MASI.AMMI {ammistability} | R Documentation |

`MASI.AMMI`

computes the Modified AMMI Stability Index (MASI)
(Ajay et al. 2018) from a modified formula of
AMMI Stability Index (ASI)
(Jambhulkar et al. 2014; Jambhulkar et al. 2015; Jambhulkar et al. 2017).
Unlike ASI, MASI calculates stability value considering all significant
interaction principal components (IPCs) in the AMMI model. Using MASI, the
Simultaneous Selection Index for Yield and Stability (SSI) is also calculated
according to the argument `ssi.method`

.

MASI.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 Modified AMMI Stability Index (\(MASI\)) (Ajay et al. 2018) is computed as follows:

\[MASI = \sqrt{ \sum_{n=1}^{N'} PC_{n}^{2} \times \theta_{n}^{2}}\]Where, \(PC_{n}\) are the scores of \(n\)th IPC; and \(\theta_{n}\) is the percentage sum of squares explained by the \(n\)th principal component interaction effect.

A data frame with the following columns:

`MASI` |
The MASI values. |

`SSI` |
The computed values of simultaneous selection index for yield and stability. |

`rMASI` |
The ranks of MASI 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.

Ajay BC, Aravind J, Abdul Fiyaz R, Bera SK, Kumar N, Gangadhar K, Kona P (2018).
“Modified AMMI Stability Index (MASI) for stability analysis.”
*ICAR-DGR Newsletter*, **18**, 4–5.

Jambhulkar NN, Bose LK, Pande K, Singh ON (2015).
“Genotype by environment interaction and stability analysis in rice genotypes.”
*Ecology, Environment and Conservation*, **21**(3), 1427–1430.
http://www.envirobiotechjournals.com/article_abstract.php?aid=6346&iid=200&jid=3.

Jambhulkar NN, Bose LK, Singh ON (2014).
“AMMI stability index for stability analysis.”
In Mohapatra T (ed.), *CRRI Newsletter, January-March 2014*, volume 35(1), 15.
Central Rice Research Institute, Cuttack, Orissa.
http://www.crri.nic.in/CRRI_newsletter/crnl_jan_mar_14_web.pdf.

Jambhulkar NN, Rath NC, Bose LK, Subudhi HN, Biswajit M, Lipi D, Meher J (2017).
“Stability analysis for grain yield in rice in demonstrations conducted during rabi season in India.”
*Oryza*, **54**(2), 236–240.
doi: 10.5958/2249-5266.2017.00030.3, https://doi.org/10.5958/2249-5266.2017.00030.3.

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) MASI.AMMI(model) # With n = 4 and default ssi.method (farshadfar) MASI.AMMI(model, n = 4) # With default n (N') and ssi.method = "rao" MASI.AMMI(model, ssi.method = "rao") # Changing the ratio of weights for Rao's SSI MASI.AMMI(model, ssi.method = "rao", a = 0.43) # ASI.AMMI same as MASI.AMMI with n = 2 a <- ASI.AMMI(model) b <- MASI.AMMI(model, n = 2) identical(a$ASI, b$MASI)

[Package *ammistability* version 0.1.2 Index]