R: AMMI Analysis

AMMI {agricolae} | R Documentation

## AMMI Analysis

### Description

Additive Main Effects and Multiplicative Interaction Models (AMMI) are widely used to analyze main effects and genotype by environment (GEN, ENV) interactions in multilocation variety trials. Furthermore, this function generates data to biplot, triplot graphs and analysis.

### Usage

    AMMI(ENV, GEN, REP, Y, MSE = 0,console=FALSE,PC=FALSE)

### Arguments

`ENV` | Environment
`GEN` | Genotype
`REP` | Replication
`Y` | Response
`MSE` | Mean Square Error
`console` | ouput TRUE or FALSE
`PC` | Principal components ouput TRUE or FALSE

### Details

additional graphics see help(plot.AMMI).

### Value

`ANOVA` | analysis of variance general
`genXenv` | class by, genopyte and environment
`analysis` | analysis of variance principal components
`means` | average genotype and environment
`biplot` | data to produce graphics
`PC` | class princomp

### Author(s)

F. de Mendiburu

### References

Crossa, J. 1990. Statistical analysis of multilocation trials. Advances in Agronomy 44:55-85

### See Also

`lineXtester`,`plot.AMMI`

### Examples

    # Full replications
    library(agricolae)
    # Example 1
    data(plrv)
    model<- with(plrv,AMMI(Locality, Genotype, Rep, Yield, console=FALSE))
    model$ANOVA
    # see help(plot.AMMI)
    # biplot
    plot(model)
    # triplot PC 1,2,3
    plot(model, type=2, number=TRUE)
    # biplot PC1 vs Yield
    plot(model, first=0,second=1, number=TRUE)
    # Example 2
    data(CIC)
    data1<-CIC$comas[,c(1,6,7,17,18)]
    data2<-CIC$oxapampa[,c(1,6,7,19,20)]
    cic <- rbind(data1,data2)
    model<-with(cic,AMMI(Locality, Genotype, Rep, relative))
    model$ANOVA
    plot(model,0,1,angle=20,ecol="brown")
    # Example 3
    # Only means. Mean square error is well-known.
    data(sinRepAmmi)
    REP <- 3
    MSerror <- 93.24224
    #startgraph
    model<-with(sinRepAmmi,AMMI(ENV, GEN, REP, YLD, MSerror,PC=TRUE))
    # print anova
    print(model$ANOVA,na.print = "")
    # Biplot with the one restored observed.
    plot(model,0,1,type=1)
    # with principal components model$PC is class "princomp"
    pc<- model$PC
    pc$loadings
    summary(pc)
    biplot(pc)
    # Principal components by means of the covariance similar AMMI
    # It is to compare results with AMMI
    cova<-cov(model$genXenv)
    values<-eigen(cova)
    total<-sum(values$values)
    round(values$values*100/total,2)
    # AMMI: 64.81 18.58 13.50  3.11  0.00

[Package agricolae version 1.2-6 Index]