Description Usage Arguments Details Value References See Also Examples

`MASI.AMMI`

computes the Modified AMMI Stability Index (MASI)
\insertCiteajay_modified_2018ammistability from a modified formula of
AMMI Stability Index (ASI)
\insertCitejambhulkar_ammi_2014,jambhulkar_genotype_2015,jambhulkar_stability_2017ammistability.
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`

. \loadmathjax

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 (\mjseqnMASI) \insertCiteajay_modified_2018ammistability is computed as follows:

\mjsdeqnMASI = \sqrt \sum_n=1^N' PC_n^2 \times \theta_n^2

Where, \mjseqnPC_n are the scores of \mjseqnnth IPC; 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:

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
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)
``` |

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