version 1.0-2

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: agricolae
 Type: Package
 Title: Statistical Procedures for Agricultural Research
-Version: 1.0-1
-Date: 2007-01-25
+Version: 1.0-2
+Date: 2007-02-06
 Author: Felipe de Mendiburu
 Maintainer: Felipe de Mendiburu <f.mendiburu@cgiar.org>
 Suggests: cluster, akima, SuppDists, klaR
@@ -19,5 +19,5 @@ Description: These functions are currently utilized by the International Potato
  Resampling and simulation: resampling.model, simulation.model, Ecology: Indices
  Biodiversity, path analysis, Uniformity Soil: Index Smith's.
 License: This software may be re-distributed freely and used for any non-commercial purpose under the GPL license.
-URL: http://tarwi.lamolina.edu.pe/~fmendiburu; http://research.cip.cgiar.org/cipstat/
-Packaged: Thu Jan 25 14:10:12 2007; FDEMENDIBURU
+URL: http://tarwi.lamolina.edu.pe/~fmendiburu 
+Packaged: Tue Feb  6 11:39:18 2007; FDEMENDIBURU

R/design.alpha.R

@@ -62,7 +62,7 @@ alpha[i,4]<-alpha[i-2,4]+1
 serie <- "IV"
 }
 if (serie == "" ) {
-cat("\nhelp(design.a01): to see the series of alpha generators\n")
+cat("\nhelp(design.alpha): to see the series of alpha generators\n")
 }
 else {
 # arreglos intermedios alpha *
@@ -120,6 +120,7 @@ plots <- number + 1:(s*k*r) - 1
 cols <-as.numeric(rep(gl(k,1),s*r))
 book<-data.frame(plots=plots,cols=cols,book)
 book<-data.frame(row.names=NULL,book)
+book$block <- gl(s*r, k)
 names(book)[4] <- name.trt
 return(book)
 }

R/friedman.test.R

@@ -55,7 +55,7 @@ cat("\npFriedman :",p.fried)
 cat("\nAlpha     :",alpha)
 cat("\nt-Student :",Tprob)
 #...............
-#cat("\nRepetición:\t",nr)
+#cat("\nReplication:\t",nr)
 if (group) {
 cat("\nLSD       :",LSD)
 cat("\n\nMeans with the same letter are not significantly different.")

R/kruskal.test.R

@@ -5,7 +5,7 @@ name.t <- paste(deparse(substitute(trt)))
 junto <- subset(data.frame(y, trt), is.na(y) == FALSE)
 N<- nrow(junto)
 junto[, 1] <- rank(junto[, 1])
-means <- tapply.stat(junto[,2],junto[,1],mean)
+means <- tapply.stat(junto[,2],junto[,1],sum)
 sds <-   tapply.stat(junto[,2],junto[,1], sd)
 nn <-   tapply.stat(junto[,2],junto[,1],length)
 means<-data.frame(means,replication=nn[,2])

R/stability.par.R

@@ -13,20 +13,19 @@ A <- nrow(y) # Number of genotypes
 M <- ncol(y) # Number of environments
 N <- rep  # Number of replications
 MKE <- MSerror # Pooled error mean square
-RR <- main # "Estabilidad de cultivares" Title of the experiments
+RR <- main # "Stability" Title of the experiments
 #
 FM0 <- qf(1-alpha,M-1,M * (A - 1) * (N - 1))
-# Datos para el estudio
-# Generacion de los arreglos
-#dimensionar para Genotipos (A)
+# Data
+#dimension for genotype (A)
 dimA <- rep(0,A); dim(dimA)<-A
 SHV<-dimA; MV<-dimA; SU<-dimA; MV1<-dimA; FF<-dimA; GY<-dimA; U1<-dimA; G<-dimA
 SI<-dimA; B<-dimA; SA<-dimA; S<-dimA; FS<-dimA; FSS<-dimA; R<-dimA; GYS<-dimA
 F1<-dimA; GY<-dimA; NN<-dimA; X1<-dimA; X1M<-dimA; W<-dimA;GYY<-dimA; MMM<-dimA
-#dimensionar para Ambientes (M)
+#dimension for environment (M)
 dimA <- rep(0,M); dim(dimA)<-M
 SHM<-dimA; MM<-dimA; II<-dimA; X2<-dimA; X2M<-dimA
-# Dimension (A,M)
+#dimension (A,M)
 dimA <- rep(0,A*M); dim(dimA)<-c(A,M)
 U<-dimA;  G1<-dimA
 SV <- 0  ; SM <- 0 ; GG1 <-0  ; L <- 0;  SMES <- 0
@@ -134,7 +133,7 @@ FS[i] <- SI[i] / MKE
 FSS[i] <- S[i] / MKE
 }
 SHF1 <-(A - 1) * (M - 1); SHF2 = (A - 1)
-# Busca los valores de F y t-student Tabulares
+# value F and t-student
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
 if( FV >= F01 ) DD <- "**"
@@ -183,7 +182,7 @@ SHF1 <- M * (A - 1) * (N - 1)
 SHF2 <- M - 2
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-#F05<- 1.76  # verdadero valor es:   2.546791447
+#F05<- 1.76  # True is:   2.546791447
 #F01<- 2.2   # 3.927394672
 for ( i in 1: A) {
 if( FSS[i] >= F01 ) MMM[i] <- "**"
@@ -245,7 +244,7 @@ for ( j in 1: M) {
 W[i] <- W[i] + (y[i, j] - X1M[i] - X2M[j] + MM1) ^ 2 * N
 }
 }
-cat("\n","Stability statistics\n")
+cat("\n\n","Stability statistics")
 cat("\n",rep("-",35))
 cat("\nGenotype    MEANS       Sigma-square      s-square      Ecovalence")
 cat("\n",rep("-",35))
@@ -255,11 +254,11 @@ Z<-as.matrix(Z)
 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 # para cada genotipo
+FF <- SI / MKE # each genotype
 SHF1 <- M * (A - 1) * (N - 1); SHF2 <- M - 1
 F05 <- qf(0.95,SHF1, SHF2)
 F01 <- qf(0.99,SHF1, SHF2)
-#F05<- 1.76  # verdadero valor es:   2.546791447
+#F05<- 1.76  # True value:   2.546791447
 #F01<- 2.2   # 3.927394672
 
 for ( i in 1: A) {
@@ -286,22 +285,18 @@ for ( i in 1: A) {
 GYY[i]<-""
 if( GYS[i] > MGYS ) GYY[i] <- "+"
 }
-cat("\n")
-cat("\n","Simultaneous selection for yield and stability  (++)")
-cat("\n")
-cat("\n",rep("-",43))
-cat("\n","Genotype           Yield       Yield Adjustment Adjusted  Stability  Stability    YS(i)")
-cat("\n","                               Rank   to Rank             Variance    Rating"           )
-cat("\n",rep("-",43))
+
+cat("\n\nSignif. codes:  0 '**' 0.01 '*' 0.05 'ns' 1\n\n")
+
+cat("Simultaneous selection for yield and stability  (++)\n\n")
+names<-c("Genotype","Yield","Rank","Adj.rank","Adjusted","Stab.var","Stab.rating","YSi","..." )
 Z<-data.frame(Genotype=rownames(y),MV, R, MV1,GY,SI,F1,GYS,GYY)
-Z<-as.matrix(Z)
-for ( i in 1: A) {
-cat("\n",Z[i,1],"\t", Z[i,2],"\t", Z[i,3], "\t",Z[i,4],"\t",Z[i,5],"\t", Z[i,6],"\t", Z[i,7],"\t", Z[i,8], Z[i,9])
-}
-cat("\n",rep("-",43))
-cat("\n","Mean         = ", MES, "\t\t\t\t",MGYS                            )
-cat("\n","LSD (p=0.05) = ", DMV05                                           )
-cat("\n",rep("-",43))
+names(Z)<-names
+print(data.frame( row.names=NULL,Z))
+cat("\n","Yield Mean:", MES)
+cat("\n","YS    Mean:", MGYS)
+cat("\n","LSD (0.05):", DMV05)
+cat("\n",rep("-",11))
 cat("\n","+   selected genotype"                                            )
 cat("\n","++  Reference: Kang, M. S. 1993. Simultaneous selection for yield")
 cat("\n","and stability: Consequences for growers. Agron. J. 85:754-757."   )

hace agricolae.r

@@ -1,5 +1,5 @@
-#setwd("d:/statistics/agricolae/R")
-setwd("f:/statistics/agricolae/R")
+setwd("d:/statistics/agricolae/R")
+#setwd("f:/statistics/agricolae/R")
 library(agricolae)
 data(CIC)
 data(ComasOxapampa)
@@ -25,41 +25,34 @@ data(natives)
 data(paracsho)
 data(plots)
 data(potato)
-data(precipitation)
 data(ralstonia)
 data(rice)
 data(sinRepAmmi)
 data(soil)
-data(stable)
 data(sweetpotato)
 data(trees)
 data(wild)
 
-source("alpha01.R")
 source("AMMI.contour.R")
 source("AMMI.R")
-source("audpc.abs.R")
-source("audpc.rel.R")
+source("audpc.R")
 source("bar.err.R")
 source("bar.group.R")
 source("BIB.test.R")
 source("carolina.R")
 source("coeff.diana.R")
-source("cor.matrix.R")
-source("cor.mv.R")
-source("cor.vector.R")
 source("correl.R")
+source("correlation.R")
 source("cv.model.R")
 source("cv.similarity.R")
-source("decimals.R")
 source("delete.na.R")
 source("design.ab.R")
-source("design.bib.r")
+source("design.alpha.R")
+source("design.bib.R")
 source("design.crd.R")
 source("design.graeco.R")
 source("design.lsd.R")
 source("design.rcbd.R")
-source("dimnames.order.R")
 source("durbin.test.R")
 source("fact.nk.R")
 source("friedman.test.R")
@@ -85,7 +78,6 @@ source("ojiva.freq.R")
 source("order.group.R")
 source("order.stat.R")
 source("path.analysis.R")
-source("plot.disc.R")
 source("polygon.freq.R")
 source("random.ab.R")
 source("reg.homog.R")

man/BIB.test.Rd

@@ -31,9 +31,11 @@ waller: test Waller-Duncan.
   \item{alpha}{ Numeric }
   \item{group}{ TRUE or FALSE }
 }
-\references{ Design of Experiments. Robert O. Kuehl. 2nd ed., Duxbury, 2000 }
-\author{ F. de Mendiburu }
+\references{ Design of Experiments. Robert O. Kuehl. 2nd ed., Duxbury, 2000 
+ Linear Estimation and Design of Experiments. D.D. Joshi. WILEY EASTERN LIMITED
+ 1987, New Delhi, India}
 
+\author{ F. de Mendiburu }
 \seealso{\code{\link{durbin.test}} }
 \examples{
 library(agricolae)
@@ -48,6 +50,15 @@ out<-BIB.test(run,psi,monovinyl,method="waller",group=TRUE)
 out<-BIB.test(run,psi,monovinyl,method="tukey",group=TRUE)
 out<-BIB.test(run,psi,monovinyl,method="tukey",group=FALSE)
 bar.err(out,density=4,ylim=c(0,60))
+# Example linear estimation and design of experiments. D.D. Joshi. 1987
+# Professor of Statistics, Institute of Social Sciences Agra, India
+# 6 varieties of wheat crop in a BIB whit 10 blocks of 3 plots each.
+y <-c(69,77,72,63,70,54,65,65,57,59,50,45,68,75,59,38,60,60,62,
+ 55,54,65,62,65,61,39,54,67,63,56)
+varieties<-gl(6,5)
+block <- c(1,2,3,4,5,1,2,6,7,8,1,3,6,9,10,2,4,7,9,10,3,5,7,8,9,4,5,6,8,10)
+model<-BIB.test(block, varieties, y)
+
 }
 \keyword{ models }
 

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 1.0-1\cr
-Date: \tab 2007-01-25\cr
+Version: \tab 1.0-2\cr
+Date: \tab 2007-02-06\cr
 License: \tab This software may be re-distributed freely and used under the GPL license 2.0 or higher\cr
 }
 Planning of field experiments: lattice, factorial, RCBD, CRD, Latin Square, Greaco, BIB, Alpha design. 

man/carolina3.Rd

@@ -7,7 +7,7 @@
 }
 \usage{data(carolina3)}
 \format{
-  A data frame with 72 observations on the following 5 variables.
+  A data frame with 64 observations on the following 5 variables.
   \describe{
     \item{\code{set}}{a numeric vector}
     \item{\code{male}}{a numeric vector}
@@ -19,6 +19,7 @@
 
 \source{
   Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979.
+  table 3. Raw data for B.C. Design-III. pag 207
 }
 \references{
  Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979.

man/correlation.Rd

@@ -41,7 +41,7 @@ analysis<-correlation(pH,soil[,3:8],method="pearson",alternative="less")
 analysis
 detach(soil)
 
-# Example 3: correlation between pH and % clay method kendall.
+# Example 3: correlation between pH and clay method kendall.
 data(soil)
 attach(soil)
 correlation(pH,clay,method="kendall", alternative="two.sided")

man/cotton.Rd

@@ -18,12 +18,12 @@
 }
 
 \source{
-  Book spanish: Métodos estadísticos para la investigación.
+  Book spanish: Metodos estadisticos para la investigacion.
   Autor: Calzada Benza
   Universidad Nacional Agraria - La Molina - Peru..
 }
 \references{
-  Book spanish: Métodos estadísticos para la investigación.
+  Book spanish: Metodos estadisticos para la investigacion.
   Autor: Calzada Benza
   Universidad Nacional Agraria - La Molina - Peru.
 }

man/decimals.Rd

@@ -3,7 +3,7 @@
 %- decimals.
 \title{ Finding the largest decimal number from the elements of a vector or matrix  }
 \description{
-  In many situations it is required to have a larger decimal number than a set 
+  In many situations it is required to have a larger decimal number than a set
   of values expressed in climbing, vector or matrix.
 }
 \usage{

man/homog1.Rd

@@ -1,9 +1,9 @@
 \name{homog1}
 \alias{homog1}
 \docType{data}
-\title{ Data of fríjol }
+\title{ Data of frijol }
 \description{
-  Data of fríjol under 4 technologies for the homogeneity of regression study.
+  Data of frijol under 4 technologies for the homogeneity of regression study.
   Yield of Frijol in kg/ha in clean and dry grain.
 
   Tecnnologies:
@@ -23,7 +23,7 @@
 }
 
 \references{
-Oriente antioqueño (1972) (ICA.- Orlando Martinez W.)
+Oriente antioqueno (1972) (ICA.- Orlando Martinez W.)
 Colombia.
 }
 \examples{

man/join.freq.Rd

@@ -43,7 +43,7 @@ table.freq(h1)
 # Join classes 9,10,11 and 12 with little frequency.
 inter<-join.freq(intervals,9:12) # with c(9,10,11,12) or 9:12
 # new table
-h2<-hist(size,breaks=inter,freq=TRUE,xlim=c(0,0.12),col="bisque")
+h2<-hist(size,breaks=inter,xlim=c(0,0.12),col="bisque")
 table.freq(h2)
 
 }