agricolae (version 1.2-8)

AMMI: 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

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

Details

additional graphics see help(plot.AMMI).

References

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

See Also

lineXtester,plot.AMMI

Examples

# NOT RUN {
# 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
# }