AMMI: AMMI Analysis

In agricolae: Statistical Procedures for Agricultural Research

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$ANOVA
# see help(plot.AMMI)
# biplot
plot(model)
# biplot PC1 vs Yield 
plot(model, first=0,second=1, number=TRUE)
# Example 2
data(CIC)
data1<-CIC$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$ANOVA,na.print = "")
# Biplot with the one restored observed.
plot(model,0,1)
# with principal components model$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

agricolae documentation built on Oct. 23, 2023, 1:06 a.m.