library(agricolae)
REND <- read.delim("~/R/REND.txt")
attach (REND)
model<-AMMI(ENV, GEN, REP, YLD, MSerror, PC=T)
model$ANOVA
## Analysis of Variance Table
##
## Response: Y
## Df Sum Sq Mean Sq F value Pr(>F)
## ENV 2 289094 144547 381.7926 4.739e-07 ***
## REP(ENV) 6 2272 379 0.5543 0.7666
## GEN 17 45423 2672 3.9122 3.503e-07 ***
## ENV:GEN 34 54856 1613 2.3623 4.370e-05 ***
## Residuals 426 290946 683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model$analysis
## percent acum Df Sum.Sq Mean.Sq F.value Pr.F
## PC1 82.4 82.4 18 15062.245 836.7914 1.23 0.2326
## PC2 17.6 100.0 16 3223.035 201.4397 0.29 0.9971
model$biplot
## type YLD PC1 PC2
## (ExL)(JxK) GEN 71.87785 -2.94643477 0.79465574
## (I)(ExL) GEN 58.33225 2.05112284 0.77590470
## (LxM)(B) GEN 60.52829 -2.13192499 0.24251586
## (N)(ExL) GEN 60.49191 -0.59971023 0.68199598
## (UANMEL1) GEN 88.36162 4.33764186 -2.00142377
## (UANMEL2) GEN 74.15987 1.51347016 -0.32161645
## (UANMEL4) GEN 68.82476 0.97021429 -0.76790860
## (UANMEL5) GEN 58.02678 2.08853533 1.13996371
## (UANMEL7) GEN 68.93587 -1.48094198 -0.12031297
## B GEN 79.14729 -2.82751657 -3.63654830
## BxI GEN 55.78985 -1.59592794 2.05454465
## BxJ GEN 70.43503 0.04275673 1.55031705
## BxN GEN 76.52568 -1.43839018 -2.00930616
## CRUISER GEN 52.75197 -0.63714514 0.60587624
## ExK GEN 61.91340 -1.49222485 0.56319802
## ExL GEN 54.33559 0.57431575 0.62757632
## ExN GEN 68.69995 0.66662305 -0.20438158
## I GEN 54.16734 2.90553664 0.02494959
## BAJIO ENV 31.97749 -0.61816631 4.65560993
## PARRAS ENV 76.50649 6.23715391 -1.96369731
## TUNEL ENV 88.73356 -5.61898760 -2.69191262
model$means
## ENV GEN YLD RESIDUAL
## 1 BAJIO (ExL)(JxK) 43.63715 5.5209939
## 2 BAJIO (I)(ExL) 26.91493 2.3443746
## 3 BAJIO (LxM)(B) 29.21354 2.4469435
## 4 BAJIO (N)(ExL) 30.27604 3.5458279
## 5 BAJIO (UANMEL1) 42.60069 -11.9992325
## 6 BAJIO (UANMEL2) 37.96528 -2.4328970
## 7 BAJIO (UANMEL4) 30.88823 -4.1748367
## 8 BAJIO (UANMEL5) 28.28125 4.0161642
## 9 BAJIO (UANMEL7) 35.52951 0.3553382
## 10 BAJIO B 30.20312 -15.1824749
## 11 BAJIO BxI 32.57986 10.5517073
## 12 BAJIO BxJ 43.86458 7.1912407
## 13 BAJIO BxN 34.29861 -8.4653814
## 14 BAJIO CRUISER 22.20486 3.2145851
## 15 BAJIO ExK 31.69618 3.5444734
## 16 BAJIO ExL 23.14062 2.5667279
## 17 BAJIO ExN 33.57465 -1.3636048
## 18 BAJIO I 18.72569 -1.6799493
## 19 PARRAS (ExL)(JxK) 62.70733 -19.9378305
## 20 PARRAS (I)(ExL) 80.36908 11.2695269
## 21 PARRAS (LxM)(B) 57.52223 -13.7733720
## 22 PARRAS (N)(ExL) 66.17950 -5.0797187
## 23 PARRAS (UANMEL1) 130.11366 30.9847304
## 24 PARRAS (UANMEL2) 94.99848 10.0713037
## 25 PARRAS (UANMEL4) 87.15138 7.5593159
## 26 PARRAS (UANMEL5) 79.58206 10.7879726
## 27 PARRAS (UANMEL7) 70.70257 -9.0006048
## 28 PARRAS B 79.42003 -10.4945759
## 29 PARRAS BxI 52.56860 -13.9885520
## 30 PARRAS BxJ 78.42467 -2.7776731
## 31 PARRAS BxN 82.26720 -5.0257918
## 32 PARRAS CRUISER 58.35555 -5.1637298
## 33 PARRAS ExK 62.26752 -10.4131865
## 34 PARRAS ExL 67.45262 2.3497258
## 35 PARRAS ExN 84.02643 4.5591741
## 36 PARRAS I 83.00793 18.0732858
## 37 TUNEL (ExL)(JxK) 109.28907 14.4168366
## 38 TUNEL (I)(ExL) 67.71273 -13.6139014
## 39 TUNEL (LxM)(B) 94.84910 11.3264286
## 40 TUNEL (N)(ExL) 85.02018 1.5338908
## 41 TUNEL (UANMEL1) 92.37050 -18.9854979
## 42 TUNEL (UANMEL2) 89.51584 -7.6384067
## 43 TUNEL (UANMEL4) 88.43466 -3.3844792
## 44 TUNEL (UANMEL5) 66.21702 -14.8041368
## 45 TUNEL (UANMEL7) 100.57551 8.6452666
## 46 TUNEL B 127.81872 25.6770508
## 47 TUNEL BxI 82.22107 3.4368447
## 48 TUNEL BxJ 89.01585 -4.4135676
## 49 TUNEL BxN 113.01124 13.4911732
## 50 TUNEL CRUISER 77.69549 1.9491447
## 51 TUNEL ExK 91.77649 6.8687131
## 52 TUNEL ExL 72.41352 -4.9164537
## 53 TUNEL ExN 88.49876 -3.1955693
## 54 TUNEL I 60.76838 -16.3933364
pc<- princomp(model$genXenv, cor = FALSE)
pc$loadings
##
## Loadings:
## Comp.1 Comp.2 Comp.3
## BAJIO -0.813 0.577
## PARRAS -0.741 0.343 0.577
## TUNEL 0.668 0.470 0.577
##
## Comp.1 Comp.2 Comp.3
## SS loadings 1.000 1.000 1.000
## Proportion Var 0.333 0.333 0.333
## Cumulative Var 0.333 0.667 1.000
summary(pc)
## Importance of components:
## Comp.1 Comp.2 Comp.3
## Standard deviation 16.7012117 7.7256612 0
## Proportion of Variance 0.8237361 0.1762639 0
## Cumulative Proportion 0.8237361 1.0000000 1
# Construcción biplot variable vs cp1
detach(REND)
bplot<-model$biplot[,1:4]
bplot
## type YLD PC1 PC2
## (ExL)(JxK) GEN 71.87785 -2.94643477 0.79465574
## (I)(ExL) GEN 58.33225 2.05112284 0.77590470
## (LxM)(B) GEN 60.52829 -2.13192499 0.24251586
## (N)(ExL) GEN 60.49191 -0.59971023 0.68199598
## (UANMEL1) GEN 88.36162 4.33764186 -2.00142377
## (UANMEL2) GEN 74.15987 1.51347016 -0.32161645
## (UANMEL4) GEN 68.82476 0.97021429 -0.76790860
## (UANMEL5) GEN 58.02678 2.08853533 1.13996371
## (UANMEL7) GEN 68.93587 -1.48094198 -0.12031297
## B GEN 79.14729 -2.82751657 -3.63654830
## BxI GEN 55.78985 -1.59592794 2.05454465
## BxJ GEN 70.43503 0.04275673 1.55031705
## BxN GEN 76.52568 -1.43839018 -2.00930616
## CRUISER GEN 52.75197 -0.63714514 0.60587624
## ExK GEN 61.91340 -1.49222485 0.56319802
## ExL GEN 54.33559 0.57431575 0.62757632
## ExN GEN 68.69995 0.66662305 -0.20438158
## I GEN 54.16734 2.90553664 0.02494959
## BAJIO ENV 31.97749 -0.61816631 4.65560993
## PARRAS ENV 76.50649 6.23715391 -1.96369731
## TUNEL ENV 88.73356 -5.61898760 -2.69191262
attach(bplot)
par(cex=0.8)
plot(YLD,PC1,cex=0.0,text(YLD,PC1,labels=row.names(bplot),col="blue", xlab="PCs"),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= "black", lwd=1.8,length=0.1)
