ammi.full: Additive Main Effects and Multiplicative Interaction (AMMI)...

In plantbreeding: Analysis and visualization of data from plant breeding and genetics experiments

Description

The function implements Additive Main Effects and Multiplicative Interaction (AMMI) analysis for multiple evironmentenvironment replicated data. AMMI analysis (Gauch 1992) is one of popular tool in GE analysis and particularly effective for depicting adaptive responses. In this process, after genotype and environment main effects in the model, the interaction is retained as multiplicative term in the statistically significant GE-interaction principal-component (PC) axes.

The results of AMMI can be visualized as biplot (Gower and Hand 1996).

Usage

ammi.full(dataframe, environment, genotype, replication, yvar)

Arguments

dataframe	dataframe objet
environment	Name of environment (location or year) variable
genotype	Name of genotype variable
replication	Name of replication variable
yvar	Name of Y variable to be used in the analysis

Author(s)

Umesh Rosyara

References

Gauch H.G.(1992). Statistical analysis of regional yield trials:AMMI analysis of factorial designs. Elsevier, Amsterdam.

Gauch H.G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46:1488-1500.

Gauch H.G., Zobel.R.W. (1996). AMMI analysis of yield trials. p.85-122. In M.S. Kang and H.G. Gauch, Jr. (ed.) Genotype-byenvironment interaction. CRC Press, Boca Raton, FL.

Gower J.C., Hand D.J. (1996). Biplots. Monographs on Statistics and Applied Probability. London, UK: Chapman & Hall

Examples

# Example: AMMI analysis 
data(multienv)
results <- ammi.full(dataframe = multienv , environment = "environments", genotype = "genotypes",
 replication = "replication", yvar = "yield")
print(results)

plantbreeding documentation built on May 2, 2019, 4:54 p.m.