version 1.0-6

cran · Dec 17, 2008 · 4be5a3c · 4be5a3c
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,12 +1,12 @@
 Package: agricolae
 Type: Package
 Title: Statistical Procedures for Agricultural Research
-Version: 1.0-5
-Date: 2008-07-25
+Version: 1.0-6
+Date: 2008-12-17
 Author: Felipe de Mendiburu
 Maintainer: Felipe de Mendiburu <f.mendiburu@cgiar.org>
 Suggests: akima, klaR, SuppDists, corpcor
 Description: Agricolae is a project in order to obtain the degree of master in Systems Engineering in the National University of Engineering in Lima-Peru (UNI in Spanish). These functions are currently utilized by the International Potato Center Research (CIP), the Universidad Nacional Agraria La Molina (UNALM-PERU), and the Instituto Nacional de Investigacion Agricola (INIA-PERU). It comprises the functionality of statistical analysis into experimental designs applied specially for field experiments in  agriculture and plant breeding: Lattice, factorial, complete and incomplete block, Latin Square, Greaco, Alpha designs, Cyclic designs, comparison of multi-location trials, comparison  between treatments, resampling, simulation, biodiversity indexes and consensus cluster.
 License: GPL
 URL: http://tarwi.lamolina.edu.pe/~fmendiburu
-Packaged: Fri Jul 25 19:04:19 2008; fdemendiburu
+Packaged: Wed Dec 17 09:47:13 2008; fdemendiburu
diff --git a/R/index.bio.R b/R/index.bio.R
@@ -1,6 +1,6 @@
 `index.bio` <-
 function (data, method = c("Margalef","Simpson.Dom", "Simpson.Div", "Berger.Parker",
-"McIntosh","Shannon"),level = 95, nboot = 500, noprint=FALSE)
+"McIntosh","Shannon"),level = 95, nboot = 500, console=TRUE)
 {
     method <- match.arg(method)
     x <- data
@@ -27,7 +27,7 @@ function (data, method = c("Margalef","Simpson.Dom", "Simpson.Div", "Berger.Park
     }
     lic = as.numeric(quantile(estimador, (1 - 0.01 * level)/2, type = 6))
     lsc = as.numeric(quantile(estimador, (1 + 0.01 * level)/2, type = 6))
-    if (!noprint) {
+    if (console) {
     cat("\nMethod:", method, "\n")
     cat("\nThe index:", index, "\n\n")
     cat(level, "percent confidence interval:\n", lic, ";", lsc, 

diff --git a/R/stability.par.R b/R/stability.par.R
@@ -107,7 +107,6 @@ SKH <- SKVM - SKMB
 MKH <- SKH / (A - 1)
 SHKLT <- M * (A - 1) * (N - 1)
 
-# valor tabulares
 T05 <- qt(0.95,SHKLT)
 DMV05 <- T05 * sqrt(2 * MKE / (N * M))
 for ( i in 1: A) {
@@ -132,58 +131,51 @@ for ( i in 1: A) {
 FS[i] <- SI[i] / MKE
 FSS[i] <- S[i] / MKE
 }
-SHF1 <-(A - 1) * (M - 1); SHF2 = (A - 1)
+SHF2 <-(A - 1) * (M - 1); SHF1 = (A - 1)
 # value F and t-student
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-if( FV >= F01 ) DD <- "**"
-if( (FV < F01) & (FV >= F05) ) DD <- "*"
-if( FV <  F05 ) DD <- "ns"
-SHF1 <- M * (A - 1) * (N - 1)
-SHF2 <- M - 1
+pvalue <- round(1-pf( FV ,SHF1, SHF2),3)
+DD<-paste("",pvalue)
+if (pvalue <0.001) DD<-"<0.001"
+SHF2 <- M * (A - 1) * (N - 1)
+SHF1 <- M - 1
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-if( FM >= F01 ) NNN <- "**"
-if( (FM < F01) & (FM >= F05) ) NNN <- "*"
-if( FM < F05 ) NNN <- "ns"
-SHF1 <- M * (A - 1) * (N - 1); SHF2 <- (A - 1) * (M - 1)
+pvalue<- round(1-pf( FM ,SHF1, SHF2),3)
+NNN<-paste("",pvalue)
+if (pvalue <0.001) NNN<-"<0.001"
+SHF2 <- M * (A - 1) * (N - 1); SHF1 <- (A - 1) * (M - 1)
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-if( FVM >= F01 ) LL <- "**"
-if( (FVM < F01) & (FVM >= F05) ) LL <- "*"
-if( FVM < F05 ) LL <- "ns"
-
-SHF1 <- (A - 1) * (M - 2); SHF2 <- A - 1
+pvalue <-  round(1-pf( FVM ,SHF1, SHF2),3)
+LL<-paste("",pvalue)
+if (pvalue <0.001) LL<-"<0.001"
+SHF2 <- (A - 1) * (M - 2); SHF1 <- A - 1
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-
-if( FH >= F01 ) HH <- "**"
-if( (FH < F01) & (FH >= F05) ) HH <- "*"
-if( FH < F05 ) HH <- "ns"
-SHF1 <- M * (A - 1) * (N - 1); SHF2 <- (A - 1) * (M - 2)
+pvalue <-  round(1-pf( FH ,SHF1, SHF2),3)
+HH<-paste("",pvalue)
+if (pvalue <0.001) HH<-"<0.001"
+SHF2 <- M * (A - 1) * (N - 1); SHF1 <- (A - 1) * (M - 2)
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-if( FMS >= F01 ) BB <- "**"
-if( (FMS < F01) & (FMS >= F05) ) BB <- "*"
-if( FMS < F05 ) BB <- "ns"
-
-SHF1 <- M * (A - 1) * (N - 1)
-SHF2 <- M - 1
+pvalue <-  round(1-pf( FMS ,SHF1, SHF2),4)
+BB<-paste("",pvalue)
+if (pvalue <0.001) BB<-"<0.001"
+SHF2 <- M * (A - 1) * (N - 1)
+SHF1 <- M - 1
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-#F05<- 1.76  # verdadero valor es:   2.413671281
-#F01<- 2.2   # 3.620981178
 for ( i in 1: A) {
 if(FS[i] >= F01 ) NN[i] <- "**"
 if( (FS[i] < F01) & (FS[i] >= F05) ) NN[i] <- "*"
 if( FS[i] < F05 ) NN[i] <- "ns"
 }
-SHF1 <- M * (A - 1) * (N - 1)
-SHF2 <- M - 2
+SHF2 <- M * (A - 1) * (N - 1)
+SHF1 <- M - 2
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-#F05<- 1.76  # True is:   2.546791447
-#F01<- 2.2   # 3.927394672
 for ( i in 1: A) {
 if( FSS[i] >= F01 ) MMM[i] <- "**"
 if( (FSS[i] < F01) & (FSS[i] >= F05) ) MMM[i] <- "*"
@@ -196,7 +188,7 @@ cat("\n","                       YIELD - STABILITY (YSi) STATISTICS")
 cat("\n\n",RR,"\n",KK," - covariate \n")
 cat("\n","Analysis of Variance")
 cat("\n",rep("-",35))
-cat("\n","Source         d.f.   Sum of Squares  Mean Squares         F")
+cat("\n","Source         d.f.   Sum of Squares  Mean Squares         F  p.value")
 cat("\n",rep("-",35))
 fuentes<- c( "TOTAL       ","GENOTYPES", "ENVIRONMENTS","INTERACTION",
              "HETEROGENEITY" , "RESIDUAL","POOLED ERROR")
@@ -255,27 +247,22 @@ for ( i in 1: A) {
 cat("\n",i, "\t", Z[i,1],"\t", Z[i,2], Z[i,3], "\t",Z[i,4], Z[i,5],"\t", Z[i,6])
 }
 FF <- SI / MKE # each genotype
-SHF1 <- M * (A - 1) * (N - 1); SHF2 <- M - 1
+SHF2 <- M * (A - 1) * (N - 1); SHF1 <- M - 1
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-#F05<- 1.76  # True value:   2.546791447
-#F01<- 2.2   # 3.927394672
-
 for ( i in 1: A) {
 if( FF[i] < FM0) F1[i] <- 0
 if( FF[i] >= FM0) F1[i] <- -2
 if( FF[i] >= F05) F1[i] <- -4
 if( FF[i] >= F01) F1[i] <- -8
 }
-
 for ( i in 1: A) {
 R0 <- 1
 for ( j in 1: A) {
 if( MV[j] < MV[i]) R0 <- R0 + 1
 }
 R[i] <- R0
 }
-
 for ( i in 1: A) GY[i] <- R[i] + MV1[i]
 for ( i in 1: A) GYS[i] <- GY[i] + F1[i]
 SGYS<-0
@@ -294,7 +281,7 @@ Z<-data.frame(Genotype=rownames(y),MV, R, MV1,GY,SI,F1,GYS,GYY)
 names(Z)<-names
 print(data.frame( row.names=NULL,Z))
 cat("\n","Yield Mean:", MES)
-cat("\n","YS    Mean:", MGYS)
+cat("`\n","YS    Mean:", MGYS)
 cat("\n","LSD (0.05):", DMV05)
 cat("\n",rep("-",11))
 cat("\n","+   selected genotype"                                            )

diff --git a/data/CIC.rda b/data/CIC.rda
diff --git a/data/Chz2006.rda b/data/Chz2006.rda
diff --git a/data/ComasOxapampa.rda b/data/ComasOxapampa.rda
diff --git a/data/DC.rda b/data/DC.rda
diff --git a/data/Glycoalkaloids.rda b/data/Glycoalkaloids.rda
diff --git a/data/Hco2006.rda b/data/Hco2006.rda
diff --git a/data/LxT.rda b/data/LxT.rda
diff --git a/data/RioChillon.rda b/data/RioChillon.rda
diff --git a/data/clay.rda b/data/clay.rda
diff --git a/data/corn.rda b/data/corn.rda
diff --git a/data/cotton.rda b/data/cotton.rda
diff --git a/data/disease.rda b/data/disease.rda
diff --git a/data/frijol.rda b/data/frijol.rda
diff --git a/data/genxenv.rda b/data/genxenv.rda
diff --git a/data/grass.rda b/data/grass.rda
diff --git a/data/greenhouse.rda b/data/greenhouse.rda
diff --git a/data/growth.rda b/data/growth.rda
diff --git a/data/haynes.rda b/data/haynes.rda
diff --git a/data/heterosis.rda b/data/heterosis.rda
diff --git a/data/huasahuasi.rda b/data/huasahuasi.rda
diff --git a/data/markers.rda b/data/markers.rda
diff --git a/data/melon.rda b/data/melon.rda
diff --git a/data/natives.rda b/data/natives.rda
diff --git a/data/pamCIP.rda b/data/pamCIP.rda
diff --git a/data/paracsho.rda b/data/paracsho.rda
diff --git a/data/plots.rda b/data/plots.rda
diff --git a/data/plrv.rda b/data/plrv.rda
diff --git a/data/potato.rda b/data/potato.rda
diff --git a/data/ralstonia.rda b/data/ralstonia.rda
diff --git a/data/rice.rda b/data/rice.rda
diff --git a/data/sinRepAmmi.rda b/data/sinRepAmmi.rda
diff --git a/data/soil.rda b/data/soil.rda
diff --git a/data/sweetpotato.rda b/data/sweetpotato.rda
diff --git a/data/wilt.rda b/data/wilt.rda
diff --git a/data/yacon.rda b/data/yacon.rda
diff --git a/man/Chz2006.Rd b/man/Chz2006.Rd
@@ -37,5 +37,14 @@ List of 2
 library(agricolae)
 data(Chz2006)
 str(Chz2006)
+wilt<-Chz2006$wilt
+yield<-Chz2006$yield
+means <- tapply.stat(wilt[,5],wilt[,1:3],function(x) mean(x,na.rm=TRUE))
+names(means)[4]<-"wilt_percent"
+model <- aov(wilt_percent ~ block + crop, means)
+anova(model)
+cv.model(model)
+yield<-yield[order(paste(yield[,1],yield[,2],yield[,3])),]
+correlation(means[,4],yield[,4],method="spearman")
 }
 \keyword{datasets}
diff --git a/man/Hco2006.Rd b/man/Hco2006.Rd
@@ -37,5 +37,14 @@ List of 2
 library(agricolae)
 data(Hco2006)
 str(Hco2006)
+wilt<-Hco2006$wilt
+yield<-Hco2006$yield
+means <- tapply.stat(wilt[,5],wilt[,1:3],function(x) mean(x,na.rm=TRUE))
+names(means)[4]<-"wilt_percent"
+model <- aov(wilt_percent ~ block + crop, means)
+anova(model)
+cv.model(model)
+yield<-yield[order(paste(yield[,1],yield[,2],yield[,3])),]
+correlation(means[,4],yield[,4],method="spearman")
 }
 \keyword{datasets}
diff --git a/man/agricolae.package.Rd b/man/agricolae.package.Rd
@@ -13,8 +13,8 @@ designs applied specially for field experiments in agriculture and plant breedin
 \tabular{ll}{
 Package: \tab agricolae\cr
 Type: \tab Package\cr
-Version: \tab 1.0-5\cr
-Date: \tab 2008-07-25\cr
+Version: \tab 1.0-6\cr
+Date: \tab 2008-12-17\cr
 License: \tab GPL\cr
 }
 Planning of field experiments: lattice, factorial, RCBD, CRD, Latin Square, Graeco, BIB, Alpha design, Cyclic Designs. 

diff --git a/man/index.bio.Rd b/man/index.bio.Rd
@@ -1,43 +1,37 @@
 \name{index.bio}
 \alias{index.bio}
-%- index.bio.
 \title{ Biodiversity Index }
 \description{
-Scientists use a formula called the biodiversity index to describe the amount of
+  Scientists use a formula called the biodiversity index to describe the amount of
 species diversity in a given area.
-
 }
 \usage{
-index.bio(data, method = c("Margalef", "Simpson.Dom", "Simpson.Div", 
-"Berger.Parker", "McIntosh", "Shannon"),level = 95, nboot = 500, noprint=FALSE)
+index.bio(data, method = c("Margalef", "Simpson.Dom", "Simpson.Div", "Berger.Parker", "McIntosh", "Shannon"), level=95, nboot=500, console=TRUE)
 }
-
 \arguments{
-  \item{data}{ number of specimens  }
+  \item{data}{ number of specimens }
   \item{method}{ Describe method bio-diversity }
   \item{level}{ Significant level }
   \item{nboot}{ size bootstrap }
-  \item{noprint} {output no console}
+  \item{console}{ output console TRUE}
 }
 \details{
-method bio-diversity
-
-"Margalef"
-"Simpson.Dom"
-"Simpson.Div"
-"Berger.Parker"
-"McIntosh"
-"Shannon" 
-
+  method bio-diversity.
+  "Margalef"
+  "Simpson.Dom"
+  "Simpson.Div"
+  "Berger.Parker"
+  "McIntosh"
+  "Shannon"
 }
 \value{
-  \item{data}{ vector }
-  \item{method}{ method bio-diversity }
-  \item{level}{ value 0-100 percentage }
-  \item{nboot}{ size 100, 500,... }
+  \item{data }{ vector }
+  \item{method }{ method bio-diversity }
+  \item{level }{ value 0-100 percentage }
+  \item{nboot }{ size 100, 500,... }
 }
+\references{Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC }
 \author{ Felipe de Mendiburu }
-
 \examples{
 library(agricolae)
 data(paracsho)
@@ -47,4 +41,4 @@ output1 <- index.bio(specimens,method="Simpson.Div",level=95,nboot=200)
 output2 <- index.bio(specimens,method="Shannon",level=95,nboot=200)
 rbind(output1, output2)
 }
-\keyword{ univar }% __ONLY ONE__ keyword per line
+\keyword{ univar }
diff --git a/man/plots.Rd b/man/plots.Rd
@@ -29,5 +29,26 @@ plots[,1] <-as.factor(plots[,1])
 # split-plot analysis
 model <- aov(yield ~ block + A + Error(plot)+ B + A:B, data=plots)
 summary(model)
+attach(plots)
+b<-nlevels(B)
+a<-nlevels(A)
+r<-nlevels(block)
+dfa <- df.residual(model$plot)
+Ea <-deviance(model$plot)/dfa
+dfb <- df.residual(model$Within)
+Eb <-deviance(model$Within)/dfb
+Eab <- (Ea +(b-1)*Eb)/(b*r)
+# Satterthwaite
+dfab<-(Ea +(b-1)*Eb)^2/(Ea^2/dfa +((b-1)*Eb)^2/dfb)
+# Comparison A, A(b1), A(b2), A(b3)
+comparison1 <-LSD.test(yield,A,dfa,Ea)
+comparison2 <-LSD.test(yield[B=="b1"],A[B=="b1"],dfab,Eab)
+comparison3 <-LSD.test(yield[B=="b2"],A[B=="b2"],dfab,Eab)
+comparison4 <-LSD.test(yield[B=="b3"],A[B=="b3"],dfab,Eab)
+# Comparison B, B(a1), B(a2)
+comparison5 <-LSD.test(yield,B,dfb,Eb)
+comparison6 <-LSD.test(yield[A=="a1"],B[A=="a1"],dfb,Eb)
+comparison7 <-LSD.test(yield[A=="a2"],B[A=="a2"],dfb,Eb)
+detach(plots)
 }
 \keyword{datasets}
diff --git a/man/resampling.cv.Rd b/man/resampling.cv.Rd
@@ -23,6 +23,7 @@ resampling.cv(A, size, npoints)
   \item{npoints}{ numeric }
 }
 
+\references{Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC }
 \author{ Felipe de Mendiburu }
 
 \seealso{\code{\link{cv.similarity}}, \code{\link{similarity} } }

diff --git a/man/resampling.model.Rd b/man/resampling.model.Rd
@@ -23,6 +23,7 @@ resampling.model(k, data, model)
   \item{model}{ model }
 }
 
+\references{Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC }
 \author{ Felipe de Mendiburu }
 
 \seealso{\code{\link{simulation.model} } }

diff --git a/man/stability.par.Rd b/man/stability.par.Rd
@@ -34,8 +34,23 @@ stability.par(data,rep,MSerror,alpha=0.1,main=NULL,cova = FALSE,name.cov=NULL,fi
   \item{name.cov}{ Text }
   \item{file.cov}{ Vector numeric }
 }
+\details{
+Stable 
+(i) determines the contribution of each genotype to GE interaction by calculating var(i);
+(ii) assigns ranks to genotypes from highest to lowest yield receiving the rank of 1;
+(iii) calculates protected LSD for mean yield comparisons;
+(iv) adjusts yield rank according to LSD (the adjusted rank labeled Y);
+(v) determines significance of var(i) usign an aproximate F-test;
+(vi) assigns stability rating (S) as follows: -8, -4 and -2 for var(i) significant at the
+0.01, 0.05 and 0.10 probability levels, and 0 for nonsignificant var(i) 
+( the higher the var(i), the less stable the genotype);
+(vii) sums adjusted yield rank, Y, and stability rating, S, for each genotype to determine YS(i) statistic; and 
+(viii) calculates mean YS(i) and identifies genotypes (selection) with YS(i) > mean YS(i).
+}
 \references{ Kang, M. S. 1993. Simultaneous selection for yield and
- stability: Consequences for growers. Agron. J. 85:754-757 }
+ stability: Consequences for growers. Agron. J. 85:754-757. 
+ Manjit S. Kang and Robert Mangari. 1995. Stable: A basic program for 
+ calculating stability and yield-stability statistics. Agron. J. 87:276-277}
 \author{ Felipe de Mendiburu }
 
 \seealso{\code{\link{stability.nonpar} } }
@@ -47,22 +62,22 @@ library(agricolae)
 # replication rep= 4
 # Mean square error, MSerror = 1.8
 # 12 environment
-# 13 genotype  = 1,2,3,.., 13
+# 17 genotype  = 1,2,3,.., 17
 # yield averages of 13 genotypes in localities
-V1 <- c(10.2, 8.8, 8.8, 9.3, 9.6, 7.2, 8.4, 9.6, 7.9, 10, 9.3, 8.0, 10.1)
-V2 <- c(7, 7.8, 7.0, 6.9, 7, 8.3, 7.4, 6.5,	6.8, 7.9, 7.3, 6.8, 8.1)
-V3 <- c(5.3, 4.4, 5.3, 4.4, 5.6, 4.6, 6.2, 6.0, 6.5, 5.3, 5.7, 4.4, 4.2)
-V4 <- c(7.8, 5.9, 7.3, 5.9, 7.8, 6.3, 7.9, 7.5, 7.6, 5.4, 5.6, 7.8, 6.5)
-V5 <- c(9, 9.2, 8.8, 10.6, 8.3, 9.3, 9.6, 8.8, 7.9, 9.1, 7.7, 9.5, 9.4)
-V6 <- c(6.9, 7.7, 7.9, 7.9, 7, 8.9,	9.4, 7.9, 6.5, 7.2, 5.4, 6.2, 7.2)
-V7 <- c(4.9, 2.5, 3.4, 2.5, 3,2.5, 3.6, 5.6,3.8, 3.9, 3.0, 3.0, 2.5)
-V8 <- c(6.4, 6.4, 8.1, 7.2, 7.5, 6.6, 7.7, 7.6, 7.8, 7.5, 6.0, 7.2, 6.8)
-V9 <- c(8.4, 6.1, 6.8, 6.1, 8.2, 6.9, 6.9, 9.1, 9.2, 7.7, 6.7, 7.8, 6.5)
-V10 <-c(8.7, 9.4, 8.8, 7.9, 7.8, 7.8, 11.4, 9.9, 8.6, 8.5, 8.0, 8.3, 9.1)
-V11 <-c(5.4, 5.2, 5.6, 4.6, 4.8, 5.7, 6.6, 6.8, 5.2, 4.8, 4.9, 5.4, 4.5)
-V12 <-c(8.6, 8.0, 9.2, 8.1, 8.3, 8.9, 8.6, 9.6, 9.5, 7.7, 7.6, 8.3, 6.6)
+V1 <- c(10.2,8.8,8.8,9.3,9.6,7.2,8.4,9.6,7.9,10,9.3,8.0,10.1,9.4,10.8,6.3,7.4)
+V2 <- c(7,7.8,7.0,6.9,7,8.3,7.4,6.5,6.8,7.9,7.3,6.8,8.1,7.1,7.1,6.4,4.1)
+V3 <- c(5.3, 4.4, 5.3, 4.4, 5.5, 4.6, 6.2, 6.0, 6.5, 5.3, 5.7, 4.4, 4.2,5.6,5.8,3.9,3.8)
+V4 <- c(7.8, 5.9, 7.3, 5.9, 7.8, 6.3, 7.9, 7.5, 7.6, 5.4, 5.6, 7.8, 6.5,8.1,7.5,5.0,5.4)
+V5 <- c(9, 9.2, 8.8, 10.6, 8.3, 9.3, 9.6, 8.8, 7.9, 9.1, 7.7, 9.5, 9.4,9.4,10.3,8.8,8.7)
+V6 <- c(6.9, 7.7, 7.9, 7.9, 7, 8.9,	9.4, 7.9, 6.5, 7.2, 5.4, 6.2, 7.2,8.8,7.3,7.1,6.4)
+V7 <- c(4.9, 2.5, 3.4, 2.5, 3,2.5, 3.6, 5.6,3.8, 3.9, 3.0, 3.0, 2.5,2.6,3.8,2.8,1.6)
+V8 <- c(6.4, 6.4, 8.1, 7.2, 7.5, 6.6, 7.7, 7.6, 7.8, 7.5, 6.0, 7.2, 6.8,7.6,6.9,7.2,7.3)
+V9 <- c(8.4, 6.1, 6.8, 6.1, 8.2, 6.9, 6.9, 9.1, 9.2, 7.7, 6.7, 7.8, 6.5,5.2,8.3,6.8,7.1)
+V10 <-c(8.7, 9.4, 8.8, 7.9, 7.8, 7.8, 11.4, 9.9, 8.6, 8.5, 8.0, 8.3, 9.1,11.0,8.1,7.8,8.0)
+V11 <-c(5.4, 5.2, 5.6, 4.6, 4.8, 5.7, 6.6, 6.8, 5.2, 4.8, 4.9, 5.4, 4.5,5.6,7.0,6.0,5.6)
+V12 <-c(8.6, 8.0, 9.2, 8.1, 8.3, 8.9, 8.6, 9.6, 9.5, 7.7, 7.6, 8.3, 6.6,9.5,9.0,9.0,8.5)
 data<-data.frame(V1,V2,V3,V4,V5,V6,V7,V8,V9,V10,V11,V12)
-rownames(data)<-LETTERS[1:13]
+rownames(data)<-LETTERS[1:17]
 stability.par(data, rep=4, MSerror=1.8, alpha=0.1, main="Genotype")
 
 #example 2 covariable. precipitation