AMMI {agricolae} | R Documentation |
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 biplot, triplot graphs and analysis.
AMMI(ENV, GEN, REP, Y, MSE = 0, number=TRUE,graph="biplot",...)
ENV |
Environment |
GEN |
Genotype |
REP |
Replication |
Y |
Response |
MSE |
Mean Square Error |
number |
TRUE or FALSE |
graph |
"biplot" or "triplot" |
... |
plot graphics parameters |
additional biplot.
ENV |
Factor |
GEN |
Factor |
REP |
Numeric |
Y |
Numeric |
MSE |
Numeric |
number |
TRUE or FALSE |
graph |
"biplot" or "triplot" |
... |
others parameters |
F. de Mendiburu
GGE Biplot Analysis: A graphical tool for breeder, geneticists, and agronomists. Weikai Yan and Manjit S. Kang. www.crepress.com 2003, Principles and procedures of statistics: a biometrical approach Steel & Torry & Dickey. Third Edition 1997
# Full replications library(agricolae) library(klaR) # Example 1 data(plrv) #startgraph # biplot model<- AMMI(plrv[,2], plrv[,1], plrv[,3], plrv[,5],xlim=c(-3,3),ylim=c(-4,4), graph="biplot") model<- AMMI(plrv[,2], plrv[,1], plrv[,3], plrv[,5],xlim=c(-3,3),ylim=c(-4,4), graph="biplot",number=FALSE) # triplot model<- AMMI(plrv[,2], plrv[,1], plrv[,3], plrv[,5],graph="triplot") model<- AMMI(plrv[,2], plrv[,1], plrv[,3], plrv[,5],graph="triplot",number=FALSE) #endgraph # 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) attach(cic) #startgraph par(cex=0.6) model<-AMMI(Locality, Genotype, Rep, relative,xlim=c(-0.6,0.6), ylim=c(-1.5e-8,1.5e-8)) #endgraph pc<- princomp(model$genXenv, cor = FALSE) pc$loadings summary(pc) model$biplot detach(cic) # Example 3 # Only means. Mean square error is well-known. data(sinRepAmmi) attach(sinRepAmmi) REP <- 3 MSerror <- 93.24224 #startgraph model<-AMMI(ENV, GEN, REP, YLD, MSerror,xlim=c(-8,6),ylim=c(-6,6)) #endgraph pc<- princomp(model$genXenv, cor = FALSE) pc$loadings summary(pc) model$biplot detach(sinRepAmmi) # Biplot with the one restored observed. rm(REP) bplot<-model$biplot[,1:4] attach(bplot) #startgraph par(cex=0.8) plot(YLD,PC1,cex=0.0,text(YLD,PC1,labels=row.names(bplot),col="blue"), main="AMMI BIPLOT",frame=TRUE) MEANS<-mean(YLD) abline(h=0,v= MEANS,lty=2,col="red") amb<-subset(bplot,type=="ENV") detach(bplot) attach(amb) s <- seq(length(YLD)) arrows(MEANS, 0, 0.9*(YLD[s]-MEANS)+MEANS, 0.9*PC1[s], col= "brown", lwd=1.8,length=0.1) #endgraph detach(amb) # Principal components by means of the covariance # It is to compare results with AMMI cova<-cov(model$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