Relative Contribution of IPCs to the Interaction (\(Za\)) (Zali et al. 2012) is computed as follows:
\[Za = \sum_{i=1}^{N'}\left | \theta_{n}\gamma_{in} \right |\]Where, \(N'\) is the number of significant IPCAs (number of IPC that were retained in the AMMI model via F tests); \(\gamma_{in}\) is the eigenvector value for \(i\)th genotype; and \(\theta_{n}\) is the percentage sum of squares explained by the \(n\)th principal component interaction effect..
Value
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.
References
Zali H, Farshadfar E, Sabaghpour SH, Karimizadeh R (2012). “Evaluation of genotype × environment interaction in chickpea using measures of stability from AMMI model.” Annals of Biological Research, 3(7), 3126–3136.
See Also
Examples
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)